Resources for policymakers concerned with managing technical debt
Category: Solutions
This category emphasizes insights, perspectives, and recommendations about deploying technical debt management programs. Included are posts about retiring, monitoring, documenting, and measuring technical debt.
Rock cairns—also known as rock stacks—in a wilderness area. Some rock stacks in parks and wilderness areas serve a practical purpose as trail markers. But in recent decades, rock stacking has become a fad. Rock stacks aren’t permanent—they use no glue, rings, or fasteners—but their presence does degrade the experience of Nature. The practice continues, in part, because many find the elegance of the structures appealing. As with rock stacks, the elegance of elegantly stated goals has a dark side.
For organizations, an elegantly stated goal is one that anyone can understand, remember, and recite to others. An example for technical debt management is, “We’ll drive technical debt to zero over five years.” Or, “No project is finished if it increases technical debt.” But when the goal relates to solving a problem that has only messy solutions, stating that goal elegantly risks becoming ensnared in what I call the trap of elegantly stated goals.
Because elegant goal statements are so memorable and repeatable, elegantly stated goals spread rapidly, especially if they’re even a bit inspirational. But elegantly stated goals become traps when they incorporate overly simplistic views of how to attain those goals.
And that often happens when technical debt is involved. Here are four guidelines that can help organizations avoid the trap of elegantly stated goals for technical debt.
Beware the halo effect
The halo effect [Thorndike 1920] is a cognitive bias [Kahneman 2011]. It systematically skews our assessments of the qualities of a person, product, brand, company, or any entity, really. If our sense of one quality of the entity is positive, we’re more likely to assess as positive other qualities of that entity. The elegance of a goal statement can cause us to regard the goal as more desirable than we would if the goal were stated less elegantly. For example, the statement, “We’ll achieve zero technical debt in five years,” can increase the chances that we’ll believe that such a goal is attainable. Indeed, some might not even question its desirability, let alone its attainability.
When devising goals for technical debt management, beware the halo effect. Always question desirability, taking costs and benefits into account.
Technical debt matters less than its metaphorical interest charges
The metaphorical interest charges (MICs) on technical debt, rather than the metaphorical principal (MPrin) of the debt itself, are what matter. A goal for total technical debt might be more elegant and more simply stated than would be a goal for technical debts that carry high MICs. But goals for total technical debt can lead to effort spent on debts with low MICs. And those efforts produce little benefit.
When setting goals for technical debt management, pay attention to the MICs. Distinguish between low-MICs and high-MICs technical debt. Keep in mind that MICs can fluctuate. One kind of technical debt can be a low priority at one point in time, and a high priority at another.
Controlling technical debt is safer than trying to drive it to zero
Blind application of an elegantly stated goal can have strikingly silly unintended consequences. Keep in mind that the policymaker’s definition of technical debt is any technological element that contributes, through its existence or through its absence, to lower productivity, or depressed velocity, or a higher probability of defects.
Consider this example of strikingly silly unintended consequences for the goal of zero technical debt. An engineer creates an innovative and superior solution to a previously solved problem. Existing assets that incorporate the old solution are instantly outmoded by the innovative solution. Those existing assets now carry technical debt. If the enterprise directive mandates zero technical debt, some engineering managers might be tempted to do the unexpected. They might inhibit the kind of creativity that leads to innovative solutions to previously solved problems. The temptation arises because introducing those new solutions creates exogenous technical debt in existing assets. Therein lies the trap of the elegantly stated goal.
Throttling efforts to find innovative solutions to previously solved problems is one example of an unintended consequence of trying to drive technical debt to zero. Controlling technical debt is probably a safer option than trying to drive it to zero. Before adopting elegantly stated goals for technical debt, it would be wise to be aware of their possible unintended consequences.
Get control of the behaviors that lead to technical debt
Technical debt management efforts typically emphasize debt retirement or engineering process improvement. While both activities are worthwhile, the root causes of technical debt often lie beyond engineering. See, for example, the thread in this blog exploring nontechnical precursors of technical debt.
For example, across-the-board budget cuts can lead to technical debt. This happens because teams suspend efforts that have already created technical artifacts. If those teams lack resources needed for retracting partially implemented capabilities, the partial implementations remain in place. See “How budget depletion leads to technical debt” for a more detailed explanation of this technical debt formation mechanism.
Budget control tactics like across-the-board cuts can be counter-effective. If they don’t attend to their technical debt implications, they can add to future expenses through the MICs on the debt they generate. They can thus create future needs for budget cuts, and that leads to a vicious cycle. To gain control of technical debt, we must alter these budget control tactics. We need to provide teams with the resources they need for retracting partial implementations. That would ensure that budget reductions don’t lead to technical debt formation.
Investments in technical debt retirement and engineering process improvement are worthwhile. But they can be futile unless we first address the nontechnical causes of technical debt. It’s like bailing out a sinking rowboat without first plugging its leaks. The stated goal, “We’ll drive technical debt to zero in five years,” might better be replaced with, “We’ll get control of the behaviors that lead to technical debt within two years.”
Last words
The template known as SMART goals provides one approach to setting goals with limited exposure to the risk of elegantly stated goals. See “Using SMART goals for technical debt reduction,” for details.
Achieving control of technical debt—rather than attaining any particular level of technical debt—is a useful goal. Either we’ll control technical debt or technical debt will control us.
References
[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
An LED traffic light. This type of signal is more efficient and more cheaply and easily maintained than incandescent signals. But in terms of traffic control, LED signals and incandescent signals are equivalent.
Policymakers whose areas of expertise overlap minimally with software engineering might be at a slight disadvantage when the conversation turns to refactoring. That can happen when the matter at hand involves software assets that carry technical debt. The engineers are likely to argue for resources and time to be set aside for refactoring. Other “re” words are also likely to pop up: restructuring, re-architecting, rewriting, replacing, repairing, retiring, retreating, and reengineering are examples. Some of these are clear—and clearly unaffordable—but some are less clear. What’s needed is a lucid explanation of refactoring for policymakers.
To refactor1 a software asset is to improve its internal structure without altering its external behavior [Fowler 1999]. The improvements usually relate to maintainability or extensibility, and for software, that usually requires improving the readability of its code (for engineers), though it might entail some minor changes of other kinds. Instance by instance, these improvements are usually small in scale. Even so, a refactoring effort might involve small changes throughout the entire asset or throughout an entire suite of assets.
Although we usually regard refactoring as a software-related activity, refactoring, like technical debt, is a concept that can apply to any technological asset. To render the refactoring concept useful for assets other than software, we must be a bit more precise about the effects of the changes involved in refactoring.
A more general definition of refactoring
Refactoring an asset inherently changes that asset; what distinguishes refactoring from other kinds of changes is the observability of the changes. For the definition of refactoring used in software engineering, the changes are observable only to the software engineers who maintain or enhance the asset.
Here’s a definition of refactoring that’s somewhat more widely applicable:
To refactor a technological asset is to apply a series of small, behavior-preserving changes to improve the structure of the asset in ways that have effects that aren’t ordinarily observable externally. When effects are observable externally, they’re very specific, usually related to attributes such as quality and usability.
For example, after a municipality replaces incandescent traffic lights with LED traffic lights, there’s no effect on traffic control. To the untrained eye, the change isn’t noticeable. But those responsible for signal maintenance or for monitoring operating costs will notice significant advantages. With respect to traffic flow, we can regard the change to LED traffic lights as a refactoring of the traffic control system.
Refactoring in manufactured consumer items
Refactoring in manufactured consumer items can be more difficult to recognize, because the useful life of the item so often ends while the item is still in the hands of the consumer. For example, we might ask how to refactor a certain subassembly of an automobile that’s already in service. Some writers have identified the vehicle recall as a kind of refactoring [Shroyer 2016]. But I prefer to regard successive models of manufactured items as containing refactorings of earlier models.
For example, in robot vacuum cleaners, the iRobot Roomba is now available in a ninth-generation “series,” though the exact number of the generations depends on what one counts as first-generation. In laptop computers, most manufacturers’ offerings do change from one model to the next version of that model. Some of these changes are more significant than what we might consider to be refactoring, such as Apple’s removal of the MagSafe power connector [Spence 2018]. For laptops, more likely to be a refactoring would be a change to a slightly more efficient internal fan.
Other applications of the refactoring concept
The refactoring concept can also apply to processes. Indeed, failure to refactor business processes is sometimes a cause of needless complexity, high maintenance costs, and other difficulties. Some of these difficulties can appear in technological assets that must interact with processes that need refactoring [Distante 2014]. The refactoring concept might even find use in organizational restructuring and debt restructuring.
Endnote
[1] One might wonder why this process is called refactoring. Martin Fowler, the author of the classic 1999 book about refactoring, has investigated the etymology of the word and concludes that it likely arose in the Forth and Smalltalk communities in the 1980s. [Fowler 2003] Jump back to the text
References
[Distante 2014] Damiano Distante, Alejandra Garrido, Julia Camelier-Carvajal, Roxana Giandini, and Gustavo Rossi. “Business processes refactoring to improve usability in E-commerce applications.” Electronic Commerce Research 14:4 (2014): 497-529.
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
Elegantly stated goals are clear, memorable, and easily recited. They propagate rapidly. The trap of elegantly stated goals is that they might not be attainable.
Because situational details are so important, there’s no formula for choosing to outsource technical debt retirement projects. But there are guidelines.
When we retire technical debt from assets, service disruptions might be necessary. Automating some procedures can minimize those disruptions and avoid errors and rework.
Organizations are more likely to gain control of their legacy technical debt portfolio if they begin by controlling the transformation of incremental technical debt into legacy technical debt.
Technical debt that appears as discrete chunks—localizable technical debt—is more readily retired, because we can do it incrementally, piece by piece. It’s a particularly manageable challenge.
For some assets, we can’t allow debt to persist, and we can’t afford replacements. We must retire the debt. This post begins exploring what it takes to design projects to retire technical debt in irreplaceable assets.
The I-35W Bridge collapse, day 4, Minneapolis, Minnesota, August 5, 2007. The proximate cause of the collapse was underweight gusset plate design, which made the bridge vulnerable to the increased static load due to concrete road surfacing additions over the years, and to the weight of construction equipment and supplies during a repair project that was then underway [NTSB 2008].
When we conduct maintenance or technical debt retirement projects involving assets that must remain operational during project execution, we risk stressing the asset in ways that extend beyond its safe operation envelope. The National Transportation Safety Board found that this occurred in the case of the I-35W bridge collapse. These effects are more difficult to imagine in software systems, but they can occur when we shift load from the systems undergoing modification to other systems. Those load-bearing systems can then become overloaded. Or these effects can occur when we shift load not from one asset to another, but from one time window to another on the same asset. The result can be high loads in some time windows.
Photo by Kevin Rofidal, United States Coast Guard, courtesy Wikimedia Commons.
When we transform assets to retire some of the technical debt they carry, service disruptions are sometimes necessary. To minimize service disruptions while technical debt retirement efforts are underway, it’s advantageous to automate some procedures. Automation-assisted technical debt retirement provides two important benefits: reduced disruption of operations and reduced incidence of errors.
The meaning of automation
I’m using the concept of automation a bit loosely here. I don’t mean to imply that these procedures are autonomous. What I mean is that engineers have available tools for performing many operations with a minimum of thought. For example, in this sense of automated, an engineer can issue a command such as, “Test Module Alpha Using Test Suite Delta.” That command executes a predefined set of tests. Following execution, the appropriate engineers receive the results. The tool also archives those results appropriately. If the results are anomalous, engineers can then take appropriate action.
Benefits of automation-assisted technical debt retirement
The more obvious benefit of automated procedures is speed. For example, an asset removed from service for testing can be returned to service more quickly if the testing is automated. And if trouble erupts during operations of a newly transformed asset, engineers can swap the untransformed asset back into place quickly. So-called roll-out and roll-back tools are just a few of the many elements of a set of practices collectively known as continuous delivery [Humble 2010].
The second benefit of this kind of automation is error avoidance. For example, inconsistent or incomplete testing can fail to find errors and defects, and that leads to rework and further disruptions. Performing tests incorrectly, finding “defects” that aren’t there, is another way to generate trouble. Automated procedures are much less prone to error if we maintain, test, and certify them periodically. For example, consider subjecting a module to a particular test suite. With automation assistance, engineers needn’t remember (or take time to look up) how to prepare the asset for tests. They needn’t remember (or take time to look up) how to run the tests, or what the members of the test suite are. Long advocated as an essential element of sound engineering practice, test automation can avoid some of these problems. But it’s far short of a panacea [Bach 1999].
Other automation opportunities
In some situations, we can automate debt retirement itself. When we can retire instances of the technical debt in question by performing an automated transformation on an asset, the transformation is faster and more reliable.
A most important practice associated with automation-assisted technical debt retirement is automation-assisted regression testing. Investments in thorough and focused regression testing have potentially shockingly high returns in the debt retirement context. They can be just as valuable during development and routine maintenance.
To perform a regression test on an asset that has undergone a change is to examine its behavior under a specified set of conditions. Such investigations can determine whether those changes caused the asset to misbehave. So a regression test determines whether the asset has regressed as a result of the change. Automated or automation-assisted regression tests help the project team detect problems in assets that they’ve transformed. And that’s much better than having the business units that depend on those assets encounter problems during operations [Ge 2014].
Many of these same regression tests can also be useful during enhancement and ongoing maintenance of the asset. Often, investing in automated regression tests in advance of the debt retirement project can enhance development and maintenance performance relative to those assets. Later, when the debt retirement project begins, the previously obtained results of regression tests will already be available.
Last words
For some debt retirement projects, specially created automated regression tests might be beneficial. Assign engineers to continual automation tool development for debt retirement projects. That’s probably the best way to support these needs.
These automation capabilities are unlikely to be available commercially, because they’re so specialized to the asset being tested. Because general applicability is unnecessary, building them in-house is both practical and economical. If people with the necessary skills are unavailable, acquire them. We can justify these investments economically if we take into account the savings from reduced service disruptions during technical debt retirement projects.
References
[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
A sinking rowboat provides a useful metaphor for illustrating the process of controlling incremental technical debt. The enterprise is the rowboat. The leaks are the properties of the enterprise and its environment that lead to creating incremental technical debt. The water entering the boat through leaks is incremental technical debt. The accumulated water in the bottom of the boat is legacy technical debt.
All technical debt in enterprise assets is either incremental technical debt or legacy technical debt or both. Incremental technical debt is technical debt newly incurred. It can be newly incurred exogenous technical debt, or it can be endogenous technical debt incurred either in projects currently underway, or projects just recently completed. Legacy technical debt is technical debt associated with assets, and which wasn’t incurred recently or which exists in any form prior to undertaking work on those assets. All legacy technical debt was at some point incremental technical debt. The vast amounts of legacy technical debt most organizations now carry are nothing more than the accumulation of incremental technical debt. The path to managing legacy technical debt therefore begins with controlling incremental technical debt.
The leaky rowboat metaphor
Organizations are more likely to gain control of their legacy technical debt portfolio if they begin by controlling the formation of incremental technical debt, and its transformation into legacy technical debt. A metaphor might make this clear:
If you find yourself in a sinking rowboat, bailing out some of the water is a good idea. It might even be necessary in the short term. But at some point, fixing the leaks where the water comes in is advisable. Unless you address the existing leaks, and prevent new ones from forming, your fate is sealed. You’ll spend increasing portions of your time, energy, and resources bailing out your leaky rowboat. You’ll spend declining portions of your time, energy, and resources rowing the boat towards your objective. And when you do devote some time and energy to rowing towards your objective, you’ll find the rowing surprisingly difficult. The boat’s leaks make it ride lower in the water. And because you must propel not only the boat and its payload, but also the dead weight of the water in the bottom of the boat.
In this metaphor, legacy technical debt is the water in the bottom of the boat, and incremental technical debt is the water coming in through the leaks. The leaks are the proximate “causes” of technical debt. The root causes of the leaks are the root causes of technical debt.
Setting technical debt management priorities
If the enterprise is in the midst of a legacy technical debt emergency, retiring some of it is necessary in the short term. But unless the enterprise addresses incremental technical debt and its root causes, a new burden of legacy technical debt will accumulate. That accumulation is then likely to eliminate the benefits of having retired the current burden of legacy technical debt.
So after the legacy technical debt emergency is passed—or if resources permit, during the emergency—establishing measures, procedures, and practices for controlling incremental technical debt would be prudent.
This change might be less challenging than it sounds. With respect to endogenous incremental technical debt, the teams that incurred it are either still at work, or just recently dispersed. Their understanding of the incremental technical debt is still fresh in their minds. If their projects are still underway, and if budget and schedule permit, retiring the incremental technical debt in the context of those projects is a superior strategy. For projects that have already delivered their work, a less preferable but still practical approach involves re-assembling some of the team. They then work to retire the incremental technical deb, while memories are still fresh.
For incremental exogenous technical debt, the team that was active when the debt formed might have little knowledge of how the debt came to be. In those cases, re-assembling the team provides little advantage. Specialized knowledge of the technical debt might prove more helpful in devising a strategy for retiring it.
For the most part, the problem of controlling incremental technical debt isn’t a technical one. It usually reduces to a problem of finding time and resources to undertake the task.
Why resources aren’t available to retire incremental technical debt
The immediate reason why most teams don’t have enough resources to retire their incremental technical debt is that the organization, as a whole, doesn’t plan for retiring incremental technical debt incrementally. This immediate reason, though, isn’t fundamental. The lack of resources is a symptom of deeper dysfunctions in the organization. The real question is this: Why do so many organizations fail to allocate time and resources to retire incremental technical debt incrementally? Here are three reasons.
Misunderstanding (or no understanding) of the concept of technical debt
The organization is unlikely to be able to manage any kind of technical debt unless its people understand the concept. They must understand that technical debt isn’t necessarily the result of engineering malpractice. For example, much technical debt arises as a natural result of working with technology. Another example: it can be a result of organizational forms that compel people to behave in ways that lead to technical debt. Unless the people of the organization accept these truths, allocating sufficient resources to managing incremental technical debt is unlikely.
Decisions regarding technical debt management ultimately reduce to a choice between allocating precious resources to technical debt retirement, and allocating them elsewhere. To make this choice responsibly, it’s necessary to fully appreciate the cost of carrying technical debt. Most believe that these costs appear in the form of lost engineering productivity. While that is indeed a factor, other factors can be far more important.
For example, if entry into an important market is delayed by even as little as 30 days due to debt-depressed engineering productivity, the financial consequences can be enormous and insurmountable. Or delays in diagnosing and repairing a fault in a product can produce financial liabilities that can actually sink the company. When one considers all possible financial consequences of carrying technical debt, it becomes clear that managing technical debt effectively is actually a strategy for survival. The decision to allocate appropriate resources to incremental technical debt retirement does require modeling these costs—calculations that few organizations actually undertake.
Miscalculating projections of returns on investments
Failing to estimate MICs with sufficient precision is problematic because it reduces the quality of decisions regarding short-term resource allocations. But it also affects long-term projections, which depend on estimating returns on investments. For example, to choose between investing in retiring incremental technical debt from an asset and investing in new capabilities for the same asset, one must compare the projected value of each choice. The investment decision could be biased in favor investing in new capabilities for many reasons. For example, the decision maker’s understanding of technical debt might be deficient. Another example: the calculations of MICs might be incomplete or underestimated. Incremental technical debt retirement might therefore be systematically deferred or avoided altogether.
The widespread belief that MICs consist largely of lost productivity of engineers almost ensures a dramatic underestimate of MICs.
Policy recommendations for controlling incremental technical debt
The simplistic approach to controlling incremental technical debt is to provide more money to projects and to engineering functions. While that approach will be somewhat helpful, its results will likely be disappointing when compared to approaches that combine resource augmentation with changes in enterprise policy, processes, and culture.
Let’s begin with an example of a needed cultural change. Nearly anyone who makes or influences decisions might occasionally bear some responsibility for incurring incremental technical debt. To achieve effective control of technical debt requires that everyone understand how to change their behavior. Any guiding principle must be simple to state and easy to understand, because we must communicate it to nearly everyone. Here’s a sample statement of a useful such a principle:
Those whose decisions lead to new technical debt are accountable for securing the resources needed to retire that debt. They might also be accountable for supplying compensating resources to those within the enterprise, or among its customers, who are negatively affected. For example, those negatively affected might suffer depressed operational effectiveness during the period in which that technical debt is outstanding.
I call this the Principle of Accountability. It’s a corollary to what Weinberg calls “Ford’s Fundamental Feedback Formula” [Weinberg 1985], which captures the idea that people make better choices when they must live with the consequences of those choices.
General guiding principles are necessary, but not sufficient. Here are five examples of changes that help in controlling incremental technical debt [Brenner 2017b].
1. Adopt a shared concept vocabulary
There must be general agreement among all parties about the meanings of concepts that relate to incremental technical debt formation.
2. Accept that technical debt is a fact of technological life
There is a widespread belief that most technical debt results from engineering malpractice. Although some technical debt does arise this way, most does not. For examples of other causes, see “Nontechnical precursors of nonstrategic technical debt.” Some technical debt arises because of advances external to the enterprise, beyond its control. Development-induced or field-revealed discoveries are especially difficult to avoid. In many instances, technical debt is an inevitable result of using technology.
3. Track the cost of carrying technical debt
The cost of retiring a particular class of technical debt (its MPrin) is significant only in the context of planning or setting priorities for resource allocation. In all other contexts, knowing that cost has little management value. What does matter, at all times, is the cost of carrying that technical debt—the MICs, or metaphorical interest charges. (See “The Principal Principle: Focus on MICs.”) MICs can fluctuate wildly [Garnett 2013].
Build and maintain expertise for estimating and tracking the costs of incurring and carrying each class of technical debt. Know how much each kind of technical debt contributes to these costs, now and for the next few years.
4. Assign accountability for kinds of technical debt
Some kinds of incremental technical debt result from actions (or inactions) within the enterprise. Some do not. To control the incremental technical debt that arises from internal causes, hold people accountable for the debt their actions generate. Use Fowler’s Technical Debt Quadrant [Fowler 2009] as the basis for assessing and distributing internal financial accountability. It’s useful for both debt retirement costs (MPrin) and metaphorical interest charges (secured technical debt is technical debt for which resources have been allocated (possibly in a forward time period) to guarantee the debt’s retirement and possibly its associated MICs.
This policy implies that deliberately incurred technical debt, whether incurred strategically or recklessly, must be secured. If anyone involved feels that technical debt has been incurred, a dispassionate third party reviews project deliverables for the presence of technical debt. However, allocating resources might require securing commitments of resources for future fiscal periods. For many organizations, such forward commitments might require modifying the management accounting system.
Last words
Controlling incremental technical debt requires changes well beyond the behavior and attitudes of engineering staff, or the technologies they employ. Achieving control of incremental technical debt formation requires engagement with enterprise culture to alter the behavior and attitudes of most of the people of enterprise.
References
[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
Electricity pylons, Hamilton Beach, Ontario, Canada: a small part of the AC power grid, which seems destined one day to manifest a great deal of nonlocalizable technical debt. Photo by Ibagli courtesy Wikimedia Commons. Pylons in the same line are visible in Google Street View.
When technical debt appears as discrete chunks—when it’s localizable—we can often retire the debt incrementally. We can retire it system-by-system, module-by-module, or even instance-by-instance. These approaches offer great flexibility, both technically and financially. That makes retiring localizable technical debt a particularly manageable challenge.
Localizable technical debt
In “Technical debt in a rail system,” I explored the case of Amtrak’s Acela Express. In that example, I explained that Acela’s passenger cars can tilt to compensate for centrifugal forces that appear when the train rounds curves. The technical debt is in the form of tracks that are too close together to permit the trains to tilt as much as they’re designed to. That limits the trains’ speed rounding curves. The instances of this debt are the curves in which the tracks are too close together. These instances are thus inherently localizable.
In “Debt contagion: how technical debt can create more technical debt,” I described an example in which an organization is unable to upgrade its desktop computers from Windows 8 to Windows 10. In this case, each computer running Windows 8 is an instance of this form of technical debt.
Both of these examples illustrate localizable technical debt. Each instance is self-contained. We can “point” to it as an instance of the debt in question. But localizable technical debt need not be associated with hardware. In software systems, for example, localizable technical debt can exist in a module interface. If interface meets a requirement that’s no longer relevant, it might contain technical debt. That module and any other modules that interact with that interface therefore manifest that technical debt.
Nonlocalizable technical debt
Nonlocalizable technical debt is debt for which the instances are amorphous or system-wide. Or they span the bulk of the system, if not all of it. Retiring nonlocalizable technical debt typically requires extensive reengineering of the assets that manifest it.
For the most part, nonlocalizable technical debt arises at the level of system architecture or above. One can easily imagine this occurring in software systems, where physical constraints have little meaning. But let’s consider a hardware system to illustrate the importance of this concept.
An illustration of nonlocalizable technical debt
Until relatively recently in the United States, most electric power consumers used power for incandescent lamps, heating, or for electric motors. These applications are compatible with an alternating current power distribution system (AC grid). The AC grid is more efficient than an equivalent direct-current architecture (DC grid) when power generation plants are few and relatively distant from power load sites. The efficiency advantage is due to AC’s lower transmission losses compared to DC.
However, advances in electronics and in distributed power generation are eroding the advantages of the AC grid [Dragičević 2016]. Most electronic devices—phones, computers, rechargeable power tools, LED lighting, and electric vehicles—use DC internally. To access the AC grid, they change AC power into DC power, which entails efficiency losses.
Moreover, most renewable power generation systems generate DC inherently. Wind turbines generate AC at a frequency determined by wind power conversion efficiency, but they then convert it to DC before a second conversion to AC at the frequency of the AC grid. And because solar and wind power generators are geographically dispersed, they’re often situated near their load sites. Therefore, the losses due to transmission from generation site to load site are less important than they would be if the generation sites were few, concentrated, and at great distances from the loads they serve.
Our current AC grid architecture is likely to become a net disadvantage in the not-too-distant future. If that happens, we could come to regard the current AC grid as manifesting technical debt. The devices that are designed for the AC grid would also manifest that debt. However, localizing that debt in each device and each component of the AC grid would make little sense. The technical debt in question would reside in the grid architecture, as a whole. It would be inherently nonlocalizable.
Addressing localizable technical debt
As noted above, we can often retire localizable technical debt incrementally—instance-by-instance. In many cases, this enables engineers to address the debt at times and in sequences that are compatible with organizational priorities. By spreading the effort over time, the organization can ensure that costs are within the organization’s capacity in any given fiscal period. This isn’t always possible for localizable technical debt. And engineers are often justifiably averse to the temporary non-uniformity that results from incremental debt retirement. But exploiting localizability when planning debt retirement is often a useful strategy for retiring technical debt economically.
Temporary structures
Retiring localizable technical debt incrementally does present some problems. During the retirement process, for any given instance, temporary structures might be necessary to support continued operation with minimal service disruption. For example, with the Acela tracks, an alternate line might serve while the new track installation is in progress. Or the new track might follow a course at some distance from the existing track while trains continue using the existing track. Both approaches require investment beyond the investment required for the new track itself. Some managers have little appetite for such temporary investments. But temporary investments are in a real sense part of the MICs on that debt. They’re unusual in the sense that they’re part of the debt retirement effort, but they’re still MICs. In a way, they’re analogous to the charges that might appear when terminating an auto lease.
Entanglements of different kinds of technical debt
Another consideration when addressing localizable technical debt is its entanglement with other forms of technical debt. With respect to the effort to retire one kind of localizable technical debt, these other forms of technical debt are what I’ve called auxiliary technical debt (ATD). Consider carefully the time order of efforts to retire the localizable technical debt and one or more forms of its ATD. Because retiring localizable technical debt can seem deceptively straightforward, the temptation to deal with it before addressing some of the ATD can be difficult to resist. But dealing with some of the ATD first might actually be the wiser course. For example, when doing so eliminates numerous instances of the localizable technical debt, dealing with the ATD first can produce real savings.
One note of caution
Within the category of localizable technical debt are kinds of debt that are so widespread that retiring them affects a large part of the asset. Each instance of such debt might be identifiable and localized. But the instances are so widespread that they collectively have the properties of nonlocalizable debt. Incremental retirement might still be possible, but a more global retirement effort might be safer and less disruptive.
One approach technologists usually favor is suspending all other work while the debt in question undergoes retirement. While that approach might indeed be safest, all stakeholders must accept and understand the technical issues. And the technologists must understand the concerns of all stakeholders. A joint decision about the retirement strategy among all stakeholders, including technologists, is probably safest.
Last words
In the context of debt retirement projects, localizable technical debt provides needed flexibility. Often, the non-uniformity that results from retiring localizable technical debt instance-by-instance can be reduced before the debt retirement project is completed. In the meantime, the team can be relatively free to retire the localizable debt in whichever order is most fitting.
References
[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).
[Dragičević 2016] Tomislav Dragičević, Xiaonan Lu, Juan C. Vasquez, and Josep M. Guerrero. “DC Microgrids–Part II: A Review of Power Architectures, Applications and Standardization Issues,” IEEE Transactions on Power Electronics, vol 31:5, 3528-3549, 2016.
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
Two alternatives to retiring legacy technical debt in irreplaceable assets. Neither one works very well.
Some irreplaceable assets carry legacy technical debt. Although retiring an asset retires its technical debt, that option isn’t available for irreplaceable assets. We need another option. For irreplaceable assets, we need to find a way to retire the debt. As decision makers gather information and recommendations from around the organization, most will come to an unsettling conclusion. They’ll find that information and recommendations aren’t sufficient for making sound decisions about technical debt retirement. The issues are complex. Education is also needed. It’s entirely possible that in some organizations, the existing executive team might be out of its depth. To understand how this situation can arise, let’s explore the nature of legacy technical debt retirement decisions.
A common technical debt retirement scenario
What compels the leaders of a large enterprise to consider retiring the technical debt encumbering one of its irreplaceable assets is fairly simple: cost. Decision makers usually begin by investigating the cost of replacing the asset. This is the option I’ve cleverly called “Replace the Asset.” They then typically conclude that replacement isn’t affordable. At this point, many decision makers choose the option I’ve called “Do nothing.” Time passes. A succession of incidents occurs, in which teams attempt required repairs or enhancements of the asset. And I use the term required here to mean “essential to the viability of the business.”
Engineers then do their best to meet the need, but the cost is high, and the work takes too long. The engineers explain that the problems are due, in part, to the heavy burden of technical debt. Eventually someone asks the engineers to estimate the cost of “cleaning things up.” Decision makers receive the estimates and conclude that it’s “unaffordable right now.” They ask the engineers to “make do.” In other words, they stick with the Do Nothing option.
After a number of cycles repeating this pattern, decision makers finally agree to provide time and resources for technical debt retirement, but only because it’s the least bad alternative. The other alternatives—Replace the Asset, and Do Nothing—clearly won’t work and haven’t worked, respectively.
So there we are. Events have forced the organization to address the technical debt problems in this irreplaceable asset. And that’s where the trouble begins.
Decisions about retiring legacy technical debt
In scenarios like the one above, people have already made the fundamental decision: the enterprise will be retiring legacy technical debt from an irreplaceable asset. But that’s just the first ripple of waves of decisions to come. Many people in a variety of roles throughout the enterprise will be making many decisions. Let’s now have a look at a short catalog of what’s in store for such an enterprise.
Recall that most large technical debt retirement projects probably exhibit a high degree of wickedness in the sense of Rittel and Webber [Rittel 1973]. One consequence of this property is the need to avoid do-overs. That is, once we make a decision about how to proceed to the next bit of the work, we want that decision to be correct, or at least, good enough. The consequences of that decision should not leave the enterprise in a state that’s more difficult to resolve than the state in which we found it. Since another property of wicked problems is the prevalence of surprises, most decisions must be made in a collaborative context, which affords the greatest possibility of opening the decision process to diverse perspectives. We must therefore regard collaborative decision-making at every level as a highly valued competency.
What follows is the promised catalog of decision types.
This decision category leads the list. It provides the highest leverage potential for changing enterprise behavior vis-à-vis technical debt. Organizations confronting the problem of technical debt retirement from irreplaceable assets would do well to begin by acknowledging that although they might be able to deal with the debt burdening these assets right now, they must make a strategic change if they want to avoid even worse trouble. Accumulating debt to a level sufficient to compel chartering a major debt retirement project took years of deferring the inevitable. A significant change of strategy is necessary.
When changing complex social systems, applying the concept of leverage provides a critical advantage. Following the work of Meadows [Meadows 1997] [Meadows 1999] [Meadows 2008], we can devise interventions at several points that can have great impact on the rate of accumulation of technical debt. The leverage points of greatest interest are Feedback Loops, Information Flows, Rules, and Goals. For example, the enterprise can set a strategic goal of a specific volume of incremental technical debt incurred per project, normalized by project budget. See “Leverage points for technical debt management.”
One might reasonably ask why enterprise strategy must change; wouldn’t a change in technology strategy suffice? Changing how engineers go about their work would help—indeed in most cases it’s necessary. But because the conditions and processes that lead to technical debt formation and persistence transcend engineering activities, additional changes are required to achieve the objective of controlling technical debt.
Some technical debt is incurred as the result of a conscious decision. But some is nonstrategic. We might even be unaware of how it occurred. Both kinds of technical debt can arise as a result of nontechnical factors. Read a review of nontechnical precursors of nonstrategic technical debt.
Organizational decisions
Before chartering a technical debt retirement project (DRP) for an irreplaceable asset, it’s wise to consider how to embed the DRP in the enterprise.
The default organizational form for DRPs concerned with an asset A is usually analogous to that used for major projects focused on asset A. If the Information Technology (IT) unit would normally address issues in asset A, the debt retirement effort usually would be organized under IT. If A is a software product normally attended to in a product group, that same group would likely have responsibility for the DRP for asset A.
Although these default organizational structures are both technically and politically sensible, there’s an alternative approach worth investigating. It entails establishing a technical debt retirement function that becomes a center of excellence for executing technical debt retirement projects. That unit is also responsible for developing sound technical debt management practice. Such an approach is especially useful if the organization contemplates multiple debt retirement projects.
The fundamental concept that makes the center-of-excellence approach necessary is the wickedness of the technical debt retirement problem. To address the problem at scale requires capabilities beyond what IT, product units, or any conventional organizational elements can provide. The explosion of technical debt in most organizations is an emergent phenomenon. Every organizational unit contributed to the formation of the problem. And every organizational unit must contribute to its resolution.
Engineering decisions
Engineers tend to identify and classify technical debt items on technical grounds. Further, they tend to set technical debt retirement priorities on a similar basis. That is, they tend to set priorities highest for those debt items that they (a) recognize as debt items and (b) see as imposing high levels of MICs charged to engineering accounts. Engineers are less likely to assign high priorities to technical debt that generates MICs that are charged to revenue, or to other accounts, because those MICs are less evident—and in many cases less visible—to engineers.
Decisions regarding recognition of technical debt items and setting priorities for retiring them must take technological imperatives into account, but they must also account for MICs of all forms. Priorities must be consistent with enterprise imperatives.
Decisions about pace
Paraphrasing Albert Einstein, technical debt retirement projects should be executed as rapidly as possible, and no faster. The tendency among nonengineers and nontechnical decision makers is to push for rapid completion of debt retirement projects, for three reasons. First, everyone, like the engineers, wants the results that debt retirement will bring. Second, everyone, like the engineers, wants an end to the inevitable disruptions debt retirement projects cause. And finally, the longer the project is underway, the more it might cost.
For these reasons, once the decision to retire the debt is firmly in hand, the enterprise might have a tendency to apply financial resources at a rate that exceeds the ability of the project team to execute the project responsibly. When that happens, rework results. And for wicked problems like debt retirement, rework is the path to catastrophe.
Decisions about pace and team scale need to be regarded as tentative. Regular reviews can ensure that the resource level is neither too low nor too high. Even when the engineers are given control over these decisions, these decisions must be reviewed, because pressures for rapid completion can be so severe that they can compromise the judgment of engineers about how well they can manage the resources applied to the project.
Resource decisions
Debt retirement projects concerned with legacy irreplaceable assets are different from most other projects. Estimates of the labor hours required are more likely to be incorrect on the low side, because so much of the work involves pieces of assets with which few staff have experience. But with respect to resources, underestimating labor requirements isn’t the real problem. Nonlabor resources are the real problem.
Irreplaceable assets probably provide critical support of ongoing operations. In some cases, the need for the assets is continuous. Many organizations have kept such assets operational by using periods of low demand for maintenance, usually scheduled and announced in advance. These practices are likely adequate for routine maintenance and enhancement. But debt retirement need a level of access to the asset that continuous delivery practices can provide [Humble 2010].
However, assets whose designs predate the widespread use of modern practices such as continuous delivery might not be compatible with the infrastructure that these practices require. In organizations that haven’t yet adopted such practices, few if any staff are experienced with them. We must therefore regard as developmental any projects whose objectives are retiring technical debt from irreplaceable assets. They’re retiring the technical debt, but they’re also developing the practices and infrastructure needed for debt retirement projects. This dual purpose drives the surprisingly high nonlabor costs associated with early debt retirement projects.
The investments required might include such “items” as a staging environment, which “is a testing environment identical to the production environment” [Humble 2010]; extensive test automation, including results analysis; blue-green deployment infrastructure; automation-assisted rollback; and zero-downtime release infrastructure. Decisions to make investments require an appreciation of their value to the enterprise. They enable the enterprise to deal effectively with the wicked problem of technical debt retirement.
Last words
Because every situation and every organization is unique, few general guidelines are available for making these decisions. The criteria most organizations have been using for dealing with (or avoiding) the issue of technical debt have produced the problems they now face. So, to succeed from this point, whatever criteria they use in the future must be different. My own view is that short-term thinking is at the heart of the problem, but it’s a wicked problem. The long-term solution will not be simple.
References
[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).
[Dragičević 2016] Tomislav Dragičević, Xiaonan Lu, Juan C. Vasquez, and Josep M. Guerrero. “DC Microgrids–Part II: A Review of Power Architectures, Applications and Standardization Issues,” IEEE Transactions on Power Electronics, vol 31:5, 3528-3549, 2016.
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
Guardrails in a track bed as a rail line crosses a bridge. The guardrails are the inner pair of rails. The rails just outside the inner pair are the running rails. Guardrails (also known as check rails) keep the wheels of derailed cars from straying too far from their proper locations. This is a risk mitigation function in high-risk geometries such as curves. It’s also advantageous even if the probability of risk events is low, as in this straight section of track. It’s worthwhile when the consequences of risk events are extremely costly. A derailment on a railway bridge or in steep terrain can result in rail vehicles falling to the earth below. That can cause them to pull other vehicles with them. Derailments under highway overpasses can also be problematic. Such derailments can result in damage to rail or highway bridge structures. That damage can result in service interruptions for periods much longer than the time needed to clear the derailment. For this same reason, guardrails are also used in tunnels and tunnel approaches. Because uncontrolled scope expansion can have such devastating effects, we need policy guardrails to control scope expansion when retiring technical debt from assets that contain auxiliary technical debt.
As noted earlier, a technical debt retirement project (DRP) emphasizes retiring a particular kind or kinds of technical debt from a set of assets. But those assets might also carry other kinds of technical debt. With respect to a given DRP, we can regard this other technical debt as Auxiliary Technical Debt (ATD). Because the presence of ATD can defocus debt retirement projects, it presents a risk we must anticipate and mitigate.
A word of caution
One word of caution. The technical debt discussed in this post is assumed to be localizable. Localizable technical debt is technical debt that manifests itself as discrete chunks. Each instance is self-contained, and we can “point” to it as an instance of the debt in question. More: “Retiring localizable technical debt”
Mitigating the risks associated with the auxiliary technical debt
This post explores concepts and approaches for mitigating the risks associated with the auxiliary technical debt (ATD) of a given debt retirement project (DRP). As might already be evident, these initialisms (ATD, DRP, and one more to come) can be difficult to keep straight. Here’s a quick guide:
T always means Technical
D always means Debt or Design
R always means Retirement
P always means Project
So, DRP is Debt Retirement Project.
Also, if you have a pointing device, you can hover the cursor over the first mention of each initialism in each section. Then your browser displays the expansion of the initialism. Touch screen users and keyboarders: sorry, I haven’t yet figured out how to help you in an analogous way, so let me know if you have an idea.
I’ve been using the term TDIQ—Technical Debt In Question—to denote the kinds of technical debt whose retirement is the objective of a given DRP. The ATD of that DRP, then, is the collection of instances of any other kinds of technical debt. That is, all types differing from the TDIQ of the DRP, and which are present in the assets being modified by the DRP. Notice that the property of being auxiliary technical debt is relative. It’s relative to the objectives of a given DRP. A particular instance of technical debt might be ATD for one DRP, and TDIQ for another DRP, depending on the respective objectives of each DRP. Notice also that the ATD of a given DRP can include several different kinds of technical debt.
The temptation to retire auxiliary technical debt
Let’s now examine a scenario in which ATD can generate risk for a DRP. In this scenario, we’ll consider only one kind of ATD; call it ATD0.
Suppose several members of the DRP team undertake work to retire the DRP’s TDIQ in a portion of one of the debt-bearing assets. In performing this work, they encounter some instances of ATD0. Studying these instances of ATD0 carefully, they devise a plan. They realize that “fixing” the ATD0 along with the TDIQ in that portion of the asset would be easier and less risky than amore focused approach. The more focused approach would be to leave the ATD0 in place and attend only to the TDIQ. Let’s call the approach they adopted the ATD approach. And let’s say that the TDIQ approach is one in which the team addresses only the TDIQ. It leaves in place the ATD0 and all other ATD it finds.
Compared to the TDIQ approach, the advantages of the ATD approach are fairly clear. After the work is complete, in either approach, the team must test and re-certify the asset. In the TDIQ approach, when a subsequent DRP tries to retire ATD0, that second DRP team will need to test and re-certify the asset again. In the ATD approach, we can avoid modifying, re-testing, and re-certifying the asset a second time. We can avoid it because we’ve already retired all instances of ATD0 from the asset. Thus, in the ATD approach we can avoid a second round of modification, testing, and re-certification.
Risks associated with retiring auxiliary technical debt
But the ATD approach also has some serious disadvantages.
Enterprise assets might be left in a mixed state
Unless the team plans to retire all instances of ATD0, then upon completion of the DRP, enterprise assets will be in a mixed state. Some will be free of both the TDIQ and ATD0; some will be free of the TDIQ but continue to harbor ATD0. This non-uniformity can create complications for subsequent maintenance, documentation, testing, training, enhancement, automation assisted development, and so on.
Complications in testing and re-certification
If test results for the modified assets indicate the possibility of new defects, the cause might be associated with the TDIQ work, or the ATD work, or both. Resolving any issues in the test results is thus more complicated under the ATD approach than it is under the TDIQ approach. Similar considerations affect re-certification. Thus, there is a risk that the ATD approach will complicate interpretation of test and re-certification results.
Questions about the reliability of technical debt inventory data
As noted in an earlier post, for any given DRP, the DRP team needs to know which assets bear that project’s TDIQ. In the TDIQ approach, any data previously or concurrently gathered about the location of instances of ATD0 remains valid. It maintains its validity because the TDIQ approach doesn’t retire any instances of ATD0. However, in the ATD approach, such inventory data must be corrected to account for the retirement of whatever instances of ATD0 are retired in the ATD approach. Thus, there are problems if ATD0 inventory data has already been collected, or if it’s being collected in parallel with the DRP. The DRP team must then take steps to adjust the inventory data regarding locations of ATD0 as it retires instances of it.
There is one minor exception to this claim about TDIQ’s validity-preserving qualities. In some instances, retiring an instance of TDIQ can incidentally retire an instance of ATD0. It happens.
There is of course a risk that this will not occur as needed. If that risk materializes, it can create problems for any subsequent DRP for which the ATD0 is contained in its TDIQ. This can be especially challenging if there are multiple DRPs in process simultaneously. Suppose each DRP is working on different TDIQs, potentially in different debt-bearing assets. If they all encounter and retire instances of ATD0, keeping the inventory current can be a complicated affair.
Unconstrained scope creep
Suppose there is a DRP whose objective is retiring its TDIQ. And suppose it has decided to also retire instances of a particular kind of ATD, say ATD0. Although that activity would represent an expansion of scope beyond retiring the TDIQ, it might be acceptable and it might even be prudent. But as the team undertakes to retire ATD0, it might confront a similar problem. That problem relates to the relationship between the ATD0 and yet another kind of ATD, which we might call ATD1. The DRP team might then decide to expand scope again. And so on. In general, there is no self-evident stopping point for such a chain of scope expansion. In these circumstances, scope creep can become an unmitigated risk, threatening the coherence and focus of the DRP, with consequences for its budget and schedule.
Last words
In some cases, some of the ATD might be so intertwined with the TDIQ that retiring some instances of the TDIQ necessarily retires some of the ATD. And in other cases, leaving the ATD in place severely complicates retiring the TDIQ. In still other cases, leaving the ATD in place leaves the assets in a complex state that makes ongoing maintenance or enhancement work more difficult. In these cases, what I called the ATD approach above is plainly the wiser course, compared to the TDIQ approach.
Policymakers have a role to play here. They can develop guidance for DRP teams to apply as they come upon these difficult situations. That guidance can help them decide whether to take the ATD approach or the TDIQ approach. The military calls this guidance “rules of engagement,” while politicians call it “guardrails.”
Deciding between the ATD and TDIQ approaches on a whim, or on what feels right at the moment, inevitably leads to a chaos of inconsistency and scope creep. The safest course is to adopt wise rules of engagement. Then adjust them as the organization learns more about retiring technical debt from its assets.
References
[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).
[Dragičević 2016] Tomislav Dragičević, Xiaonan Lu, Juan C. Vasquez, and Josep M. Guerrero. “DC Microgrids–Part II: A Review of Power Architectures, Applications and Standardization Issues,” IEEE Transactions on Power Electronics, vol 31:5, 3528-3549, 2016.
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
When we first set out to plan a large technical debt retirement project (DRP), a question arises very early in the planning process. It is this: Which assets are carrying the kind of technical debt we want to retire? And a second question is: Which operations will be affected—and when—by the debt retirement work? Although these questions are clear, and easily expressed, the answers might not be. And the answers are important. So where is the technical debt?
The challenges of identifying debt-bearing assets
Determining which of the enterprise’s many technological assets might be carrying the Technical Debt In Question (TDIQ) can be a complex exercise in itself. It’s challenging because inspecting the asset might be necessary. Inspection might require temporarily suspending operations, or determining windows of time during which inspection can be performed safely and without interfering with operations. Further, inspection might require knowledge of the asset that the DRP team doesn’t possess. Moreover, access to the asset might be restricted in some way. In these cases, staff from the unit responsible for the asset must be available to assist with the inspection.
Although asset inspection might be necessary or preferable, it might not be sufficient for determining which assets are carrying the TDIQ. This is easy to understand for physical assets. For example, physical inspection cannot determine the release version of the firmware of the hydraulic controller electronics of a tunnel boring machine. But asset inspection might also be insufficient for purely software assets. For determining the presence of the TDIQ in software assets, reading source code might not be sufficient or efficient.
To locate the technical debt, it might be easier, faster, and more accurate to operate the asset under special conditions. For example, an inspector might want to provide specific inputs to various assets and then examine their responses. As a second example, we might use automation assistance to examine the internal structure of an asset, searching for instances of the TDIQ. And as with other assets, the assistance of the staff of the business unit responsible for the asset might be necessary for the inspection.
Which enterprise operations depend on debt-bearing assets?
Knowing which assets bear the TDIQ is useful to the DRP team as it plans the work to retire the TDIQ. But part of that plan could include service disruptions. If so, it’s also necessary to determine how those disruptions might affect operations. That information enables the team to control the effects of the disruptions and negotiate with affected parties. Thus for each asset that bears the TDIQ, we must determine what operations would be affected by service suspension.
Observations of actual operations in conditions in which the asset is out of service in whole or in part can be valuable. Such observations might be the only economical way to discover which enterprise functions depend on the assets that carry the TDIQ. Other techniques include examining historical data such as trouble reports and outstanding defect lists, and correlating them across multiple asset histories and operations histories.
Last words
In some cases, these investigations produce results that have a limited validity lifetime, or “shelf life.” The short shelf life is mainly due to ongoing evolution of the debt-bearing assets and the assets that interact with them. That’s why the work of retiring the TDIQ must begin as soon as possible after the inventory is complete. This suggests that the size of the DRP team is a critical success factor. Larger size teams can complete the inventory inspections rapidly. Speed is important because of the validity lifetime of the team’s research results.
Managing teams of great size is a notoriously difficult problem. One approach that can help involves delegating some of the DRP research effort. The people most qualified for this work are in the business units that own the assets in question. Properly motivated, they can provide the labor hours and expertise needed for the research. In this way, the DRP can deploy a team-of-teams structure, known as a Multi-Team System (MTS) [Mathieu 2001] [Marks 2005]. The DRP team can then bring to bear a large force in a way that renders the overall MTS manageable.
References
[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).
[Dragičević 2016] Tomislav Dragičević, Xiaonan Lu, Juan C. Vasquez, and Josep M. Guerrero. “DC Microgrids–Part II: A Review of Power Architectures, Applications and Standardization Issues,” IEEE Transactions on Power Electronics, vol 31:5, 3528-3549, 2016.
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.
[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
A map of the U.S. Interstate Highway System. The map shows primary roadways, omitting most of the urban loop and spur roads that are actually part of the system. In 2016, the total length of highways in the system was about 50,000 miles (about 80,000 km). About 25% of all vehicle miles the U.S. occur in this system. The cost to build it was about USD 500 billion in 2016 currency. Significant advances have occurred since the 1950s in technologies such as rail, electronics, data management, and artificial intelligence. And the effects of fossil fuel combustion on global climate are well known. One wonders whether such a system would be the right choice if construction were to begin today. If alternatives would be better, then this system might be regarded as technical debt. But replacing it might not be practical. Finding a way to retire the technical debt without replacing the entire asset might be the most viable solution. Image by SPUI courtesy Wikipedia.
Designing a project to retire some portion of the technical debt from a critical, irreplaceable asset, can be a daunting task. It’s best to acknowledge that the project design problem is very likely a wicked problem in the sense of Rittel and Webber [Rittel 1973]. See my post “Retiring technical debt can be a wicked problem” for more. In this thread, of which this is the first post, I suggest some basic preparations for dealing with irreplaceable assets. They form a necessary foundation for success in approaching the debt retirement problem for irreplaceable assets.
Wicked problems
As I’ve noted in previous posts, the problems associated with retiring technical debt can be wicked problems. And if some of these problems aren’t strictly wicked problems, they can possess many of the attributes of wicked problems in degrees sufficient to challenge the best of us. That’s why approaching a technical debt retirement project as you would any other project is risky.
For convenience and to avoid confusion, in my last post I adopted the following terminology:
DRP is the Debt Retirement Project
DDRP is the effort to design the DRP
DBA is the set of Debt Bearing Assets undergoing modification in the context of the DRP
IA is the set of assets, excluding the DBA assets, that interact directly or indirectly with assets in the DBA
In the posts in this thread, convenience demands that we add at least one more shorthand term:
TDIQ is the Technical Debt In Question. That is, it’s the kind of technical debt we’re trying to retire from the DBA assets. Other instances of the TDIQ might also be found elsewhere, in other assets, but retiring those instances of the TDIQ is beyond the scope of the DRP.
Know when and why we must retire technical debt
For those technical debt retirement projects (DRPs) that exhibit a high degree of wickedness, a critical success factor is clear communication of the mission of the DRP. Clear communication is important because the DRP team must deal with many stakeholders who are in the early stages of familiarity with the concept of technical debt. Some of them might be cooperating reluctantly. Expressing the objectives and benefits of the DRP in a clear and inspiring way is very helpful. With that in mind, I offer the following reminder of the reasons for tackling such a large and risky project that produces so few results immediately visible to customers.
Examining alternatives to retiring the TDIQ is a good place to begin. One alternative is simply letting the TDIQ remain in place. Call this alternative “Do Nothing.” A second alternative to retiring the TDIQ is replacing the debt-bearing asset with something fresh and clean and debt-free. Call this alternative “Replace the Asset.” The problem many organizations face is that they cannot always rely on these alternatives. And because these two alternatives to debt retirement aren’t always practical, some organizations must develop the expertise and assets necessary to retire widespread technical debt in large, critical, irreplaceable systems. Below is a high-level discussion of these two alternatives to debt retirement.
Do Nothing
The first alternative is to find ways to accept that the DBA will continue to operate in their current condition, carrying the technical debt that they now bear. This alternative might be acceptable for some assets, including those that are relatively static and which need no further enhancement or extension. This category also includes those assets the organization can afford to live without.
One disadvantage of the “Do Nothing” approach is that technology moves rapidly. What seems acceptable today might not be acceptable in the very near future. It might become old-fashioned, behind the times, or non-compliant with future laws or regulations. Styles, fashions, technologies, laws, regulations, markets, and customer expectations all change rapidly. And even if the asset doesn’t change what it does, the organization might need to enhance the asset. The enhancements might become very expensive to accomplish due to the technical debt the asset carries.
An especially troubling scenario takes shape when the DBA contains portions that are severely out of date. When that happens the organization might no longer be able to find qualified candidates who can perform needed work on the DBA. This situation can also arise when portions of the DBA were developed in-house. In that case, there might not be any qualified candidates outside the organization. When everyone who understands the DBA has departed the organization, work can proceed only if the DBA is properly documented and a training and mentoring program is healthy and current.
For these reasons, Do Nothing can be a high-risk strategy.
Replace the Asset
The second alternative to retiring the TDIQ is to replace the entire asset. For this option, the question of affordability arises. In some instances this alternative is practical, but for many assets, the organization simply cannot afford to purchase or design and construct replacements.
Pay special attention to those assets that “learn.” They might contain data gathered from experience over a long period of time. Retiring the asset can require developing some means of recovering the experience data and migrating it to the replacement asset. That task is a potentially daunting effort in itself.
Replacement is especially problematic when the asset is proprietary. If the organization created the asset itself, they might have constructed it over an extended period of time. Replacement with commercial products could require extensive adaptation of those products, or adaptation of organizational processes. Worse yet, replacement with assets of its own making will likely be costly.
Last words
When organizations depend on assets that they must enhance or extend, and which they cannot afford to replace, they face a daunting problem. They must develop the expertise and resources needed to address the technical debt that such assets inevitably accumulate.
This series of posts explores the issues that arise when an organization undertakes to retire the technical debt that its irreplaceable assets are carrying. Below, I’ll be inserting links to the subsequent posts in this series.
[Dragičević 2016] Tomislav Dragičević, Xiaonan Lu, Juan C. Vasquez, and Josep M. Guerrero. “DC Microgrids–Part II: A Review of Power Architectures, Applications and Standardization Issues,” IEEE Transactions on Power Electronics, vol 31:5, 3528-3549, 2016.
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.
[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
The interchange between Interstate 35 and U.S. Route 30, just outside Ames, Iowa. The new flyover ramp is indicated in red. It replaces the cloverleaf ramp in the upper right quadrant of the cloverleaf. A construction error has forced a delay, while the piers of the flyover ramp bridge are corrected. The cloverleaf, designed with curves a bit too tight, was a high-accident area. It constituted a technical debt, with the ongoing vehicle accidents comprising the metaphorical interest charges. The construction error in the flyover ramp piers necessitated a rollback. Rollbacks are one of the indicators of wickedness in the projects to design technical debt retirement projects. But in this case, the indicated wickedness isn’t much greater than was anticipated by the project designers. Construction drawing by Iowa Department of Transportation [Iowa DOT 2016].
Several properties of the problem of designing technical debt retirement projects tend to make those design problems more likely to be wicked problems. These properties make these projects more likely to satisfy all ten of the criteria of Rittel and Webber [Rittel 1973]. I call these properties indicators of wickedness.
We usually have some notion of the degree of wickedness of a given design effort for a technical debt retirement project. But actually executing the debt retirement project can reveal unanticipated issues and complexity. Some of what’s revealed can cause us to adjust our estimate of the degree of wickedness of the design effort. If we know in advance what kinds of revelations are most likely to cause such adjustments, we can reduce the incidence of unanticipated revelations.
Criteria for wickedness of wicked problems
In my post, “Degrees of wickedness,” I noted that we can regard all problems as lying on a Tame/Wicked spectrum, with wicked problems lying at the extreme Wicked end of the spectrum, and the tamest of the tame lying at the opposite end. As for the ten criteria of wickedness developed by Rittel and Webber, I proposed that they could be satisfied in degrees, with the most wicked problems satisfying all ten criteria absolutely.
As a quick review, here are the attributes of wicked problems 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
Conditions or situations that tend to increase wickedness
Below is a sample of conditions or situations that tend to increase the wickedness of the problem of designing a technical debt retirement project. I have no data to support these conjectured effects. But the principles I used to generate them are three. If a condition tends to…
…expand the set of stakeholders in a debt retirement project, it tends to enhance the wickedness of the design problem.
…increase the number or heterogeneity of the assets or processes that we must consider, it tends to enhance the wickedness of the design problem.
…create a need for a rollback of work performed as part of the debt retirement project, and that rollback creates a need to redesign the debt retirement project, it tends to enhance the wickedness of the design problem.
In what follows, I use the term “DRP” to indicate the Debt Retirement Project itself. The effort to design the DRP is the “DDRP.” The problem whose wickedness we’re considering isn’t the DRP itself. Rather, it is the DDRP. Also, let DBA (for debt-bearing assets) be the set of assets undergoing modification in the context of the DRP. And let IA (for interacting assets) be the set of assets, excluding the DBA assets, that interact directly or indirectly with the DBA assets.
With all this in mind, I offer the following nine examples of indicators of wickedness of the DDRP.
1. A previous attempt to retire this debt was abandoned
Two indicators of the wickedness of the DDRP are perhaps most significant. The first is the failure of a previous attempt to execute a DRP with similar objectives. And the second is the failure of a previous attempt to execute a DDRP for a DRP with those objectives. There are two reasons why such failures are significant indicators of wickedness.
First, it’s reasonable to assume that these previous attempts weren’t founded on any recognition of the wickedness of the DRP or the DDRP. Few such efforts are. (A Google search for the two phrases “technical debt” and “wicked problem” yields less than 1000 results) (update 12 Nov 2018: 1160 results; 24 May 2021: 246,000 results) Consider first the DDRP. If it is a wicked problem, proceeding as if it were not would very likely fail. If the designers of the previous DDRP did assume that it was a wicked problem, investigating their approach could prove invaluable, and save much time and effort. An analogous argument applies for the DRP itself.
Second, if the previous attempt to execute a DRP with similar objectives has left traces of itself in the DBA, and if those traces must be taken into account while executing the DDRP, they might complicate the DRP, and they might be incompletely addressed in the DDRP. To the extent that these conditions prevail, Criterion 5 is satisfied, and the DDRP exhibits wickedness.
2. The Debt Retirement Project (DRP) will interrupt some revenue streams
If the work of the DRP entails temporary interruption of revenue streams, executing the DRP can have significant and long lasting effects on the organization. In estimating the cost of the DRP, it’s clearly necessary to account for the financial impact of any revenue shifted into the future, and any revenue irretrievably lost as well. And in some cases, market share might also suffer. All of these factors tend to increase the wickedness of the DDRP.
When these effects are expected, political opposition to the DRP can develop. Senior management can prevent this opposition from halting the DDRP inappropriately by requiring that the business case for the DRP include these financial factors and demonstrate clearly the need to proceed despite them. For example, including these factors might entail adjusting revenue targets downward to account for the interruptions due to the DRP. Involving potential political opponents of the DRP in business case development can be an effective means of ensuring the strength of the business case.
The ability to model all these financial effects is an important organizational asset that can be developed and maintained, for deployment across multiple DDRPs. The organization can monitor DRPs, gathering actual experience data for comparison to the effects projected in the respective business cases of the DRPs. Those comparisons are useful for enhancing the modeling capability.
3. We need to re-certify some assets the DRP doesn’t directly touch
A DDRP is more likely to be a wicked problem if, as a result of the changes executed in the DRP, any of the assets in IA need to be re-tested after or during DRP execution. The need to re-test any assets in IA typically arises when one of two conditions occurs. One condition occurs when there’s some risk that the DRP’s changes in the DBA could somehow affect the performance of the assets in IA. The second is when the consequences of such a risk event are severe.
Five ways this need to re-certify increases wickedness
This scenario enhances the wickedness of the DDRP for at least five possible reasons.
Baseline testing of IA is necessary to enable the DRP team to recognize the effects of the DRP on IA behavior. But this baseline testing can reveal pre-existing and unaddressed faults. Leaving those faults in place can seriously complicate interpreting anomalies that appear in IA assets after DRP work has begun. That’s why the DDRP team might insist that the owners of the IA assets in question address some of these faults. With regard to these issues, political differences between the DDRP team and the owners of IA assets are possible. The additional testing of IA assets can…
…expand dramatically the set of stakeholders affected by the DRP, to include the owners, users, and maintainers of the IA assets.
…increase the need to interrupt revenue streams temporarily, and increase the number, duration, and frequency of such interruptions.
…require expertise and staffing beyond the DRP project team, which can disrupt other elements of the organization as the people needed are temporarily assigned to IA testing.
…reveal unanticipated consequences of the DRP alterations, which can trigger re-planning or redesign of the DRP during its execution. That re-planning or redesign, in turn, can trigger alterations in the DDRP.
The need to re-test assets not directly touched in the DRP is more likely when the DRP alters the external behavior of any of the DBA assets. The goal of many DRPs is improvement of the internals of assets without altering their external behavior, except possibly for performance improvements. This goal is desirable. It limits the need for re-testing and re-certification of IA assets.
Two classes of debt affect re-testing and re-certifying
The need to re-test and re-certify IA assets distinguishes two classes of debt in the DBA assets. Externally detectable debt in the DBA assets is debt that can be detected in the externals of the DBA assets. It includes their architecture, behavior, appearance, or interfaces. Externally undetectable debt in the DBA assets is any other debt not facially evident in the DBA assets. Retiring externally undetectable debt from the DBA assets is relatively straightforward. Only the DBA assets require re-testing and re-certification. Retiring externally detectable debt from the DBA assets is inherently more difficult and riskier. It requires more extensive re-testing and re-certification of both DBA and IA assets.
4. The DRP directly touches multiple sites
DRPs that entail modifying technological assets of geographically dispersed organizations tend to be more wicked. This comes about because of factors including the following:
Sites might be geographically dispersed. But they might also be separated by language boundaries, legal jurisdictions, cultural divides, time zones, financial reporting practices, and much more. The required work of actually retiring the debt can vary from site to site for both technical and nontechnical reasons.
The multiple sites might have different landlords, with different lease agreements governing the organization’s occupancy of the property. This is just one of many factors that increase the numbers of stakeholders involved. It also exacerbates their heterogeneity. And the leases might constrain the kind of work that is permissible according to the day of the week or time of day.
If local vendors provide services such as communications or Internet connections to some of the sites, the DRP can be more complicated. If the work of the DRP involves these technologies and the local vendors, the task of coordinating all the different players can be complex and can encounter unanticipated obstacles.
Examples of unanticipated obstacles
For example, consider a case in which the work of the DRP involves networking hardware and software. That is work that we might prefer to perform at night or on a weekend, when it is less disruptive for users. For a global enterprise, there might not be a suitable time of day for such work.
As a second example, consider a network upgrade for retail branch offices of a global bank. If that upgrade requires trenching for new cable connections, the project design must take into account local regulations governing the trenches. Factors to consider include the permitting process and trench requirements. Trench requirements include specifications for filling, covering, and marking while still open. These regulations vary with national and sometimes local jurisdiction. The complexity causes most organizations to rely on local vendors. But even then, the vendor selection process must include reliable vendor assessment and evaluation. Scheduling becomes a complex and risky endeavor.
For these reasons, a DDRP that involves technological assets housed at multiple geographically dispersed sites has an elevated probability of exhibiting the properties of a wicked problem.
5. Government agencies and/or industrial standards organizations must re-certify assets
Another driver of stakeholder expansion is the need for re-certifying assets after the DRP has modified them. The certification agencies can range from local and municipal regulators to national regulators and pan-industrial standards organizations. The number of possible agencies itself contributes to increased wickedness. But the operating style of these organizations merits special notice.
Many of these agencies operate without competitors. Perhaps for this reason, “customer service” might not be their strength. Gaining timely cooperation from them might be a challenging undertaking. Even though re-certification might be a small part of the DRP, it can become a blocking obstacle. Researching these requirements and their associated lead times, and maintaining a current knowledge base about them, can be an important task of the DDRP.
To acquire experience and information about their performance, consider using a pilot approach. Try to gain certification for an asset similar to the target of the DRP.
6. Nontechnical stakeholders must change their behavior
Generally, people don’t like to change how they work. There are exceptions, of course, if they recognize a benefit that arrives in some direct way. But unless there is a direct benefit, requiring people to change how they work as part of a DRP is likely to increase the wickedness of the DDRP. And the difficulty is more problematic if the people affected are technically unsophisticated. They’re less likely to appreciate the value of managing technical debt, and less likely to accept explanations of that value.
DDRP wickedness increases in this case for another reason. In addition to retiring the technical debt, the DRP must address the tasks of motivating and training the affected population. That requires preparing materials, scheduling and accounting for the time spent in training, and monitoring training effectiveness. The business case must also address these issues. It must also provide the evidence required to defuse any political opposition that might otherwise develop.
7. Major unanticipated complexity triggers redesign of the DRP
Unanticipated complexity happens in almost every project of almost any kind. But for DDRPs, unanticipated complexity that triggers adjustment of the DRP is especially unpleasant. Such a discovery can mean that the DBA assets or their connections to the IA assets have changed since the design team devised the plan. Or it can mean that the design team had an incomplete or incorrect understanding of the problem at hand. These events can occur for a number of reasons.
Examples of nontechnical causes
Imagining technical causes might be easier. So I’ll focus on nontechnical causes, which can actually be more serious. For example, suppose a political alliance enabled the VP of Sales and the VP of Engineering to reach a deal. They agreed that the DRP team would work on some important DBA assets, taking them off line for defined periods. If that political alliance weakens, or if the deal between the two VPs collapses for other reasons, the scheduled downtime of those assets might vanish. This pattern is more likely to arise in situations in which the DDRP team isn’t a party to such agreements. The DDRP team must be a party to any agreements regarding access to assets by the DRP team.
As a second example, consider what happens when the enterprise undertakes an acquisition of another enterprise. And suppose the acquisition team doesn’t inform the DDRP team during their design effort. Because chances are good that the DDRP would have a significant amount of rework to do for the acquisition, this scenario is illustrates a problem. The DDRP team must be aware of any organizational changes that could affect the DRP, for the active life of the DRP.
Redesigning the DDRP can take time. The DDRP team must periodically revisit elements of the DDRP that have short shelf lives during the design period. And the need to redesign can also indicate gaps in the DDRP team’s understanding of the problem.
All of these conditions tend to move the DDRP in the direction of increased wickedness.
8. The DRP requires weekend or middle-of-the-night work periods
The need to perform critical operations on weekends or in nighttime hours suggests three things. First, the work is risky in the sense that undetected faults that go into production can lead to costly operational errors. Second, the organization lacks a simulated operating environment that emulates the actual operating environment faithfully enough to enable defect detection before deployment. Such environments are also known as staging environments. Third, and finally, the organization lacks a rapid rollback mechanism that can restore the original state of an asset if the new modified state proves problematic when deployed.
If you anticipate multiple DRPs,before undertaking a DRP, it’s wise to construct a staging environment and devise a rollback mechanism. Cost is usually the blocking issue. But compare that cost to the cost of retarding all future DRPs, and the cost of any operational failures arising from deploying faulty systems. Staging and rollback capabilities are usually good investments.
Continued refusal to provide a staging environment with rapid rollback increases the wickedness of this and any future DDRPs.
9. Rollback of attempted changes triggers redesign
In the course of executing the DRP, if reverting some (or all) of the work performed becomes necessary, we say that we’re ordering a rollback. Minor rollbacks do happen. But if we discover the need for a rollback long after completion of the work in question, the damage can be catastrophic. When these incidents occur, they can indicate a deep misunderstanding of the consequences of the work of the DRP. Because that misunderstanding could have consequences not yet recognized, such a rollback could suggest that the DDRP team underestimated the wickedness of the DDRP.
Let DBAf (faulty DBA) be the set of assets in the DBA that formerly contained some of the debt being retired. And suppose the DRP alterations contained or led to exposure of some kind of fault(s). Suppose further that the faults forced a rollback after they were deployed. Let DBAfw (wicked-faulty DBA) represent the subset of DBAf for which that rollback did trigger a redesign of the DDRP. Then wickedness of the DDRP is correlated with the size of DBAfw and the extent of the DDRP redesign that the rollback triggered.
An illustration of the effect of defects
For example, let Efw be a member of DBAfw. And suppose Efw is a modular element of a system that monitors the clicks of users of a Web site. It records data for later analysis, and because of the fault it does so incorrectly. When the site operators discover the errors, the DRP orders the rollback of Efw. They replace Efw with its original, unaltered, debt-bearing form. Because Efw contaminated the original database, data rollback is impossible. The site operators did discover the error, but they can’t re-capture lost data. That’s why the DDRP team must re-design the DRP. This scenario is an example of Criterion 5. If there are political consequences for the loss of data, this scenario could be an example of Criterion 10.
This example suggests how the frequency of incidents that trigger redesign of the DDRP can be an indicator of the wickedness of the DDRP.
Example of a non-indicator: the I-35 SR-30 interchange near Ames, Iowa
Just outside Ames, Iowa, is an interchange between Interstate 35 (a four-lane, divided, limited-access roadway) and U.S. Route 30 (four-lane, divided, not limited-access). The interchange is a conventional cloverleaf design. The “leaves” are rather tight, though, and consequently, there have been numerous rollovers and crashes at this interchange. We can regard these tight cloverleaf ramps as technical debt in the highway system, and the rollovers and crashes as metaphorical interest charges on that debt.
In 2016, construction began on a new “flyover” exit ramp from northbound Interstate 35 onto westbound U.S. Route 30. The objective was to reduce the number of accidents at the interchange by replacing the current tight-curvature cloverleaf ramp with a flyover exit ramp with a longer radius of curvature. We can regard this project as a Debt Retirement Project (DRP). The project that planned that DRP was an effort to Design a Debt Retirement Project (DDRP).
Much went well, but an error occurred
Completion of the DRP was scheduled for November 2018. When completed, the new ramp will replace the northeast leaf of the cloverleaf. Like most civil engineering projects, this project does have some elements of wickedness. But the project dealt with those elements effectively. Nevertheless, a construction error is delaying completion [Magel 2018] [Iowa DOT 2018].
The error involves the height and position of the bolt anchors where steel bridge beams will connect to the concrete piers of the new flyover ramp. The contractor constructed six piers to support the flyover. Correcting the piers involves jackhammering the concrete tops, leaving the steel reinforcement in place. After positioning the beam anchors correctly, and re-pouring the concrete, the piers will be ready to support the beams. At this writing, the contractor has not yet announced the new completion date.
How they’re correcting the error
This effort, which includes a rollback and re-deployment, is a significant project in itself. It requires scheduling the work. But it also requires scheduling highway lane closures and lane shifts, and working around high-volume traffic periods. Depending on the schedule, they will possibly pour concrete in winter conditions. And after correcting the piers, the bridge beam placement and bridge roadbed work must proceed on a new schedule.
Consequently, the construction error triggered a redesign of the flyover project’s DRP. But it probably did not trigger a significant redesign of the DDRP. The construction error is therefore unlikely to be an indicator of significant additional wickedness for the DDRP.
Last words
You can become better managers of the risk of unanticipated wickedness. If your organization is embarking upon a long-term program of technical debt retirement, you’ll be executing many DDRPs and DRPs. Gathering data about incidents of unanticipated wickedness in DDRPs can be a useful practice, if you use that data when you design new technical debt retirement projects.
References
[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).
[Dragičević 2016] Tomislav Dragičević, Xiaonan Lu, Juan C. Vasquez, and Josep M. Guerrero. “DC Microgrids–Part II: A Review of Power Architectures, Applications and Standardization Issues,” IEEE Transactions on Power Electronics, vol 31:5, 3528-3549, 2016.
[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).
[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.
[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.
[Iowa DOT 2018] “Work Continues on the Northbound I-35 Flyover Ramp at U.S. 30 Near Ames,” Iowa Department of Transportation News Release, June 27, 2018.
[Marks 2005] Michelle A. Marks, Leslie A. DeChurch, John E. Mathieu, Frederick J. Panzer, and Alexander Alonso. “Teamwork in multiteam systems,” Journal of Applied Psychology 90:5, 964-971, 2005.
[Mathieu 2001] John E. Mathieu, Michelle A. Marks and Stephen J. Zaccaro. “Multi-team systems”, in Neil Anderson, Deniz S. Ones, Handan Kepir Sinangil, and Chockalingam Viswesvaran, eds., Handbook of Industrial, Work, and Organizational Psychology Volume 2: Organizational Psychology, London: Sage Publications, 2001, 289–313.
[Thorndike 1920] Edward L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4:1, 25-29, 1920. doi:10.1037/h0071663
The first report of the halo effect. Thorndike found unexpected correlations between the ratings of various attributes of soldiers given by their commanding officers. Although the halo effect was thus defined only for rating personal attributes, it has since been observed in assessing the attributes of other entities, such as brands. Available: here; Retrieved: December 29, 2017
