In a recent post I explored conditions that tend to make designing a project to retire technical debt a wicked problem. And in another post I noted some conditions that tend to make designing a project to retire technical debt a super wicked problem. But not all technical debt retirement project design efforts are wicked problems, and “wickedness” can occur in degrees. Designing these projects can be a tame problem, especially if we target the technical debt for retirement early in its existence. In this post I’ll explore degrees of wickedness in retiring technical debt, and propose a framework for dealing with technical debt retirement project design problems that are less-than-totally wicked.
The degree of wickedness of a problem
As a quick review, here are the attributes of a wicked problem as Rittel and Webber see them [Rittel 1973], rephrased for brevity:
There is no clear problem statement
There’s no way to tell when you’ve “solved” it
Solutions aren’t right/wrong, but good/bad
There’s no ultimate test of a solution
You can’t learn by trial-and-error
There’s no way to describe the set of possible solutions
Every problem is unique
Every problem can be seen as a symptom of another problem
How you explain the problem determines what solutions you investigate
The planner (or designer) is accountable for the consequences of trying a solution
Window blinds with some slats open and some closed. We can think of the slats as the wicked problem criteria of Rittel and Weber. A closed slat is a criterion satisfied by the problem; a partially open slat is a criterion that the problem satisfies to some extent. A wicked problem has all slats closed; a tame problem has at least one slat open or partially open. When many slats are mostly closed, the problem isn’t wicked, but it can be very difficult to resolve. That’s the way it is with most technical debt retirement project design problems. When even one slat is partially open, we can get a peek at the other side of the blinds, and use that information to pry open some of the other slats.
Rittel and Webber held that wicked problems possessed all of these characteristics, but Kreuter, et al., take a different view, which I find compelling [Kreuter 2004]. Their view is that wicked problems and tame problems lie at opposite ends of a spectrum. A problem that satisfies all ten of the criteria would lie at the wicked end of the spectrum; one that satisfies none would lie at the tame end.
A close examination of Rittel’s and Webber’s ten criteria reveals that they aren’t black-and-white; that we can regard each one as occurring in various degrees. For example, consider Criterion 1: “There is no clear problem statement,” which Rittel and Webber express as, “There is no definitive formulation of a wicked problem.” Burge and co-author McCall, who was a student of Rittel, offer this interpretation [Burge 2015]:
Here by the term formulation Rittel means the set of all the information need [sic] to understand and to solve the problem. By definitive he means exhaustive.
The original language of Rittel and Webber, with the interpretation of Burge and McCall, is indeed black-and-white. But one can easily imagine problems that satisfy this criterion to varying degrees. That is, one problem formulation might have almost everything one might need to understand and solve the problem, while another might have almost none of what one might need. In some of these cases, the problem solver might be able to make reasonable assumptions to fill in any gaps and then make some progress towards a solution. Or the formulation as given might be incomplete, but by working on a solution despite these lacunae, the missing information might reveal itself, or might arrive as a result of other research. For these reasons, I find it possible to regard the degree to which a problem satisfies Criterion 1 as residing on a continuum. And I expect one can make analogous arguments for all ten criteria.
This “continuum hypothesis” doesn’t conflict with the definition of a wicked problem. Wickedness still requires that all ten criteria be satisfied absolutely. But the position where a problem resides on the Tame/Wicked spectrum can be determined, conceptually, by the degree to which the problem fits the ten criteria of Rittel and Webber. In other words, as we address the problem of designing a technical debt retirement project, we can contemplate and consider the degree of wickedness of the problem; not merely that a problem is wicked or it isn’t.
The degree of a problem’s wickedness provides useful guidance. Specifically, if a problem clearly satisfies nine of the ten criteria, but not the tenth, according to Rittel and Webber, it would not be categorized as a wicked problem. Because it might be extraordinarily difficult to resolve, we would do well to treat it as wicked with respect to the nine criteria it satisfies. We would use that information to guide our decisions about where we apply resources, and what kind of resources we apply. The model of wicked problems provided by Rittel and Webber would be useful, even though the problem itself might not meet their definition in the strictest sense.
And so we’re led to the concept of the dimensionality of wickedness.
The dimensionality of wickedness
If we regard the ten criteria of Rittel and Webber as dimensions in a ten-dimensional space, then our “wickedness spectrum” becomes much richer. Maybe too rich, in the sense that its complexity presents difficulty when we try to think about it. But the concept of the dimensionality of wickedness can be useful, if we consider each dimension as having a degree of wickedness. This enables us to choose problem-solving techniques that work well for wicked problems that owe their wickedness to specific dimensions. That is the approach taken by Kreuter, et al. [Kreuter 2004]
This suggests a framework for designing (or re-designing) technical debt retirement projects:
Deal separately (and first) with any parts of the technical debt retirement project design problem that are tame
Use that information to determine which of the ten criteria of Rittel and Webber are most relevant to this particular technical debt retirement project design problem
Apply established approaches that account for the relevant criteria to formulate a project design
This program is too much for a single post. But I can make a start in my next post with descriptions of the indicators of wickedness, including an examination of the implications of each of these indicators relative to the presence of each of the ten criteria of Rittel and Webber. The next step will be to suggest techniques for technical debt retirement project design problems that meet, to some degree, the criteria of Rittel and Webber.
Buckle up.
References
[Burge 2015] Janet E. Burge and Raymond McCall. “Diagnosing Wicked Problems,” Design Computing and Cognition 14, 2015, 313-326.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
In my last post I provided a list of attributes of wicked problems [Rittel 1973], and listed the reasons why I feel that technical debt retirement projects can qualify as wicked problems. As a quick review, here are the attributes of a wicked problem as Rittel and Webber see them, rephrased for brevity:
There is no clear problem statement
There’s no way to tell when you’ve “solved” it
Solutions aren’t right/wrong, but good/bad
There’s no ultimate test of a solution
You can’t learn by trial-and-error
There’s no way to describe the set of possible solutions
Every problem is unique
Every problem can be seen as a symptom of another problem
How you explain the problem determines what solutions you investigate
The planner (or designer) is accountable for the consequences of trying a solution
A subset of wicked problems can be viewed as super wicked [Levin 2012]. Levin, et al. list the following four properties of super wicked problems. With each one, I’ve added my thoughts about how retiring technical debt can qualify as a super wicked problem.
Time is running out
Two pilots line up their F/A-22 Raptor aircraft behind a KC-10 Extender to refuel while en route to Hill Air Force Base, Utah. When Hurricane Michael made landfall just after noon local time on October 10, 2018, it swept almost directly over Tyndall Air Force Base. Tyndall is the base for the U.S. Air Force 325th Fighter Wing, which has primary responsibility for air dominance training for pilots of F-22s, and maintenance personnel and air battle managers. 55 F-22s are based at Tyndall. When Hurricane Michael was approaching, 33 of the F-22s were repositioned to Wright-Patterson Air Force Base near Dayton, Ohio [Phillips 2018a]. According to U.S. Senator Bill Nelson (D-Florida), the aircraft that were left behind were undergoing maintenance and were not in condition to be flown [Gabriel 2018]. Some of these aircraft are known to have been damaged in the storm. At this writing, the extent of any damage hasn’t been disclosed, but it’s believed that the aircraft are repairable. Given the reality of anthropogenic climate change [Cook 2016], further increases in storm intensity and frequency are likely, and air bases located in vulnerable coastal regions are at risk [Phillips 2018b]. Because these bases were constructed during the period before the general recognition of anthropogenic climate change, they now constitute a technical debt. Relocating them would undoubtedly be a wicked problem, and possibly a super wicked problem. However, if federal policies continue to fail to account for anthropogenic climate change, those policies could prevent relocating these bases. If that happens, the policies themselves represent a technical debt. Changing federal policy to account for anthropogenic climate change is certainly a super wicked problem. U.S. Air Force photo by TSgt Ben Bloker, courtesy Wikipedia
The problem presents difficulty that has an inherent timescale. For example, many believe that climate change will become irreversible if it isn’t dealt with successfully within 30 years.
Technical debt retirement can have an inherent time scale, though it varies with the debt in question. For example, Microsoft ended mainstream support for Windows 7 in January 2015. At that point, by any definition, every computer running Windows 7 incurred a technical debt. Yet by September 2018, almost four years later, 46.7% of all computers running Windows were running Windows 7, and that number represented an increase from the previous month by 0.6% [Keizer 2018]. The safe-operability window for Windows 7 is closing fast. Well, to be honest, no faster than the calendar. But the end of standard support is January 2020, which represents the inherent timescale for this kind of technical debt. The consequences are much more serious than just converting to Windows 10. All applications running on those machines are also potentially reaching end of life, unless they operate correctly on Windows 10. And all users of converted machines will need to learn how to use the new Windows 10 OS and any applications that must be updated or replaced. So there’s also a training issue, a learning curve, and period of elevated user error rates to deal with, too.
Other technical debts might also have explicitly inherent timescales, of course. But for enterprises whose securities are publicly traded, all technical debt has a shared inherent timescale associated with the cost of retiring it. For any technical debt that must eventually be retired, if the cost of retirement is high enough to be noticed as a decline in net income in enterprise financial reports, the impact of debt retirement can be severe and negative. Letting debt remain in place, unaddressed and growing, can be a financially dangerous strategy. A safer strategy is to spread the cost of retirement across as many fiscal quarters as possible. For Windows 10 conversions, there are only about five fiscal quarters remaining for that purpose. For other technical debts, the number of fiscal quarters available for diluting the costs of retirement might be more—or less.
Those who cause the problem also seek to provide a solution
The phrase “seek to provide a solution” might be somewhat tactful. I expect that some super wicked problems have the property that those who cause the problem exert some degree of control over what kinds of solutions are acceptable, or even discussible. In many cases, this represents a conflict of interest that can prevent the organization from deploying the more effective options.
That conflict of interest is certainly present in the context of technical debt retirement projects. Technical debt formation and persistence are due, in part, to a failure to commit resources to retiring it, or, at least, to inhibiting its formation. That failure is the responsibility of those in leadership roles in the enterprise. Typically, these are the same people who must decide to commit resources to retire technical debt in the future.
The central authority needed to address it is weak or non-existent
Again, I find this description unnecessarily limiting. I would prefer a phrasing such as, “The central authority, for whatever reason, chooses to exert, or is unable to exert, its authority in furtherance of solution, or even investigation.” In other words, the central authority need not be weak for it to be a source of difficulty in addressing the super wicked problem. It need only choose not to act. This can happen when those who cause the problem are the people who constitute the central authority, or they capture the central authority, or they capture the function to which the central authority has delegated responsibility for solution.
With respect to technical debt retirement, consider this scenario. At AMUFC, A Made-Up Fictitious Corporation, the sales and marketing functions have repeatedly struggled with the engineering function for shares of budget resources. Engineering has argued repeatedly, and unsuccessfully, that it needs additional resources to address the technical debt that has accumulated in several products. But the CEO is a former VP Sales, and a close friend of the CFO. Together, they have always decided to defer technical debt retirement in favor of new products and enhancements favored by the VP Marketing, by customers, and by investors.
Scenarios like this are common. Enterprise leadership is strong, but not inclined to address the technical debt retirement issue.
Partly as a result, policy responses discount the future irrationally
Irrational discounting of future costs and benefits occurs when policies are deployed that give too much emphasis to producing short-term benefits and/or to avoiding short-term costs or inconveniences. Benefits are pulled in from the future towards the present; costs and inconveniences are pushed out toward the future and deferred. One form of this discounting scheme—one of many—is hyperbolic discounting.
This tendency is one way of distracting attention from the actual problem. It is the principal tactic that enables the persistence of technical debt, and the means by which enterprises repeatedly defer attention to the problem of retiring technical debt.
Both the problem of managing technical debt and the problem of designing technical debt retirement projects, exhibit all of these properties to some degree. It’s likely, in my view, that these problems are super wicked problems.
Intervention strategies for super wicked problems
Levin, et al., recommend four distinct strategies for resolving super wicked problems. They are all approaches to devising policies that are difficult to alter, thus committing the organization to a particular path forward.
Exploit lock-in
Lock-in is usually regarded as dysfunctional adherence to a strategy or course of action despite the existence of superior alternatives [Brenner 2011]. It occurs when a policy confers some kind of immediate benefit on a subset of the population. If that benefit is significant, if the population subset would be harmed by alterations of the policy that remove the benefit, and if the subset has enough political power to defend the benefit, the policy will be “locked in” and thus difficult to change. Levin, et al., suggest that this phenomenon can serve a beneficial purpose by protecting a constructive policy, thus preventing its abandonment.
Most technical debt retirement efforts are explicitly designed to focus solely on retiring the debt, with all (or most) of the benefit appearing in the form of increased engineering productivity, decreased sources of frustration for engineers, or increased engineering agility. Benefits for non-engineering stakeholders tend to be indirect. To establish policies that exploit lock-in we must craft them so that they provide ongoing, direct benefit to the most politically powerful stakeholders. For example, addressing first the forms of technical debt that are most likely to lead to product innovations that non-engineering stakeholders would value highly could cause those stakeholders to favor further technical debt retirement efforts.
Positive feedback
This strategy makes policies durable when people or organizations already supporting the policy derive some kind of increased benefit, leading to others not yet supporting or covered by the policy to decide to support it. This mechanism is sometimes known as a “network effect.” When network effects are present, the value of a product or service increases as the size of the population using it increases [Shapiro 1998].
To exploit network effects when devising technical debt retirement efforts, focus on retiring the kinds of technical debts that confer benefits on stakeholders of platform assets. A platform asset is an asset that supports multiple other assets. Examples: an application development tool suite, a product line architecture, or an enterprise data network. Platform assets that support collaboration communities are more likely to generate network effects.
Increasing Returns
Policies and interventions that enable increasing returns to the population are more likely to be durable than those that offer steady returns. Because people adapt to steady levels of stimuli, policies that produce a change in the context only during the period immediately following initial adoption of those policies are less likely to maintain popular support than are policies that continue to provide increasing returns as long as they’re in place.
But among policies that provide increasing returns, Levin, et al., identify two types. Type I policies, which are less durable, confer their benefits on an existing population of supporters. Type I policies don’t cause others to become supporters. Type II policies also confer benefits on their supporters, but they do cause others to become supporters. Type II policies are thus far more durable than are Type I, because they foster growth in the supporting population.
Framing technical debt retirement projects as individual projects with the objective of retiring a specified kind of technical debt is likely to lead to the enterprise population viewing the effort as a Type I policy at best. But framing each project as a phase of a longer-term effort could position the larger effort as a Type II policy, if the larger effort is designed to affect increasing portions of the enterprise population.
Self-reinforcing
A self-reinforcing policy is a policy that creates a dynamic that causes that policy to be maintained. Reinforcement can come about either as a result of increases in the benefits the policy generates, or by causing increases in the cost of rescinding the policy, or a combination of both effects. As with the strategy of Increasing Returns, there are two types of self-reinforcing policies. Type I self-reinforcing policies focus on maintaining support for the policy within the subset of the population consisting of its original supporters. In analogy with Type II Increasing Returns policies, Type II Self-reinforcing policies affect both the original supporting population and portions of the population not yet affected directly by the policy.
To exploit self-reinforcement, technical debt retirement programs must emphasize retiring debts that have curtailed organizational agility in recognizable ways, or which have prevented introduction of capabilities that the population values. Communicating these objectives is an important part of the program, because self-reinforcing popular support is possible only if the population understands the strategy and how it benefits the enterprise.
Because of the essential uniqueness of any wicked problem (Proposition 7 of Rittel and Webber), it is futile to attempt to apply as a template any retirement program that worked for some other organization, or for some other portion of a given organization at an earlier time with a different form of technical debt. But these four strategies, implemented carefully and communicated widely and effectively within the organization, can build organizational commitment to a long-term technical debt retirement program, even though retiring technical debt may be a super wicked problem.
References
[Brenner 2011] Richard Brenner. “Indicators of Lock-In: I,” Point Lookout 11:12, March 23, 2011.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
The theory of wicked problems originated with Horst Rittel in the mid-1960s. He was trying to address “that class of problems which are ill-formulated, where the information is confusing, where there are many decision makers and clients with conflicting values, and where the ramifications in the whole system are confusing.” [Churchman 1967] The term wicked isn’t intended as a moral judgment. It suggests the mischievous streak in these problems, many of which have the property that proposed solutions often lead to conditions even more problematic than the situation they were intended to resolve. Is it just me, or are you also thinking, “Ah, technical debt”? In this post, I suggest that retiring technical debt can be a wicked problem. I’ll show how wickedness explains many of the difficulties we associate with retiring forms of technical debt that involve many stakeholders, assets, revenue streams, infrastructure, architectures, policies, or strategies.
Introduction
Prototypes of President Trump’s “border wall.” Building such a wall is undoubtedly an example of a wicked problem. Building prototypes in short segments of the wall to see what they would look like is a tame problem. But these are just prototypes of short segments of the wall. They aren’t prototypes of the project. They don’t demonstrate the process by which private property will be taken for the wall, or how construction access roads will be built, or how wildlife will be affected (and respond), or how the government of the adjacent nation (in this case, Mexico) will respond, or how the wall will be repaired when drug gangs destroy sections of it in isolated regions, or even if the wall would actually work as promised. Prototyping works well for tame problems. It helps us project how the finished project will perform, and how difficult it will be to complete the project. But for wicked problems, prototyping is of limited value and as Rittel observes, prototyping can make the problem worse. Photo by Mani Albrecht, U.S. Customs and Border Protection Office of Public Affairs, Visual Communications Division.
Horst Rittel was a design theorist at the University of California at Berkeley. His interest in wicked problems came about because of the need for designers to deal with the interactions between architecture and politics. In today’s technology-dependent enterprises, analogous problems arise when we try to retire technical debt, because multiple sets of quasi-independent stakeholders are affected by major technical debt retirement projects.
In the years since he first originated the wicked problem concept, people have extended it in ways that have led some to regard the concept as inflated and less than useful. But since extension and imitation rarely occur unless the extended concept has value, I take this phenomenon to mean that Rittel’s version of the concept is worthwhile. The focus of this post, then, is Rittel’s version of wicked problems, as applied to the problem of designing a complex technical debt retirement project.
The wicked problem concept has propagated mostly in the realm of public policy and social planning. Certainly wicked problems abound there: poverty, crime control, and climate change are examples. I know of no attempt to explore the wickedness of retiring technical debt in large enterprises, but have a look below and see what you think.
Rittel defines a problem as the discrepancy between the current state of affairs and the “state as it ought to be.” For the purposes of technical debt retirement planning, the state as it ought to be might at times be a bit ambitious. So I take the objective of a technical debt retirement project to be an attempt to resolve the discrepancy between the current state of affairs and some other state that’s more desirable. For the present purpose, then, the problem is designing a technical debt retirement project that converts the current state of affairs to a more desirable state that might still contain technical debt in some form, but it leaves us in a better position.
Actually, there is a subset of wicked problems—super-wicked problems—that I think might include some technical debt retirement problems. I address them in the post “Retiring technical debt can be a super wicked problem.”
For now, though, let’s examine the properties of wicked problems, and see how well they match up with the problem of designing technical debt retirement projects.
Attributes of wicked problems
Rittel’s summary of the attributes of wicked problems [Rittel 1973] convinced me that major technical debt retirement projects present wicked problems. Here are those attributes. In what follows, I use Rittel’s term tame problem to refer to a problem that isn’t wicked.
1. [No Definitive Formulation] Wicked problems have no definitive formulation
For any given tame problem, it’s possible to state it in such a way that it provides the problem-solver all information necessary to solve it. That’s what is meant by definitive formulation. For wicked problems, on the other hand, one’s understanding of the problem depends on the solution one is considering. Each candidate solution might—potentially—require its own understanding of the problem.
When designing a technical debt retirement project, we must fully grasp the impact of the effort on all activities in the enterprise. Each proposed project plan has its own schedule and risk profile, affecting enterprise activities in its own way. In principle, each candidate approach to the effort affects a different portfolio of enterprise assets in its own unique order. Because it isn’t practical to examine all possible candidate project plans, choosing a project plan by seeking an optimal set of effects isn’t practical. By the time you’re ready to execute the project plan you like best, the elapsed time required for such an analysis could render obsolete the data you collected first.
2. [No Stopping Rule] Wicked problems have no stopping rule
For any given tame problem, solutions have “stopping rules”—signatures that indicate clearly that they are indeed solutions. For example, in a chess problem to be solved in N moves, we know how to count to N and the position of checkmate is well defined.
Wicked problems have no stopping rule.
When planning a major technical debt retirement project, we must determine a task breakdown, a sequence for performing the tasks (which might be only partially ordered), a resource array including both human and non-human resources, a risk plan including risk mitigations and risk responses, a revenue stream interruption schedule, and so on. For each such plan, we can estimate the direct and indirect costs to the enterprise, and project the effects of the plan on market share for every affected product or service. Every plan has these attributes; when we compute them for a given candidate plan, the result doesn’t reveal that we’ve found “the solution.” We will have found only an estimate for that given solution. What we learn by doing this doesn’t reveal whether or not a “better” solution exists. There is no indicator contained in any given candidate solution that tells us we can “stop” solving the problem. Most often, we just stop when we run out of time for finding solutions. In some cases, we stop when we find just one solution.
3. [Solutions Are Good/Bad] Solutions to wicked problems are not true-or-false, but good-or-bad
The criteria for finding solutions to tame problems are unambiguous. For example, if a candidate function satisfies a differential equation, it’s a solution to the equation. The volume of concrete required to pave a section of roadway is a single number, determined by computing the area of roadway and multiplying by the thickness of the roadbed, and subtracting the volume of any reinforcing steel.
The solutions to wicked problems have no such clarity. When evaluating a candidate project plan for retiring a technical debt, we can estimate its cost, the time required, interruptions in revenue streams, and the timing of resource requirements. But determining how “good” that is might be difficult. Much depends on what other demands there might be for those resources or funds, and the political power of the people making those demands. No single number measures that.
4. [No Test of Solutions] There is no immediate and no ultimate test of a solution to a wicked problem
To test a candidate solution to a tame problem, the problem solving team determines whether the solution meets the requirements set in the tame problem statement. The consequences of implementing the solution are all evident to the problem solving team, and they’re well equipped to judge the success of the solution.
Not so with wicked problems. Any candidate solution to a wicked problem will generate waves of consequences. As these waves propagate, some of the problem’s stakeholders might find the solution unsatisfactory, and they’ll report this, possibly through politically powerful people or organizations, to the problem solving team. Because the consequences can be so diverse, the team can’t anticipate all of them, and in some cases, the team might have difficulty understanding how the troubles that plague some stakeholders were actually related to the implemented solution. Some undesirable consequences can be far more severe than any intended benefits. In other cases, the undesirable consequences might not be discovered until long after the solution has been deployed and accepted as operational.
When designing a technical debt retirement project, it’s necessary to determine everything that must be changed, what resources must be assembled to do the work, and what processes might be interrupted, when and for how long. Only rarely, if ever, can all of that be determined with certainty in advance. For that reason, determining that the design of the project is “correct” isn’t possible, except perhaps in the probabilistic sense. We never really know in advance that we’ve found a solution. Most of the time, after execution begins, we must make adjustments along the way, in real time.
5. [No Trial-and-error] Every solution to a wicked problem is a “one-shot operation”; because there is no opportunity to learn by trial-and-error, every attempt counts significantly
When solving tame problems, we can try candidate solutions without incurring significant penalties for the solution-finding effort. That is, trying a solution might require some effort (and therefore incur a cost), but it doesn’t otherwise affect the ability to find other solutions. Wicked problems are different. Every attempt to “try” a solution leaves traces that can potentially make further solution attempts more difficult, costly, or risky than they would have been if we hadn’t tried that solution. These traces of past solution attempts might also impose constraints on future solutions such that the wicked problem is effectively transformed into a new wicked problem. This property makes trial-and-error approaches undesirable and possibly infeasible. Indices of such undesirability are the half-lives of the traces of attempts to address the problem. A long half-life may mean that the problem solver has only one shot at addressing the problem.
When designing a technical debt retirement project, we sometimes try to “pilot” a potential approach to determine difficulty, costs, feasibility, political issues, or risk profiles. Even when we can revert the asset to its former state after a pilot is completed or suspended, the consequences for stakeholders and for stakeholder operations might not be reversible. When we next try another “pilot,” or perhaps a fully committed retirement project, these stakeholders might be significantly less willing to cooperate. Every attempted solution can leave political or financial traces like these, making future attempts riskier and more challenging.
6. [Solutions Are Not Describable] Wicked problems do not have an enumerable (or an exhaustively describable) set of potential solutions, nor is there a well-described set of permissible operations that may be incorporated into the plan
In devising solutions to tame problems, one common approach entails gathering the full set of possibilities, and screening them according to a set of favorability criteria. Reducing the field of possibilities is a useful strategy for finding optimal solutions—or even acceptable solutions—to tame problems.
Wicked problems defy such strategies. Gathering the full set of possible solutions to a given wicked problem can in itself be a wicked problem. The set of possible solutions cannot be parameterized; no finite set of attributes fully covers the solution space. We can never be certain that the set of candidates we have is a complete set.
Candidate designs for technical debt retirement projects present this same quality. We’re confronted with a dizzying array of choices, including the order in which we retire different kinds of technical debts, the order in which we address the debts borne by different assets, the possibilities of “refinancing” portions of the debt to intermediate forms of technical debt [Zablah 2015], what kinds of refactoring to perform and when, and much more. Because options like these are neither denumerable nor parameterizable, it’s difficult to know whether a given set of candidate project designs includes all possible project designs.
7. [Essential Uniqueness] Every wicked problem is essentially unique
Among tame problems, we can define classes or categories that have the property that all problems in a given class can be solved using the same method. For example, all the elements of the class of second order linear differential equations can all be solved using similar methods.
There are no classes of wicked problems that have such a property. There are no classes of wicked problems for which we can define a solution strategy that is effective for every member of the class. Even though we can define classes of wicked problems whose members are in some sense similar, that similarity doesn’t enable us to find a unified solution strategy that works for every member of the class.
So it is with designing technical debt retirement projects. Certainly, the collection of all technical debt retirement projects is a class; but the problem of designing a given retirement project is essentially unique. What “works” for one project in one enterprise in one fiscal year probably won’t work for another project in another enterprise in another fiscal year, or even another project in that same enterprise in that same fiscal year. Elements of the solution for one project might be useful for another project, but even then, we might need to adapt them to the conditions of that next project.
This essential uniqueness property of technical debt retirement projects collides with a common pattern decision-makers use when chartering major efforts. That pattern is reliance on consultants, employees, or contractors who “have demonstrated success and experience with this kind of work.” Because each technical debt retirement project is essentially unique, relying on a history of demonstrated success is a much less viable strategy than it would be with tame problems. Decision-makers would do well to keep this in mind when they seek approaches, leaders, and staff for major technical debt retirement efforts: no major technical debt retirement project is like any other.
8. [Problems as Symptoms] Every wicked problem can be considered to be a symptom of another problem
With tame problems or wicked, we typically begin the search for solutions by inquiring as to the cause of the current condition. When we find the cause or causes, and remove them, we usually find a new problem underlying them. Thus, for wicked problems, what we regarded initially as the problem is thereby converted into a symptom of newly recognized underlying problem. By repeating this process, we escalate the “level” of the problem we’re addressing. Higher-level problems do tend to be more difficult to resolve, but addressing symptoms, though easier, isn’t a path to ultimate resolution.
Rittel also observes that incremental approaches to resolving wicked problems can be self-defeating. The difficulty arises from the traces left behind by incrementalism, as described in the discussion of the unworkability of trial-and-error strategies. Rittel provides the example of the increase in difficulty of changing processes after we automate them.
To regard the wicked problem of designing a technical debt retirement project as a symptom of a higher-level wicked problem, we must be willing to regard as problems the very things that make the technical debt retirement project design effort a wicked problem. That is, the processes that lead to formation of technical debt, or that enhance its persistence, are themselves wicked problems. For example, one might inquire about how to change the enterprise culture so as to reduce the incidence of technical debt contagion. To undertake major technical debt retirement efforts without first determining what can be done to limit technical debt formation or persistence, due to contagion or due to other processes, might be unwise.
9. [No Controlled Experiments] The existence of a discrepancy representing a wicked problem can be explained in numerous ways. The choice of explanation determines the nature of the problem’s resolution
When addressing tame problems, problem-solving teams can often perform controlled experiments. The general framework of these experiments is as follows. They form a hypothesis H as to the cause of the problem, conjecturing a solution. Then assuming that H is correct, and given a set of conditions C, they deduce a set of consequences E that must follow if H is correct. If any elements of E don’t occur, then H is falsified. The process repeats until an H’ is found that cannot be falsified. H’ is then used to develop a solution. This is, essentially, the scientific method.
With wicked problems, the method fails in numerous ways. Foremost among these failure modes is the absence of the ability to control C. That is, interventions that might be required to set C to be a desired C0 tend to be impossible. Moreover, even if one can establish C0, the experiments one performs to determine whether E is observed tend to leave the kinds of traces discussed in Proposition 5 [No Trial-and-error]. Finally, the nature of the elements of E is usually such that determining their presence or absence is largely subjective.
When planning enterprise-scale technical debt retirement projects, as with many projects of similar scale, we believe that we can benefit from running a pilot of our proposed plan, to determine its fitness. However, because we cannot control the conditions in which we execute the pilot, we cannot be confident that our interpretation of the results of the pilot will apply to the actual project. Moreover, a small-scale pilot cannot generate some of the effects we most want to observe because they occur only at full scale. These effects include staff shortages, resource contention, and revenue interruption incidents.
10. [100% Accountability] The planner (or designer) has no right to be wrong
In solving tame problems, solvers are free to experiment with proposed solutions. They make conjectures about what might work, and gather the results of trials to determine how to improve their conjectured solutions. There is no social or legal penalty for failed conjectures.
In solving wicked problems, experiments don’t exist. Any trial solution is a real solution, with real effects on stakeholders and later, on the problem solvers themselves. Problem solvers are held accountable for the undesirable consequences of each solution, whether it’s a trial or not.
In planning a technical debt retirement project, any attempt to gather data about how the approach would affect the enterprise could potentially have real, lasting, deleterious effects. The costs associated with these consequences are charged to the project, if not officially and financially, then politically. The politics of failure can lead to serious consequences for the problem solvers. Any approach that the team deploys, on any scale no matter how small, can potentially create financial problems for the enterprise, and political problems for anyone associated with the technical debt retirement project.
Last words
The fit between wicked problems and technical debt retirement project design looks pretty good to me. But the research on a subset of wicked problems—super wicked problems—is also intriguing. I’ll look at that in my next post. After that, we’ll be ready to examine which approaches to retiring technical debt take these matters into account.
References
[Brenner 2011] Richard Brenner. “Indicators of Lock-In: I,” Point Lookout 11:12, March 23, 2011.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
In deciding whether or not to undertake technical debt retirement projects, organizations are at risk of making inappropriate decisions because of a synergy between the reification error and confirmation bias. Together, these two errors of thought create conditions that make committing appropriate levels of resources difficult. And in those cases in which resources are committed, there is a tendency to underestimate costs, which can lead to an elevated incidence of failures of technical debt retirement projects.
The reification error and confirmation bias
As explained elsewhere in this blog, the reification error is an error of reasoning in which we treat an abstraction such as technical debt as if it were a real, concrete, physical thing, which, of course, it is not. (See “Metrics for technical debt management: the basics”)
The reification error might be responsible, in part, for a widely used management practice that often appears in the exploratory stages of undertaking projects. Let’s start with an illustration from the physical world.
A feedback loop that now provides budgetary control in most organizations.
In the physical world, when we want cherries, we go to the produce section of the market and check the price per pound or kilo. Then we decide how many pounds or kilos we want. If the price is high, we might decide that fewer cherries will suffice. If the price is low, we might purchase more cherries. We have in mind a total cost target, and we adjust the weight of the cherries to meet the target, given the price. In the physical world, we can often adjust what we purchase to match our ability to pay for it.
Retiring technical debt doesn’t work like that, in part, because technical debt is an abstraction. But we try anyway; here’s how it goes. Management decides to retire a particular class of technical debt, and asks an engineer to work up an estimate of the cost. Sometimes Management reveals the target they have in mind if they have one; sometimes not. The estimate comes back as Total ± Uncertainty. Management decides that’s too high—or the Uncertainty is too great—and asks the engineer to find a way to do it for less, with less Uncertainty, maybe by being clever or doing less.
Management—the “customer” in this scenario—makes this request, in part, based on the belief that it’s possible to adjust the work to meet a (possibly unstated) target, in analogy to buying cherries in the produce department. That thinking is an example of the reification error. In this dynamic, we rarely take into account the fact that retiring technical debt isn’t exactly like buying cherries.
How confirmation bias affects engineering estimates
Now back to the interaction between Management and estimator. The engineer now suspects that Management does have a target in mind. Some engineers might ask what the target is. Some don’t. In any case, the engineer comes back with a lower estimate, which might still be too high. This process repeats until either Management decides against retiring the debt, or accepts the lowest Total ± Uncertainty, hoping for a final cost that isn’t too high.
In adjusting their estimates, engineers have a conflict of interest that can compromise their objectivity through the action of confirmation bias. In the case of technical debt retirement efforts, engineers are usually highly motivated to gain Management approval of the project, because the technical debt in question depresses engineering productivity and induces frustration. And since engineers typically sense that Management approval of the project is contingent on finding an estimate that’s low enough, the engineers have a preconception. That is, they have an incentive to convince themselves that the budget and schedule adjustments they make are reasonable. Because of the confirmation bias, they tend to seek justifications for the belief that lowering costs and tightening schedules is reasonable, while they tend to avoid seeking justifications for believing that their adjustments might not be feasible. That’s the confirmation bias in action.
How synergy between the reification error and confirmation bias comes about
So, because of the reification error, Management tends to believe that the work needed to retire technical debt of a particular kind is more adjustable than it actually is. And because of confirmation bias, engineers tend to believe that they can do the work for a cost and within a schedule that Management is willing to permit. Too often, the synergy between the two errors of thinking provides a foundation for disaster.
Why this synergy creates conditions for disaster in technical debt retirement projects
Management usually interprets estimates as commitments. Engineers do not equate estimates with commitments. Management usually forgets or ignores the upside Uncertainty. So typically, when Management finally accepts an estimate, the engineering team finds that it has made a commitment to deliver the work for the cost Total, with zero upside Uncertainty. Few engineering teams are actually asked to make an explicit commitment to perform the work for a cost Total with zero upside Uncertainty. An analogous problem occurs with schedule.
By ignoring the Uncertainty, Management (the buyer) transfers the uncertainty risk to the project team. That strategy might work to some extent with conventional development or maintenance projects, where we can adjust scope and risk before the work begins. But for technical debt retirement projects, this practice creates problems for two reasons.
Adjusting the scope of debt retirement projects is difficult
First, with technical debt retirement we’re less able to adjust scope. To retire a class of technical debt, we must retire it in toto. If we retire only some portion of a class of technical debt, we would leave the asset in a mixed state that can actually increase MICs. So it’s usually best to retire the entirety of any class of technical debt, so as to leave the asset in a uniform state.
Debt retirement efforts are notoriously unpredictable
Second, the work involved in retiring a particular class of technical debt is more difficult to predict than is the work involved in more conventional projects. (See “Useful projections of MPrin might not be attainable”) Often, we must work with older assets, or older portions of younger assets. The people who built them aren’t always available, and documentation can be sparse or unreliable. Moreover, it’s notoriously difficult to predict with accuracy when affected assets must be temporarily withdrawn from production—and for how long—to support the technical debt retirement effort. Revenue stream interruptions, which can be significant portions of total costs, can be difficult to schedule or predict. Thus, technical debt retirement projects tend to be riskier than other kinds of projects. They have wider uncertainty bands. Ignoring the Uncertainty, or trying to transfer responsibility for it to the project team, is foolhardy.
A strategy for reducing the effects of this synergy
To intervene in the dynamic between the consequences of the reification error and the consequences of confirmation bias, we must find a way to limit how their consequences can interact. That will curtail the ability of one phenomenon to reinforce the other. This task is well suited for application of Donella Meadows’ concept of leverage points [Meadows 1999], which I discussed in an earlier post, “Leverage points for technical debt management.”
In that post, I summarized Meadows’ idea that to alter the behavior of a complex system, one can intervene at one or more of 12 categories of leverage points. These are elements in the system that govern the behavior of the people and institutions that comprise the system. In that post, I sketched the use of Leverage Point #9, Delays, to alter the levels of technical debt in an enterprise.
In this post, I’ll sketch the use of interventions at Leverage Point #8, which Meadows calls, “The strength of negative feedback loops, relative to the impacts they are trying to correct against.”
A feedback loop that now provides budgetary control in most organizations
One feedback loop at issue in this case, illustrated above, provides budgetary control. It influences managers who might otherwise overrun their budgets by triggering some sort of organizational intervention when they do overrun their budgets. And it leads to increases in the portfolios of managers who handle their budgets responsibly. Presumably, that’s why managers compel estimators to find approaches that cost less. The feedback loop to which managers are exposed causes them to establish another feedback loop involving the engineer/estimator, and later the engineering team, to hold down their estimates, and later their actual expenditures.
We can use a diagram of effects [Weinberg 1992] to illustrate the feedback mechanism commonly used to control the performance of managers who are responsible for portfolios of project budgets. In the diagram, the oval blobs represent quantities indicated by their respective captions. Each of these quantities is assumed to be measurable, though their precise values and the way we measure them are unimportant for our rather qualitative argument.
Notice that arrows connect the blobs. The arrows represent the effect of changes in the value represented by one blob on the value represented by another. The blob at the base of the arrow is the effector quantity. The blob at the point of the arrow is the affected quantity. Thus, the arrow running from the blob labeled “Actual Spend” to the blob labeled “Overspend” expresses the idea that a positive (or negative) change in the amount of actual spending on projects causes a positive (or negative) change in Overspend. When a change in the effector quantity causes a like-signed change in the affected quantity, we say that their relationship is covariant.
Because increases in Budget Authority tend to decrease Overspend, all other things being equal, the relationship between Budget Authority and Overspend is contravariant. We represent a contravariant relationship between the effector quantity and the affected quantity as an arrow with a filled circle on it.
Finally, notice that the arrow from Overspend (effector) to Promotion Probability (affected) has a filled Delta on it. This represents the idea that as Overspend increases, it negatively affects the probability that the manager will be promoted at some point in the future. The Delta indicates a delayed effect; that the Delta is filled indicates a contravariant relationship. (An unfilled Delta would indicate a delayed covariant effect.)
This diagram, which contains a loop connecting Budget Authority, Overspend, and Promotion Probability, has the potential to “run away.” That is, as we go around the loop, we find self-re-enforcement, because the loop has an even number of contravariant relationships. It works as follows:
As Overspend increases, after a delay, the Probability of Promotion decreases. This causes reductions in Budget Authority because, presumably, the organization has reduced faith in the manager’s performance. Reductions in Budget Authority make Overspend more likely, and round and round we go.
Similarly:
As Overspend decreases, after a delay, the Probability of Promotion increases. This causes increases in Budget Authority because, presumably, the organization has increased faith in the manager’s performance. Increases in Budget Authority make Overspend less likely, and round and round we go.
Fortunately, other effects usually intervene when these self-re-enforcing phenomena get too large, but that’s beyond the scope of this argument. For now, all we need observe is that managers who manage their budgets effectively tend to rise in the organization; those who don’t, don’t.
The result is that managers seek to limit spending so as to avoid overspending their budget authority. And that’s one reason why they push engineers to produce lower estimates for technical debt retirement projects.
How this feedback loop overlooks important drivers of technical debt formation
To break the connection between the managers’ reification error and the engineers’ confirmation bias, our intervention must cause the managers and the engineers to make calculations differently. We can accomplish this by requiring that they consider more than the mere cost of retiring the class of technical debt under consideration. They must estimate the consequences of not retiring that technical debt, and they must also estimate costs beyond the cost of retiring the debt. In what follows, I’ll use the shorthand TDBCR to mean the class of Technical Debt Being Considered for Retirement.
Specifically, the estimates that are now typically generated for such projects cover only the cost of performing the work required to retire the TDBCR. It’s then left to Management to decide whether, when, and to what extent to commit resources to execute the project. The primary consideration is the effect on the decision-maker’s budget, and the consequences for achieving the goals for which the decision-maker is responsible.
Since the retirement project can potentially provide benefits beyond the manager’s own portfolio, failing to undertake the project can have negative consequences for which the manager ought to be held accountable. That’s the heart of the problem. So let’s look at some examples of considerations that must be taken into account.
Adjustments that would be needed in these feedback loops to gain control of technical debt
In making a resource allocation decision for a technical debt retirement project, there are considerations beyond the cost of retiring the debt. A responsible decision regarding undertaking technical debt retirement projects is possible only if other kinds of estimates are also generated and available. Here are some examples:
The effects of retiring TDBCR on the cost of executing any other development or maintenance efforts contemplated or already underway
The effects of retiring TDBCR on revenue and market share for all existing assets that directly produce revenue and which could be affected by retiring TDBCR
The revenue that would be generated (and timing thereof) by any new products or services that would be enabled by retiring TDBCR
The effects of retiring TDBCR on the cost of executing other technical debt retirement efforts
And these items might not be related to anything for which the decision-maker is responsible. That’s the core of the problem we now face: the feedback loop we now use to influence the decision-maker excludes considerations that are affected by the decision-maker’s decisions. Until we install feedback loops that cause the decision-maker to consider these consequences, or until we make decisions at levels that include these other consequences, the effects of the decision-maker’s decisions are uncontrolled, and might not lead to decisions optimal for the enterprise.
References
[Brenner 2011] Richard Brenner. “Indicators of Lock-In: I,” Point Lookout 11:12, March 23, 2011.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
I’ve mentioned the reification error in a previous post (see “Metrics for technical debt management: the basics”), but I haven’t explored its dual, the resilience error. Let me correct that oversight now.
The future USS Zumwalt (DDG 1000) is underway for the first time conducting at-sea tests and trials in the Atlantic Ocean Dec. 7, 2015. The first of the Zumwalt class of US Navy guided missile destroyers, it is designed to be stealthy, and to be supported by a minimal crew. After the program experienced explosive cost growth, the class has been downsized from 32 ships to three, and complement increased from 95 to over 140 to reduce capital costs. The three vessels on order now have significantly reduced missions. As one might expect, the causes of these troubles are much debated. But it’s possible that the resilience error plays a role. Before the first of a new class of ships goes to sea, it exists as an abstraction—a collection of concepts, plans, promises, and technologies, tried and untried. Many elements of this collection have never inter-operated with other elements. The first ship represents the first opportunity to see how all the elements work together. Although troubles often appear even before the ship is fully assembled, anticipating all troubles is extraordinarily difficult.
Reification risk is the risk that an error of reasoning known as the reification error might affect decisions—in this case, decisions regarding technical debt. The reification error [Levy 2009] [Gould 1996] (also called the reification fallacy, concretism, or the fallacy of misplaced concreteness [Whitehead 1948]) is an error of reasoning in which we treat an abstraction as if it were a real, concrete, physical thing. Reification is useful in some applications, such as object-oriented programming and design.
But when we reify in the domain of logical reasoning, troubles can arise. For example, we can encounter trouble when we think of “measuring” technical debt. Strictly speaking, we cannot measure technical debt. It isn’t a real, physical thing that can be measured. What we can do is estimate the cost of retiring technical debt, but estimates are only approximations. And in the case of technical debt, the approximations are usually fairly rough—they have wide uncertainty bands. That’s one way for trouble to enter the scene. When we regard the estimate as if it were a measurement, we tend to think of it as more certain than it actually is. Technical debt retirement projects then overrun their budgets and schedules, and chaos reigns.
For example, if we think we’ve measured the MPrin of a class of technical debt, rather than that we’ve estimated it, we’re more likely to believe that one measurement will suffice, and that it will be valid for a long time (or indefinitely). On the other hand, if we think we’ve estimated the MPrin of a class of technical debt, we’re more likely to believe that obtaining a second independent estimate would be wise, and that the estimate we do have might not be valid for long. These are just some of the consequences of the reification error.
The resilience error
If the reification error is risky because it entails regarding an abstraction as a real, physical thing, we might postulate the existence of a resilience error that’s risky because it entails regarding an abstraction as more resilient, pliable, adaptable, or extensible than it actually is.
When we commit the resilience error with respect to an abstraction, we adopt the belief—usually without justification, and possibly outside our awareness—that if we make changes in the abstraction without fully investigating the consequences of those changes, we can be certain that the familiar properties of the abstraction we modified will apply, suitably modified, to the new form of the abstraction. Or we assume incorrectly that the abstraction will accommodate any changes we make to its environment.
Sometimes we benefit when we modify abstractions; usually we encounter unintended and unpleasant consequences. For example, unless we examine our modifications carefully, it’s possible that the implications of a modification might conflict with one or more of the fundamental assumptions of the abstraction.
Examples of the resilience error
Perhaps a (ahem) concrete example will illustrate. Consider the steel hull of an ocean liner. We can manufacture it more cheaply if we can devise a way to use less steel. So one approach to that goal is to remove a small portion of the bottom of the hull, say, a circular hole one meter in diameter. We send some people into the ship to do the work, and they return with panicky reports of water coming in. But the ship seems fine, so we reject the reports. Even a day later, all seems well. But by the end of the second day, the trouble is obvious. The ship is sinking.
The problem in our example is that the circular hole in the hull violated a fundamental assumption about how ship hulls work: they work by keeping all water out of the ship. We had extended the idea of hull to make it lighter, but in doing so, we encountered some unintended consequences because our extension violated a fundamental property of hulls.
Now for a less fanciful example.
Consider the fictitious company Alpha Properties LLC, which manages small condominium associations (from 25 to 100 units). Things have been going swimmingly at Alpha Properties, and they’ve decided to expand to handle large condominium associations. Their financial accounting software has worked well, and their employees have become quite expert in its use. Alpha management has heard good reports from other management companies that deal with large client associations. So Alpha decides to use the same software for its larger accounts too. But things don’t work out so well.
The software is fine, but the processes used by the staff are cumbersome and slow. For example, setting up a new association requires too much manual data entry. For a 100-unit association, client setup wasn’t a burden, but for a 900-unit association the problem is just unmanageable.
This is a fine example of the resilience error. When we make this error, we fail to appreciate how an abstraction can encapsulate assumptions that make for difficulties when we try to extend it or apply it in a new or altered context. In this example, Alpha’s data flow processes are the abstraction. The context is signing up a new client association. When the context (signing up a large new client) is different, it violates an internal assumption of the abstraction (the data flow process for signing up a new client).
How the resilience error leads to technical debt
In many cases, the resilience error is at the heart of the causes of technical debt. It works like this. We have an asset that works perfectly well for one set of applications or in one set of contexts. We want to apply that asset in a new way, which might (or might not) require some minor extensions. When we try it, we find that the asset incorporates some assumptions about the application or the context, and one or more of those assumptions are violated by the new application or the new context. Scrambling, we find some quick fixes that can get things working again, but those fixes usually aren’t well designed or easily maintained. The result is a trail of technical debt.
Acquiring companies is like that. Before the acquisition, we think we’ll be able to merge the IT operations to save some expenses in operations. When we actually try it, though, merging them proves to be far more expensive than we imagined. Ah, the resilience error.
What makes this situation so difficult is that often we’re unable to anticipate what assumptions we might be about to violate. That’s why we make the resilience error.
Spotting difficulties with adapting to new applications and new contexts isn’t so difficult with physical entities. For example, we can see in advance that a square peg won’t fit into a round hole. But with abstractions, we can’t always see the problems in advance. Piloting, prototypes, games, and simulations can help us avoid some trouble, but not all.
References
[Brenner 2011] Richard Brenner. “Indicators of Lock-In: I,” Point Lookout 11:12, March 23, 2011.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
Technical debt arises in enterprise assets through the effects of two classes of drivers: obsolescence and decision-making. When technologies advance, or new technologies arise, or laws or regulations evolve or are introduced, existing assets or assets under development can sometimes be left behind. That’s how obsolescence produces technical debt. Debt driven mainly by decision-making is more difficult to describe, but anything that biases decisions away from strictly rational results presents risk. Three cognitive biases likely have strong effects on technical debt formation and persistence.
Photo of Daniel Ellsberg, speaking at a press conference in New York City in 1972. Best known for his role in releasing the Pentagon Papers, Dr. Ellsberg made important contributions to decision theory while at the RAND Corporation. Photo by Bernard Gotfryd, courtesy U.S. Library of Congress.Decision-making produces technical debt as the people of the enterprise make choices in design, development, and resource acquisition or allocation. Typically, both obsolescence and decision-making contribute to producing any particular instance of technical debt, though either obsolescence or decision-making might be more important than the other in any given instance.
Managing debt driven principally by obsolescence isn’t difficult, but I’ll leave that topic for another time. For now, let’s focus on decision-making. Already widely accepted is the contribution of engineering decisions to technical debt formation. Indeed, many believe that all — or most — technical debt arises as a result of faulty decisions by engineers. While some engineering decisions are indeed faulty, the current scale of technical debt is so large as to create doubt about the idea that the only decisions contributing to technical debt formation are engineering decisions. Investigating how resource allocation decisions might contribute to technical debt formation is certainly worthwhile.
In this post, I propose three examples illustrating how resource allocation decisions might contribute to technical debt formation and persistence. Each example illustrates how people make faulty decisions while believing they’re proceeding objectively and rationally. In each case, what causes the problem is a phenomenon called cognitive bias, though each example in this post illustrates the action of a different cognitive bias.
Loss aversion
The cognitive bias known as loss aversion, first identified by Amos Tversky and Daniel Kahneman [Kahneman 1984], is the tendency to prefer options that avoid losses to options that lead to gains that are equivalent or even greater. A decision-maker affected by loss aversion bias might conclude that it’s better to not lose $5 than to find $5 or even $10. In this way, loss aversion skews decisions so as to favor options that enable the enterprise to protect or enhance existing revenue streams, even if that decision causes increases in operating expenses. And this bias has effect even if the increases in operating expenses exceed the value of whatever revenue that decision protected.
Retiring technical debt usually entails deferring revenue in the short term, for two classes of reasons. First, we must turn the attention of some part of the engineering organization to debt retirement, instead of whatever they were doing. Assuming that they would have been working on maintaining or enhancing existing products or services, this redirection of their attention can lead to reducing or deferring revenue. Second, during the debt retirement operation, some work might require short-term interruptions of revenue streams, for the purpose of installing or testing the assets that are being revised for debt retirement purposes.
Thus, debt retirement efforts often do reduce revenue — or reduce revenue increases — in the short term, and some decision-makers can perceive that effect as a loss.
However, the long-term effects of debt retirement can be gains, and those gains can be considerable. Typically, by retiring an asset’s technical debt, we reduce the difficulty (read: time required, effort, cost, and risk) of future maintenance and enhancement efforts involving the asset. We also reduce the probability of debt contagion.
Since these long-term effects of debt retirement are ongoing, their impact on the enterprise can be significant. But unless one is experienced with dealing with the consequences of technical debt, recognizing the value of retiring technical debt can be difficult. When loss aversion is in play, intuitive comparisons of the effects of (a) a short-term revenue loss or delay to (b) a long-term benefit of debt retirement are biased in favor of not retiring technical debt.
Insulating decisions about debt retirement from the effects of loss aversion bias requires objective mathematical modeling of revenue losses and operating cost benefits for all options under consideration. Those models must also account for uncertainty, which makes them inherently ambiguous. And that leads us to consider our next cognitive bias, the ambiguity effect.
The ambiguity effect
The cognitive bias known as the ambiguity effect causes us to prefer options for which the probability of a desirable outcome is relatively better known, over options for which the probability of a desirable outcome is less well known, even if the expected value of that more ambiguous outcome exceeds the expected value of the less ambiguous outcome. The effect was first described by Daniel Ellsberg [Ellsberg 1961].
Consider a choice between allocating resources to a new development project and allocating resources to a technical debt retirement project. In most enterprises, decision-makers are familiar with new development projects. Likewise, project champions, project sponsors, and project managers are also familiar with new development projects. All parties are less familiar with debt retirement. It’s reasonable to suppose that when confronted with such a choice, decision-makers are likely to see debt retirement as carrying with it a probability of positive outcome that is less well known than the probability of a positive outcome for the new development project.
Because of the ambiguity effect, resource allocation decisions are likely to be biased against technical debt retirement, and in favor of maintenance or new development.
But there’s more. Most projects, of any kind, encounter trouble from time to time. When that happens, the urge to reallocate organizational resources can be powerful. Troubled projects might receive more resources if they’re viewed as important to the organization. If so, those resources often come from other projects. The ambiguity effect biases these resource reallocation decisions in a way analogous to initial resource allocation decisions, as described above. In other words, because of the ambiguity effect, when projects encounter trouble, debt retirement projects are less likely to be able to retain previously allocated resources than are maintenance or new development projects.
The availability heuristic
The availability heuristic is a method humans use to evaluate the validity or effectiveness of decisions, concepts, methods, or propositions [Tversky 1973]. According to the heuristic, if we recognize the item being evaluated as familiar, or related to something with which we are familiar, we’re more likely to regard it as valid or workable. And when making comparisons between two alternative decisions, concepts, methods, or propositions, we’re likely to assess more favorably the decision, concept, method, or proposition with which we’re more familiar, all other things being equal.
In organizations where decision-makers have more experience evaluating maintenance or development project proposals than they have with technical debt retirement proposals, the availability heuristic acts to reduce the relative assessed favorability of technical debt retirement proposals. It does this in three ways.
First, in most organizations, technical debt retirement projects are less familiar to decision-makers than are maintenance or development projects. On that ground alone the technical debt retirement project proposals are at a disadvantage.
But the second effect of the availability heuristic is more important, because the effect of the availability heuristic extends to the consequences of the decision, concept, method, or proposition under consideration. To grasp the value of a maintenance or development project, one must understand how it will affect the users of the assets being developed or maintained. Likewise, to grasp the value of a technical debt retirement project, one must understand how the presence of the technical debt hampers the enterprise in its attempts to achieve its objectives. On must also understand how retiring the technical debt might confer advantages in terms of future engineering efforts. Usually, understanding the consequences of maintenance or development projects is more “available” to decision-makers than is understanding the consequences of technical debt retirement projects. Even more dramatic is the difference between understanding the consequences of not funding a maintenance or development project and the consequences of not funding a technical debt retirement project.
Finally, much of the benefit of a technical debt retirement project is indirect. That is, although there is some direct benefit in terms of the assets from which the debt has been retired, the most dramatic benefits are manifested in projects that follow the debt retirement project, and which depend on the assets that have been relieved of debt. Sometimes, those follow-on projects are known at the time decision-makers are considering funding the debt retirement project. Sometimes those follow-on projects have yet to be specified or even recognized. In either case, they are less “available” to decision-makers because those follow-on projects are indirect beneficiaries.
These three effects of the availability heuristic cause decisions about resource allocations to tend to favor maintenance or development projects over debt retirement projects.
Mitigating the risks of these three cognitive biases
Over time, as everyone becomes more familiar with technical debt retirement projects, these effects may wane somewhat. But waiting for that to happen isn’t exactly what one might call risk mitigation. For one thing, familiarity grows only if one is motivated and pays attention. As busy as are decision-makers in modern organizations, depending on them to actively enhance their own familiarity with technical debt retirement projects is probably not the safest course.
An effective program of actively mitigating the risks of these three cognitive biases probably should focus on four areas.
Familiarity
Do what you can to increase decision-maker familiarity with the concept of technical debt, and with the consequences of carrying existing technical debt. Conventional presentation-based training will help, but interactive, experiential training is far more effective. Participants must actually experience the consequences of technical debt in a well-designed and professionally facilitated simulation of a problem-solving task. A faithful simulation should include estimation, changing and ambiguous requirements, and team composition volatility.
Retrospectives
Retrospectives (also known as after-action reviews, post mortems, debriefings, or lessons-learned sessions) are meetings convened to review processes that just completed pieces of work [Kerth 2001]. Typically, attendance is restricted to the project team members. To maintain psychological safety and to encourage truth telling, attendance by enterprise decision-makers is not recommended, unless the organizational culture includes appropriate safeguards. In any case, a section of the retrospective dedicated to investigating the causes and consequences of technical debt in the context of the current project can ensure capture of relevant knowledge and experience.
Mathematical modeling practice
Mathematical modeling is one path to creating a more objective foundation for decisions. It’s essential for improving estimation quality. Also helpful are high quality effort data and metrics data related to the formation and lifetime of technical debt. Reviews of estimates and projections during retrospectives can help improve their quality over time.
Metrics development
Determining the effects of risk mitigation failure provides important guidance for corrective action in risk mitigation. Developing metrics that reveal these failures is therefore essential to managing cognitive bias risk. I’ll be suggesting some valuable metrics in a future post.
Last words
These three cognitive biases are by no means the only cognitive biases that can affect the formation or persistence of technical debt. Of the more than 200 identified cognitive biases, those most likely to be relevant are those that affect decision-making. Watch this space for links to posts about additional cognitive biases and their affects on technical debt formation or persistence.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Tversky 1973] Amos Tversky and Daniel Kahneman. "Availability: A heuristic for judging frequency and probability." Cognitive Psychology 5:2, 207-232, 1973.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
With all the talk of technical debt these days, it’s a bit puzzling why there’s so little talk in the financial community about how to go about accounting for technical debt [Conroy 2012]. Perhaps one reason for this is the social gulf that exists between the financial community and the community most keenly aware of the effects of technical debt — the technologists. But another possibility is the variety of mechanisms compelling technologists to leave technical debt in place and move on to other tasks.
Accounting for technical debt isn’t the same as measuring it. We usually regard our accounting system as a way of measuring and tracking the financial attributes of the enterprise. As such, we think of those financial attributes as representations of money. Technical debt is different. It isn’t real, and it isn’t a representation of money — it’s a representation of resources. Money is just one of those resources. Money is required to retire technical debt, and money is consumed when we carry technical debt, but other kinds of resources are also required. Sometimes we forget that when we account for technical debt.
Here’s an example. One common form of technical debt is the kind first described by Cunningham [Cunningham 1992]. Essentially, when we complete a project, we often find that we’ve advanced our understanding of what was actually needed to accomplish our goals. And we’ve advanced our understanding to such an extent that we recognize that we should have taken a different approach. Fowler described this kind of technical debt as, “Now we know how we should have done it.” [Fowler 2009] At this point, typically, we disband the team and move on to other things, leaving the technical debt outstanding, and often, undocumented and soon to be forgotten.
A (potentially) lower-cost approach involves immediate retirement of the debt and a re-release of the asset — an “echo release” — in which the asset no longer carries the technical debt we just incurred and immediately retired. But because echo releases usually offer no immediate, evident advantage to the people and assets that interact with the asset in question, decision-makers have difficulty allocating resources to echo releases.
This problem is actually due, in part, to the effects of a shortcoming in management accounting systems. Most enterprise management accounting systems track effectively the immediate costs associated with technical debt retirement projects. They do a much less effective job of representing the effects of failing to execute echo releases, or failing to execute debt retirement projects in general. The probable cause of this deficiency is the distributed nature of the MICs — the metaphorical interest charges associated with carrying a particular technical debt. The MICs appear in multiple forms: lower productivity, increased time-to-market, loss of market share, elevated voluntary turnover in the ranks of technologists, and more (see “MICs on technical debt can be difficult to measure”). These phenomena are poorly represented in enterprise accounting systems.
Decision-makers then adopt the same bias that afflicts the accounting system. In their deliberations regarding resource allocation, they emphasize only the cost of debt retirement, often omitting from consideration altogether any mention of the cost of not retiring the debt, which can be ongoing.
If we do make long-term or intermediate-term projections of costs related to carrying technical debt or costs related to debt retirement releases, we do so in the context of a cost/benefit computation as part of the proposal to retire the debt. Methods vary from proposal to proposal. Many organizations lack a standard method for making these projections. And because there’s no standard method for estimating costs, comparing the benefits of different debt retirement proposals is difficult. This ambiguity and variability further encourages decision-makers to base their decisions solely on current costs, omitting consideration of projected future benefits.
Dealing with accounting for technical debt
Relative to technical debt, the accounting practice perhaps most notable for its absence is accounting for outstanding technical debts as liabilities. We do recognize outstanding financial debt as such, but few balance sheets — even those for internal use only — mention outstanding technical debt. Ignorance of the liabilities imposed by outstanding technical debt can cause decision-makers to believe that the enterprise has capacity and resources that it doesn’t actually have. Many of the problems associated with high levels of technical debt would be alleviated more readily if we began to track our technical debts as liabilities — even if we did so for internal purposes only.
But other shortcomings in accounting practices can create additional problems almost as severe.
Addressing the technical-debt-related shortcomings of accounting systems requires adopting enterprise-wide patterns for proposals, which gives decision-makers meaningful comparisons between different technical debt retirement options and between technical debt retirement options and development or maintenance options. One area merits focused and immediate attention: estimating MPrin and estimating MICs.
Standards for estimating MPrin are essential for estimating the cost of retiring technical debt. Likewise, standards for estimating MICs, at least in the short term, are essential for estimating the cost of not retiring technical debt. Because both MPrin and MICs can include contributions from almost any enterprise component, merely determining where to look for contributions to MPrin or MICs can be a complex task. So developing a checklist of potential contributions can help proposal writers develop a more complete and consistent picture of the MICs or MPrin associated with a technical debt. Below are three suggestions of broad areas worthy of close examination.
Revenue stream disruption
Technical debt can disrupt revenue streams either in the course of being retired, or when defects in production systems need attention. When those systems are taken out of production for repairs or testing, revenue capture might undergo short disruptions. Burdens of technical debt can extend those disruptions, or increase their frequency.
For example, a technical debt consisting of the absence of an automated test can lengthen a disruption while the system undergoes manual tests. A technical debt consisting of a misalignment between a testing environment and the production environment can allow defects in a repair or enhancement to slip through, creating new disruptions as those new defects get attended to. Even a short disruption of a high-volume revenue stream can be expensive.
Some associations between classes of technical debt and certain revenue streams can be discovered and defined in advance of any debt retirement effort. This knowledge is helpful in estimating the contributions to MICs or MPrin from revenue stream disruption.
Extended time-to-market
Although technologists are keenly aware of productivity effects of technical debt, these effects can be small compared to the costs of extended time-to-market. In the presence of outstanding technical debt, time-to-market expands not only as a result of productivity reduction, but also from resource shortages and resource contention. Extended time-to-market can lead to delays in realizing revenue potential, and persistent and irreparable reductions in market share. To facilitate comparisons between different technical debt retirement proposals, estimates of these effects should follow standard patterns.
Data flow disruption
All data flow disruptions are not created equal. Some data flow processes can detect their own disruptions and backfill when necessary. For these flows, the main consequence that might contribute to MICs or MPrin is delay. And the most expensive of these are delays in receipt of orders, delays in processing orders, and delays in responding to anomalous conditions. Data flows that cannot detect disruptions are usually less critical, but they nevertheless have costs too. All of these consequences can be modeled and estimated, and we can develop standard packages for doing so that we can apply repeatedly to MICs or MPrin estimates for different kinds of technical debt.
Last words
Estimates of MICs or MPrin are helpful in estimating the costs of retiring technical debt. They’re also helpful in estimating the costs of not retiring technical debt. In either case, they’re only estimates, and as such, they have error bars and confidence limits. The accounting systems we now use have no error bars. That, too, is a shortcoming that must be addressed.
References
[Brenner 2011] Richard Brenner. “Indicators of Lock-In: I,” Point Lookout 11:12, March 23, 2011.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Tversky 1973] Amos Tversky and Daniel Kahneman. "Availability: A heuristic for judging frequency and probability." Cognitive Psychology 5:2, 207-232, 1973.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
Whether it is wise to use metrics for technical debt management is an open question. But whether it will become a widespread practice does seem to be a settled question. Using metrics for technical debt management now does appear to be inevitable. So let’s explore just what we mean by “metrics,” and what traps might lie ahead when we use them for technical debt management.
Measuring tools needed to follow a recipe in the kitchen. The word “measurement” evokes images that relate to our earliest understanding of the word as children. For most of us, that involves determining the attributes of a physical thing. In most cases, the physical thing of most concern is our own body — its height and weight. But we also measure everyday things, as when following recipes. So the strongest associations with the word “measurement” involve physical things. “Measuring” attributes of an abstract construct like technical debt can be perilous, unless we make allowances for its lack of physicality.
Skepticism about the effectiveness of using metrics for technical debt management is reasonable, because technical debt isn’t a physically measurable thing. “Measuring” technical debt is therefor susceptible to what psychologists call the reification error [Levy 2009] or what philosophers call the Fallacy of Misplaced Concreteness [Whitehead 1948].
The logical fallacy of reification occurs when we treat an abstract construct as if it were a concrete thing. Although reification can provide helpful mental shorthand, it can produce costly cognitive errors. For example, advising someone who’s depressed to get more self-esteem is unlikely to work, because self-esteem isn’t something one can order from Amazon, or anywhere else. One can enhance self-esteem through counseling, reflection, or many other means, but it isn’t a concrete object one can “get.” Self-esteem is an abstract construct.
Technical debt is likewise an abstract construct. We can discuss “measuring” it, but attempts to specify measurement procedures will eventually confront the inherently abstract nature of technical debt, leading to debates about both definitions and the measurement process.
Metrics inherently require some kind of measurement. That’s why skepticism about using metrics for technical debt management is a reasonable position. Reasonable or not, though, metrics will be used. We’d best be prepared to use them responsibly. That’s the focus of this post — and a few to come. If we use metrics for technical debt management, how can we do it responsibly? How can we manage the risks of reification?
This post is about metrics in general. In coming posts I’ll apply this line of thinking to specific examples of metrics for managing technical debt, and suggest approaches that could mitigate reification risk.
Foundations for behavior guidance decisions
The objective of technical debt management is support for behavior guidance decisions — decisions that guide the behavior and choices of employees so as to control the volume of technical debt. Although many frameworks exist for supporting behavior guidance decisions, they generally consist of four elements:
Quantifiers
A quantifier is a specification for a measurement process designed to yield a numeric representation of some attribute of an asset or process. With respect to technical debt, we use quantifiers to prescribe how we produce data that represents the state of technical debt of an asset. We also use quantifiers to generate data that captures other related items, such as budgets, the cost or availability of human effort, revenue flows — almost anything that interacts with the assets whose debt burden we want to control.
An example of a quantifier is the process for estimating the MPrin of a particular kind of technical debt borne by an asset. The MPrin quantifier definition includes an explicit procedure for measuring it — that is, for estimating the size of the MPrin in advance of actually retiring that debt. After retirement, we know its value without estimating, because the MPrin is what we spent to complete the retirement.
Measures
A measure is the result of determining the value of a quantifier. For example, we might use a quantifier’s definition to determine how much human effort has been expended on an asset in the past fiscal quarter. Or we might use another quantifier’s definition to determine the current size of the MPrin carried by the asset.
Metrics
A metric is an arithmetic formula expressed in terms of constants and a set of measures. One of the simpler metrics consists of a single ratio of two measures. For example, the metric that captures the average cost of acquiring a new customer in the previous fiscal quarter is the ratio of two measures, namely, the investment made in acquiring new customers, and the number of new customers acquired.
Associated with some metrics is a defined set of actors (actual people) who are authorized to take steps — or who are authorized to direct others to take steps — designed to affect the value of the metric in some desirable way. Metrics that have defined sets of actors are usually Key Performance Indicators (see below). If more than one individual is a designated actor for a metric, a process is defined to resolve differences among the designated actors about what action to take, if any. In some cases, this process is as simple as determining which designated actor has the highest organizational rank.
An example of a technical debt metric is the ratio MPrin(i)/MPrin(r) = the total of incremental technical debt (MPrin(i)) incurred in the given time period divided by the total of MPrin retired (MPrin(r)) in that same time period. In periods during which this ratio exceeds 1.0, the organization is accumulating incremental technical debt faster than it is retiring technical debt. Computing it as a ratio, as opposed to a difference, has the effect of expressing the increases (or decreases) in technical debt portfolio size in units of the size of the debt retired. This enables the organization to take on some incremental technical debt responsibly.
This metric also has the virtue of displaying meaningful trends in an easily recognized way. In this case, a steady upward trend means a steadily increasing debt portfolio, even if in some time periods the debt doesn’t increase much. In other words, the ratio removes some of the “choppiness” that might plague a metric expressed in terms of absolute values.
Key Performance Indicators
A Key Performance Indicator (KPI) is a metric that provides meaningful insight that’s used to guide business decisions. All KPIs are metrics; not all metrics are KPIs.
A KPI is derived from one or more metrics. It represents how successful the business is in accomplishing a given business objective. A metric, on the other hand, represents only the degree of success in reaching a targeted value for that metric. Because the relationship between the target value of a metric and any given business objective can be complicated, and potentially involve other metrics, metrics that aren’t KPIs are of less value in indicating the degree of success in achieving business objectives.
MPrin(i)/MPrin(r) is a metric that could also serve as a KPI, if the business objective is to achieve steady declines in overall technical debt.
Dimensions of measure vs. dimensions of metrics
Some metrics, such as MPrin(i)/MPrin(r), are dimensionless. Their values are pure numbers. And some metrics have dimensions — units of measure. For example, consider the metric MPrin(i)*Tdelay, where MPrin(i) is the volume of incremental technical debt borne by the deliverable, and Tdelay is the number of days after the target date that the project was delivered. The dimension of this metric is Currency*Days. This metric is particularly interesting, because a common assertion about technical debt is that we incur it as a means of advancing project delivery. Because the evidence for this assertion is mostly anecdotal, actually determining the value of this metric over a number of projects might reveal useful information about the effectiveness of the strategy of incurring technical debt as a means of advancing project delivery. Thus, we would expect to see small time delays associated with increased incremental technical debt. In other words, all projects of similar scale should lie along the same hyperbola in a plot of this metric in a space in which one axis is debt and the other is the number of days after the target date that the project was delivered.
Units of measures are often different from the units of the metrics that those measures support. For example, measures of technical debt in code include test coverage, documentation asynchrony, documentation omissions, code duplication, code complexity, dependency cycles, rule violations, or interface violations. The units of these measures are all different.
To those who must make strategic technical debt management decisions by comparing the costs of retiring different kinds of debt, these detailed measures are awkward to use. MPrin is more directly related to the issues they must address. MPrin provides a unit of comparison among debt retirement options, and between retirement options and available resources. Beyond the level of the particular debt being considered for retirement, MPrin is the dimension of greatest utility. [Brown 2010]
In a future post I’ll describe the properties of metrics that are needed for technical debt management.
References
[Brenner 2011] Richard Brenner. “Indicators of Lock-In: I,” Point Lookout 11:12, March 23, 2011.
[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Tversky 1973] Amos Tversky and Daniel Kahneman. "Availability: A heuristic for judging frequency and probability." Cognitive Psychology 5:2, 207-232, 1973.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
Throughout this blog, I’ve been using the terms legacy technical debt and incremental technical debt. Legacy technical debt is debt that existed before we undertook the current project; incremental technical debt is debt we incurred in the course of executing the current project. But there is some incremental technical debt that’s actually legacy debt incurred intentionally.
The locomotive known as “The General,” in Union Station, Chattanooga, Tennessee. Built in 1855 in Paterson, New Jersey, for the Western & Atlantic Railroad, it’s best known as the engine stolen by Union spies in the Great Locomotive Chase, as part of a plan to cripple the Confederate rail network during the American Civil War. The General is preserved at the Southern Museum of Civil War and Locomotive History in Kennesaw, Georgia. It was originally built to conform to the southern rail gauge of 5 ft (1,524 mm), but it was converted to the U.S. Standard Gauge of 4 ft 8 1⁄2 in (1,435 mm) after 1886. Its original construction amounted to legacy debt. If it had been built after the war, it would have comprised legacy debt incurred intentionally. Photo “The General, Union Station, Chattanooga, Tenn.,” Detroit Publishing Co., publisher, ca. 1907. Courtesy U.S. Library of Congress.As I’ve defined incremental technical debt, it’s any debt we incur in the course of the current work. That definition works well for most incremental technical debt. For example, if we recognize at the end of the project that we should have done something a bit differently, we’ve incurred incremental technical debt. This is one of the four forms of technical debt identified by Fowler in his 2x2 technical debt matrix [Fowler 2009].
But we must be a bit more careful, because some incremental technical debt is actually legacy debt incurred intentionally.
Legacy technical debt is debt that was incurred earlier, and which we’ve inherited as part of the asset. Sometimes we’re aware of legacy technical debt; sometimes we haven’t actually realized yet that it is indeed technical debt. In any case, the technical artifacts that comprise the legacy technical debt can impose constraints on any new development. Unless we retire the legacy debt, however we modify an asset must be compatible with the assets as they are.
Sometimes technical debt can be both legacy and incremental
Although the two kinds of technical debt — legacy and incremental — might seem at first to be mutually exclusive, there’s a subset of legacy technical debt that can be incurred in the course of executing the current project.
Here’s a physical example:
After the United States Civil War, the state of the U.S. rail system was a bit chaotic. Most of the rail lines in the northeast and western regions of the country used what is called standard gauge rail beds: rails that are separated by 1,435 mm (4 ft 8 1⁄2 in). Most of the South was using a broader gauge: 1,524 mm (5 ft). These conflicting gauges comprised a legacy technical debt. The debt was finally retired over a two-day period beginning on Monday, May 31, 1886, when all the southern railroads coordinated to convert from a 5-foot gauge to 4 feet 9 inches [Southern Railfan 1966].
In the years immediately before the legacy debt was retired, any expansion or repair of the southern rail network that was compatible with the broader gauge, which was about to be retired, would have added to — or maintained — the legacy technical debt. It would thus have comprised newly incurred technical debt that would have also added to the legacy technical debt. Thus, in some situations, some newly incurred technical debt can be legacy technical debt.
Here’s a software example:
A software development team is engaged in a project to enhance the capabilities of the Marigold product, which is one product in the Garden Flowers personal productivity suite. Unfortunately, the original architecture of the suite didn’t anticipate the course that the suite has since taken, and it now comprises legacy technical debt. However, because changing the suite architecture isn’t in the charter of the Marigold enhancement team, they’ll be creating new technical artifacts that are compatible with the current architecture, but which will someday be modified or replaced when the Garden Flowers architecture is revamped or replaced. Thus, some of the new technical debt now being incurred by the Marigold team will be added to the legacy technical debt associated with the Garden Flowers architecture.
Moreover, the Marigold team might incur other technical debt in the course of its activities, if, for example, it fails to complete its task, or completes it in some suboptimal way. In that case it will be incurring incremental technical debt that it probably should retire soon after (if not before) delivery of the Marigold enhancements. Thus, in the same project, it would be incurring both (a) purely incremental technical debt, and (b) incremental technical debt that’s also legacy technical debt.
Why legacy debt incurred intentionally matters
Any program of rational technical debt management entails measuring — or at least estimating — the volume of technical debt incurred in the course of executing each project. The goal is to limit the debt incurred, so as to get control of the total technical debt outstanding.
But with legacy technical debt, as in the example above, we can’t always control the debt we incur. In some projects, it’s necessary to incur additional legacy technical debt because the work we do must be compatible with existing assets. We want to limit incremental technical debt, but we can’t always avoid incurring incremental debt that’s also legacy debt.
This distinction is important for both policy formation and management intervention. For instance, if purely non-legacy incremental technical debt is incurred, we might want to address it immediately, or perhaps commit to addressing it immediately after delivery. Alternatively, if we can obtain good data about a particular kind of legacy technical debt that’s growing because of the need to keep new development compatible with existing debt-ridden assets, we can use that data to elevate the priority of retiring the legacy debt before it grows even larger.
So when we ask projects to report their incremental technical debt, we want them to distinguish between legacy debt incurred intentionally, and incremental debt that was incurred for reasons specific to the project. Having data about both kinds of incremental technical debt is a necessity if we want to take appropriate management action to maintain control of the technical debt portfolio.
References
[Brenner 2011] Richard Brenner. “Indicators of Lock-In: I,” Point Lookout 11:12, March 23, 2011.
[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Tversky 1973] Amos Tversky and Daniel Kahneman. "Availability: A heuristic for judging frequency and probability." Cognitive Psychology 5:2, 207-232, 1973.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
Managing technical debt is something few organizations now do, and fewer do well. Several issues make managing technical debt difficult and they’re discussed elsewhere in this blog. This thread explores tactics for dealing with those issues from a variety of initial conditions. For example, tactics that work well for an organization that already has control of its technical debt, and which wants to keep it under control, might not work at all for an organization that’s just beginning to address a vast portfolio of runaway technical debt. The needs of these two organizations differ. The approaches they must take might then also differ.
A jumble of jigsaw puzzle pieces. Where do we begin? With these puzzles, we usually begin with two assumptions: (a) we have all the pieces, and (b) they fit together to make coherent whole. These assumptions might not be valid for the puzzle of technical debt in any given organization.
The first three posts in this thread illustrate the differences among organization in different stages of developing technical debt management practices. In “Leverage points for technical debt management,” I begin to address the needs of strategists working in an organization just beginning to manage its technical debt, and asking the question, “Where do we begin?” In “Undercounting nonexistent debt items,” I offer an observation about a risk that accompanies most attempts to assess the volume of outstanding technical debt. Such assessments are frequently undertaken in organizations at early stages of the technical debt management effort. In “Crowdsourcing debt identification,” I discuss a method for maintaining the contents of a database of technical debt items. Data maintenance is something that might be undertaken in the context of a more advance technical debt management program.
Whatever approach is adopted, it must address factors that include technology, business objectives, politics, culture, psychology, and organizational behavior. So what you’ll find in this thread are insights, observations, and recommendations that address one or more of the issues related to these fields. “Demodularization can help control technical debt” considers mostly technical strategies. “Undercounting nonexistent debt items” is an exploration of a psychological phenomenon. “Leverage points for technical debt management” considers the organization as a system and discusses tactics for altering it. And “Legacy debt incurred intentionally” explores how existing technical debt can grow as long as it remains outstanding.
[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.
[Cook 2016] John Cook, Naomi Oreskes, Peter T. Doran, William R.L. Anderegg, Bart Verheggen, Ed W. Maibach, J. Stuart Carlton, Stephan Lewandowsky, Andrew G. Skuce, Sarah A. Green, Dana Nuccitelli, Peter Jacobs, Mark Richardson, Bärbel Winkler, Rob Painting, and Ken Rice. “Consensus on consensus: a synthesis of consensus estimates on human-caused global warming,” Environmental Research Letters 11, 2016, 048002.
[Gabriel 2018] Melissa Gabriel. “Hurricane Michael: Fate of costly stealth fighter jets at Tyndall Air Force Base still unknown,” USA Today: Pensacola News Journal, October 17, 2018.
[Kreuter 2004] Marshall W. Kreuter, Christopher De Rosa, Elizabeth H. Howze, and Grant T. Baldwin. “Understanding wicked problems: a key to advancing environmental health promotion.” Health Education and Behavior 31:4, 2004, 441-454.
[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.
[Phillips 2018a] Dave Phillips. “Tyndall Air Force Base a ‘Complete Loss’ Amid Questions About Stealth Fighters,” The New York Times, October 11, 2108.
[Tversky 1973] Amos Tversky and Daniel Kahneman. "Availability: A heuristic for judging frequency and probability." Cognitive Psychology 5:2, 207-232, 1973.
[Zablah 2015] Raul Zablah and Christian Murphy. “Restructuring and Refinancing Technical Debt.” Proceedings of the IEEE 7th International Workshop on Managing Technical Debt (MTD). IEEE, 2015.
