Controlling incremental technical debt

Last updated on January 20th, 2019 at 01:54 pm

All technical debt in enterprise assets is either incremental technical debt or legacy technical debt.  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.

A sinking rowboat
A sinking rowboat provides a useful metaphor for illustrating the effects of 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; water entering the boat through leaks is incremental technical debt; the accumulated water in the bottom of the boat is legacy technical debt.

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 at least some of the water is a good idea, and it might be necessary in the short term. But at some point, fixing the leaks where the water comes in is advisable. Unless you address the leaks that already exist, and prevent new ones from forming as the rowboat ages, your fate is sealed. You’ll spend increasing portions of your time, energy, and resources bailing out your leaky rowboat, and 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, because the boat is lower in the water, and because you must propel not only the mass of 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.

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 products, a somewhat less preferable—but still practical—approach involves re-assembling some of the team to retire the incremental technical debt as soon as possible, while memories are still fresh. Other approaches might be needed for incremental exogenous technical debt.

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. Much technical debt arises either as a natural result of working with technology, or as a result of organizational forms that compel people to behave in ways that lead to generating technical debt. Unless the people of the organization accept these truths, allocating sufficient resources to managing incremental technical debt is unlikely.

Not appreciating the MICs

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, as noted, 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. If the decision-maker’s understanding of the technical debt concept is deficient, or if the calculations of MICs now or in the future are incomplete or underestimated, the investment decision is likely to be biased in favor investing in new capabilities. Incremental technical debt retirement is thereby systematically deferred or avoided altogether.

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 all such people understand how to change their behavior, whether they’re acting individually or in collaboration with others. Any guiding principle offered to them must be simple to state and easy to understand, because we must communicate it to nearly everyone in the enterprise. Here’s a sample statement of a useful such principle:

Those whose decisions cause the enterprise to incur technical debt are accountable for securing the resources needed to retire that debt, and for supplying compensating resources to those within the enterprise, or among its customers, who 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 of 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.

Examples: the definitions of “done” vis-à-vis projects, strategic technical debt, reckless technical debt, unethical technical debt, exogenous-technical-debt, endogenous technical debt, MICs, MPrin, and more. An enterprise-wide education program, including on-line reference material and new-employee orientation components, are probably also necessary.

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 “Non-technical precursors of non-strategic 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 kinds of incremental technical debt that arise 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 for debt retirement costs (MPrin) and metaphorical interest charges (MICs). For example, in Fowler’s terms, a Reckless/Inadvertent incident could carry a rating that would lead to imposing higher assessed charges for the debt originators than would a Prudent/Deliberate incident, the charges for which might be zero.

5.       Require that deliberately incurred technical debt be secured

In the financial realm, secured debt is debt for which repayment is guaranteed by specifically pledged assets. By analogy, 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 in a development or maintenance effort feels that technical debt has been incurred, a dispassionate third party, unaligned with any function involved in the effort, reviews project deliverables for the presence of technical debt. However, allocating future resources might require securing commitments of resources for fiscal periods beyond the current one. 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

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

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The resilience error and technical debt

Last updated on October 4th, 2018 at 02:09 pm

I’ve mentioned the reification error in a previous post (see “Metrics for technical debt management: the basics”), but I haven’t explored its dual, the resilience error. Let me correct that oversight now.

The future USS Zumwalt (DDG 1000) in sea trials, December 2015
The future USS Zumwalt (DDG 1000) is underway for the first time conducting at-sea tests and trials in the Atlantic Ocean Dec. 7, 2015. The first of the Zumwalt class of US Navy guided missile destroyers, it is designed to be stealthy, and to be supported by a minimal crew. After the program experienced explosive cost growth, the class has been downsized from 32 ships to three, and complement increased from 95 to over 140 to reduce capital costs. The three vessels on order now have significantly reduced missions. As one might expect, the causes of these troubles are much debated. But it’s possible that the resilience error plays a role. Before the first of a new class of ships goes to sea, it exists as an abstraction—a collection of concepts, plans, promises, and technologies, tried and untried. Many elements of this collection have never inter-operated with other elements. The first ship represents the first opportunity to see how all the elements work together. Although troubles often appear even before the ship is fully assembled, anticipating all troubles is extraordinarily difficult.

Reification risk is the risk that an error of reasoning known as the reification error might affect decisions—in this case, decisions regarding technical debt. The reification error [Levy 2009] [Gould 1996] (also called the reification fallacy, concretism, or the fallacy of misplaced concreteness [Whitehead 1948]) is an error of reasoning in which we treat an abstraction as if it were a real, concrete, physical thing. Reification is useful in some applications, such as object-oriented programming and design.

But when we reify in the domain of logical reasoning, troubles can arise. For example, we can encounter trouble when we think of “measuring” technical debt. Strictly speaking, we cannot measure technical debt. It isn’t a real, physical thing that can be measured. What we can do is estimate the cost of retiring technical debt, but estimates are only approximations. And in the case of technical debt, the approximations are usually fairly rough—they have wide uncertainty bands. That’s one way for trouble to enter the scene. When we regard the estimate as if it were a measurement, we tend to think of it as more certain than it actually is. Technical debt retirement projects then overrun their budgets and schedules, and chaos reigns.

For example, if we think we’ve measured the MPrin of a class of technical debt, rather than that we’ve estimated it, we’re more likely to believe that one measurement will suffice, and that it will be valid for a long time (or indefinitely). On the other hand, if we think we’ve estimated the MPrin of a class of technical debt, we’re more likely to believe that obtaining a second independent estimate would be wise, and that the estimate we do have might not be valid for long. These are just some of the consequences of the reification error.

The resilience error

If the reification error is risky because it entails regarding an abstraction as a real, physical thing, we might postulate the existence of a resilience error that’s risky because it entails regarding an abstraction as more resilient, pliable, adaptable, or extensible than it actually is.

When we commit the resilience error with respect to an abstraction, we adopt the belief—usually without justification, and possibly outside our awareness—that if we make changes in the abstraction without fully investigating the consequences of those changes, we can be certain that the familiar properties of the abstraction we modified will apply, suitably modified, to the new form of the abstraction.  Or we assume incorrectly that the abstraction will accommodate any changes we make to its environment.

Sometimes we benefit when we modify abstractions; usually we encounter unintended and unpleasant consequences. For example, unless we examine our modifications carefully, it’s possible that the implications of a modification might conflict with one or more of the fundamental assumptions of the abstraction.

Examples of the resilience error

Perhaps a (ahem) concrete example will illustrate. Consider the steel hull of an ocean liner. We can manufacture it more cheaply if we can devise a way to use less steel. So one approach to that goal is to remove a small portion of the bottom of the hull, say, a circular hole one meter in diameter. We send some people into the ship to do the work, and they return with panicky reports of water coming in. But the ship seems fine, so we reject the reports. Even a day later, all seems well. But by the end of the second day, the trouble is obvious. The ship is sinking.

The problem in our example is that the circular hole in the hull violated a fundamental assumption about how ship hulls work: they work by keeping all water out of the ship. We had extended the idea of hull to make it lighter, but in doing so, we encountered some unintended consequences because our extension violated a fundamental property of hulls.

Now for a less fanciful example.

Consider the fictitious company Alpha Properties LLC, which manages small condominium associations (from 25 to 100 units). Things have been going swimmingly at Alpha Properties, and they’ve decided to expand to handle large condominium associations. Their financial accounting software  has worked well, and their employees have become quite expert in its use. Alpha management has heard good reports from other management companies that deal with large client associations. So Alpha decides to use the same software for its larger accounts too. But things don’t work out so well.

The software is fine, but the processes used by the staff are cumbersome and slow. For example, setting up a new association requires too much manual data entry. For a 100-unit association, client setup wasn’t a burden, but for a 900-unit association the problem is just unmanageable.

This is a fine example of the resilience error. When we make this error, we fail to appreciate how an abstraction can encapsulate assumptions that make for difficulties when we try to extend it or apply it in a new or altered context. In this example, Alpha’s data flow processes are the abstraction. The context is signing up a new client association. When the context (signing up a large new client) is different, it violates an internal assumption of the abstraction (the data flow process for signing up a new client).

How the resilience error leads to technical debt

In many cases, the resilience error is at the heart of the causes of technical debt. It works like this. We have an asset that works perfectly well for one set of applications or in one set of contexts. We want to apply that asset in a new way, which might (or might not) require some minor extensions. When we try it, we find that the asset incorporates some assumptions about the application or the context, and one or more of those assumptions are violated by the new application or the new context. Scrambling, we find some quick fixes that can get things working again, but those fixes usually aren’t well designed or easily maintained. The result is a trail of technical debt.

Acquiring companies is like that. Before the acquisition, we think we’ll be able to merge the IT operations to save some expenses in operations. When we actually try it, though, merging them proves to be far more expensive than we imagined. Ah, the resilience error.

What makes this situation so difficult is that often we’re unable to anticipate what assumptions we might be about to violate. That’s why we make the resilience error.

Spotting difficulties with adapting to new applications and new contexts isn’t so difficult with physical entities. For example, we can see in advance that a square peg won’t fit into a round hole. But with abstractions, we can’t always see the problems in advance. Piloting, prototypes, games, and simulations can help us avoid some trouble, but not all.

References

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

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Accounting for technical debt

Last updated on September 16th, 2018 at 03:28 pm

With all the talk of technical debt these days, it’s a bit puzzling why there’s so little talk in the financial community about how to go about accounting for technical debt [Conroy 2012]. Perhaps one reason for this is the social gulf that exists between the financial community and the community most keenly aware of the effects of technical debt — the technologists. But another possibility is the variety of mechanisms compelling technologists to leave technical debt in place and move on to other tasks.

Accounting for technical debt isn’t the same as measuring it
Accounting for technical debt isn’t the same as measuring it. We usually regard our accounting system as a way of measuring and tracking the financial attributes of the enterprise. As such, we think of those financial attributes as representations of money. Technical debt is different. It isn’t real, and it isn’t a representation of money — it’s a representation of resources. Money is just one of those resources. Money is required to retire technical debt, and money is consumed when we carry technical debt, but other kinds of resources are also required. Sometimes we forget that when we account for technical debt.

Here’s an example. One common form of technical debt is the kind first described by Cunningham [Cunningham 1992]. Essentially, when we complete a project, we often find that we’ve advanced our understanding of what was actually needed to accomplish our goals. And we’ve advanced our understanding to such an extent that we recognize that we should have taken a different approach. Fowler described this kind of technical debt as, “Now we know how we should have done it.” [Fowler 2009] At this point, typically, we disband the team and move on to other things, leaving the technical debt outstanding, and often, undocumented and soon to be forgotten.

A (potentially) lower-cost approach involves immediate retirement of the debt and a re-release of the asset — an “echo release” — in which the asset no longer carries the technical debt we just incurred and immediately retired. But because echo releases usually offer no immediate, evident advantage to the people and assets that interact with the asset in question, decision-makers have difficulty allocating resources to echo releases.

This problem is actually due, in part, to the effects of a shortcoming in management accounting systems. Most enterprise management accounting systems track effectively the immediate costs associated with technical debt retirement projects. They do a much less effective job of representing the effects of failing to execute echo releases, or failing to execute debt retirement projects in general. The probable cause of this deficiency is the distributed nature of the MICs — the metaphorical interest charges associated with carrying a particular technical debt. The MICs appear in multiple forms: lower productivity, increased time-to-market, loss of market share, elevated voluntary turnover in the ranks of technologists, and more (see “MICs on technical debt can be difficult to measure”). These phenomena are poorly represented in enterprise accounting systems.

Decision-makers then adopt the same bias that afflicts the accounting system. In their deliberations regarding resource allocation, they emphasize only the cost of debt retirement, often omitting from consideration altogether any mention of the cost of not retiring the debt, which can be ongoing.

If we do make long-term or intermediate-term projections of costs related to carrying technical debt or costs related to debt retirement releases, we do so in the context of a cost/benefit computation as part of the proposal to retire the debt. Methods vary from proposal to proposal. Many organizations lack a standard method for making these projections. And because there’s no standard method for estimating costs, comparing the benefits of different debt retirement proposals is difficult. This ambiguity and variability further encourages decision-makers to base their decisions solely on current costs, omitting consideration of projected future benefits.

Dealing with accounting for technical debt

Relative to technical debt, the accounting practice perhaps most notable for its absence is accounting for outstanding technical debts as liabilities. We do recognize outstanding financial debt as such, but few balance sheets — even those for internal use only — mention outstanding technical debt. Ignorance of the liabilities imposed by outstanding technical debt can cause decision-makers to believe that the enterprise has capacity and resources that it doesn’t actually have. Many of the problems associated with high levels of technical debt would be alleviated more readily if we began to track our technical debts as liabilities — even if we did so for internal purposes only.

But other shortcomings in accounting practices can create additional problems almost as severe.

Addressing the technical-debt-related shortcomings of accounting systems requires adopting enterprise-wide patterns for proposals, which gives decision-makers meaningful comparisons between different technical debt retirement options and between technical debt retirement options and development or maintenance options. One area merits focused and immediate attention: estimating MPrin and estimating MICs.

Standards for estimating MPrin are essential for estimating the cost of retiring technical debt. Likewise, standards for estimating MICs, at least in the short term, are essential for estimating the cost of not retiring technical debt. Because both MPrin and MICs can include contributions from almost any enterprise component, merely determining where to look for contributions to MPrin or MICs can be a complex task. So developing a checklist of potential contributions can help proposal writers develop a more complete and consistent picture of the MICs or MPrin associated with a technical debt. Below are three suggestions of broad areas worthy of close examination.

Revenue stream disruption

Technical debt can disrupt revenue streams either in the course of being retired, or when defects in production systems need attention. When those systems are taken out of production for repairs or testing, revenue capture might undergo short disruptions. Burdens of technical debt can extend those disruptions, or increase their frequency.

For example, a technical debt consisting of the absence of an automated test can lengthen a disruption while the system undergoes manual tests. A technical debt consisting of a misalignment between a testing environment and the production environment can allow defects in a repair or enhancement to slip through, creating new disruptions as those new defects get attended to. Even a short disruption of a high-volume revenue stream can be expensive.

Some associations between classes of technical debt and certain revenue streams can be discovered and defined in advance of any debt retirement effort. This knowledge is helpful in estimating the contributions to MICs or MPrin from revenue stream disruption.

Extended time-to-market

Although technologists are keenly aware of productivity effects of technical debt, these effects can be small compared to the costs of extended time-to-market. In the presence of outstanding technical debt, time-to-market expands not only as a result of productivity reduction, but also from resource shortages and resource contention. Extended time-to-market can lead to delays in realizing revenue potential, and persistent and  irreparable reductions in market share. To facilitate comparisons between different technical debt retirement proposals, estimates of these effects should follow standard patterns.

Data flow disruption

All data flow disruptions are not created equal. Some data flow processes can detect their own disruptions and backfill when necessary. For these flows, the main consequence that might contribute to MICs or MPrin is delay. And the most expensive of these are delays in receipt of orders, delays in processing orders, and delays in responding to anomalous conditions. Data flows that cannot detect disruptions are usually less critical, but they nevertheless have costs too. All of these consequences can be modeled and estimated, and we can develop standard packages for doing so that we can apply repeatedly to MICs or MPrin estimates for different kinds of technical debt.

Last words

Estimates of MICs or MPrin are helpful in estimating the costs of retiring technical debt. They’re also helpful in estimating the costs of not retiring technical debt. In either case, they’re only estimates, and as such, they have error bars and confidence limits. The accounting systems we now use have no error bars. That, too, is a shortcoming that must be addressed.

References

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Conroy 2012] Patrick Conroy. “Technical Debt: Where Are the Shareholders' Interests?,” IEEE Software, 29, 2012, p. 88.

Available: here; Retrieved: August 15, 2018.

Cited in:

[Cunningham 1992] Ward Cunningham. “The WyCash Portfolio Management System.” Addendum to the Proceedings of OOPSLA 1992. ACM, 1992.

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

Cited in:

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Metrics for technical debt management: the basics

Last updated on September 16th, 2018 at 05:13 pm

Whether it is wise to use metrics for technical debt management is an open question. But whether it will become a widespread practice does seem to be a settled question. Using metrics for technical debt management now does appear to be inevitable. So let’s explore just what we mean by “metrics,” and what traps might lie ahead when we use them for technical debt management.

Measuring tools needed to follow a recipe in the kitchen
Measuring tools needed to follow a recipe in the kitchen. The word “measurement” evokes images that relate to our earliest understanding of the word as children. For most of us, that involves determining the attributes of a physical thing. In most cases, the physical thing of most concern is our own body — its height and weight. But we also measure everyday things, as when following recipes. So the strongest associations with the word “measurement” involve physical things. “Measuring” attributes of an abstract construct like technical debt can be perilous, unless we make allowances for its lack of physicality.

Skepticism about the effectiveness of using metrics for technical debt management is reasonable, because technical debt isn’t a physically measurable thing. “Measuring” technical debt is therefor susceptible to what psychologists call the reification error [Levy 2009] or what philosophers call the Fallacy of Misplaced Concreteness [Whitehead 1948].

The logical fallacy of reification occurs when we treat an abstract construct as if it were a concrete thing. Although reification can provide helpful mental shorthand, it can produce costly cognitive errors. For example, advising someone who’s depressed to get more self-esteem is unlikely to work, because self-esteem isn’t something one can order from Amazon, or anywhere else. One can enhance self-esteem through counseling, reflection, or many other means, but it isn’t a concrete object one can “get.” Self-esteem is an abstract construct.

Technical debt is likewise an abstract construct. We can discuss “measuring” it, but attempts to specify measurement procedures will eventually confront the inherently abstract nature of technical debt, leading to debates about both definitions and the measurement process.

Metrics inherently require some kind of measurement. That’s why skepticism about using metrics for technical debt management is a reasonable position. Reasonable or not, though, metrics will be used. We’d best be prepared to use them responsibly. That’s the focus of this post — and a few to come. If we use metrics for technical debt management, how can we do it responsibly? How can we manage the risks of reification?

This post is about metrics in general. In coming posts I’ll apply this line of thinking to specific examples of metrics for managing technical debt, and suggest approaches that could mitigate reification risk.

Foundations for behavior guidance decisions

The objective of technical debt management is support for behavior guidance decisions — decisions that guide the behavior and choices of employees so as to control the volume of technical debt. Although many frameworks exist for supporting behavior guidance decisions, they generally consist of four elements:

Quantifiers

A quantifier is a specification for a measurement process designed to yield a numeric representation of some attribute of an asset or process. With respect to technical debt, we use quantifiers to prescribe how we produce data that represents the state of technical debt of an asset. We also use quantifiers to generate data that captures other related items, such as budgets, the cost or availability of human effort, revenue flows — almost anything that interacts with the assets whose debt burden we want to control.

An example of a quantifier is the process for estimating the MPrin of a particular kind of technical debt borne by an asset. The MPrin quantifier definition includes an explicit procedure for measuring it — that is, for estimating the size of the MPrin in advance of actually retiring that debt. After retirement, we know its value without estimating, because the MPrin is what we spent to complete the retirement.

Measures

A measure is the result of determining the value of a quantifier. For example, we might use a quantifier’s definition to determine how much human effort has been expended on an asset in the past fiscal quarter. Or we might use another quantifier’s definition to determine the current size of the MPrin carried by the asset.

Metrics

A metric is an arithmetic formula expressed in terms of constants and a set of measures. One of the simpler metrics consists of a single ratio of two measures. For example, the metric that captures the average cost of acquiring a new customer in the previous fiscal quarter is the ratio of two measures, namely, the investment made in acquiring new customers, and the number of new customers acquired.

Associated with some metrics is a defined set of actors (actual people) who are authorized to take steps — or who are authorized to direct others to take steps — designed to affect the value of the metric in some desirable way. Metrics that have defined sets of actors are usually Key Performance Indicators (see below). If more than one individual is a designated actor for a metric, a process is defined to resolve differences among the designated actors about what action to take, if any. In some cases, this process is as simple as determining which designated actor has the highest organizational rank.

An example of a technical debt metric is the ratio MPrin(i)/MPrin(r) = the total of incremental technical debt (MPrin(i)) incurred in the given time period divided by the total of MPrin retired (MPrin(r)) in that same time period. In periods during which this ratio exceeds 1.0, the organization is accumulating incremental technical debt faster than it is retiring technical debt. Computing it as a ratio, as opposed to a difference, has the effect of expressing the increases (or decreases) in technical debt portfolio size in units of the size of the debt retired. This enables the organization to take on some incremental technical debt responsibly.

This metric also has the virtue of displaying meaningful trends in an easily recognized way. In this case, a steady upward trend means a steadily increasing debt portfolio, even if in some time periods the debt doesn’t increase much. In other words, the ratio removes some of the “choppiness” that might plague a metric expressed in terms of absolute values.

Key Performance Indicators

A Key Performance Indicator (KPI) is a metric that provides meaningful insight that’s used to guide business decisions. All KPIs are metrics; not all metrics are KPIs.

A KPI is derived from one or more metrics. It represents how successful the business is in accomplishing a given business objective.  A metric, on the other hand, represents only the degree of success in reaching a targeted value for that metric. Because the relationship between the target value of a metric and any given business objective can be complicated, and potentially involve other metrics, metrics that aren’t KPIs are of less value in indicating the degree of success in achieving business objectives.

MPrin(i)/MPrin(r) is a metric that could also serve as a KPI, if the business objective is to achieve steady declines in overall technical debt.

Dimensions of measure vs. dimensions of metrics

Some metrics, such as MPrin(i)/MPrin(r), are dimensionless. Their values are pure numbers. And some metrics have dimensions — units of measure. For example, consider the metric MPrin(i)*Tdelay, where MPrin(i) is the volume of incremental technical debt borne by the deliverable, and Tdelay is the number of days after the target date that the project was delivered. The dimension of this metric is Currency*Days. This metric is particularly interesting, because a common assertion about technical debt is that we incur it as a means of advancing project delivery. Because the evidence for this assertion is mostly anecdotal, actually determining the value of this metric over a number of projects might reveal useful information about the effectiveness of the strategy of incurring technical debt as a means of advancing project delivery. Thus, we would expect to see small time delays associated with increased incremental technical debt. In other words, all projects of similar scale should lie along the same hyperbola in a plot of this metric in a space in which one axis is debt and the other is the number of days after the target date that the project was delivered.

Units of measures are often different from the units of the metrics that those measures support. For example, measures of technical debt in code include test coverage, documentation asynchrony, documentation omissions, code duplication, code complexity, dependency cycles, rule violations, or interface violations. The units of these measures are all different.

To those who must make strategic technical debt management decisions by comparing the costs of retiring different kinds of debt, these detailed measures are awkward to use. MPrin is more directly related to the issues they must address. MPrin provides a unit of comparison among debt retirement options, and between retirement options and available resources. Beyond the level of the particular debt being considered for retirement, MPrin is the dimension of greatest utility. [Brown 2010]

In a future post I’ll describe the properties of metrics that are needed for technical debt management.

References

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.

Available: here; Retrieved: July 30, 2018

Cited in:

[Conroy 2012] Patrick Conroy. “Technical Debt: Where Are the Shareholders' Interests?,” IEEE Software, 29, 2012, p. 88.

Available: here; Retrieved: August 15, 2018.

Cited in:

[Cunningham 1992] Ward Cunningham. “The WyCash Portfolio Management System.” Addendum to the Proceedings of OOPSLA 1992. ACM, 1992.

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

Cited in:

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Legacy debt incurred intentionally

Throughout this blog, I’ve been using the terms legacy technical debt and incremental technical debt. Legacy technical debt is debt that existed before we undertook the current project; incremental technical debt is debt we incurred in the course of executing the current project. But there is some incremental technical debt that’s actually legacy debt incurred intentionally.

The locomotive known as “The General,” in Union Station, Chattanooga, Tennessee
The locomotive known as “The General,” in Union Station, Chattanooga, Tennessee. Built in 1855 in Paterson, New Jersey, for the Western & Atlantic Railroad, it’s best known as the engine stolen by Union spies in the Great Locomotive Chase, as part of a plan to cripple the Confederate rail network during the American Civil War. The General is preserved at the Southern Museum of Civil War and Locomotive History in Kennesaw, Georgia. It was originally built to conform to the southern rail gauge of 5 ft (1,524 mm), but it was converted to the U.S. Standard Gauge of 4 ft 8 1⁄2 in (1,435 mm) after 1886. Its original construction amounted to legacy debt. If it had been built after the war, it would have comprised legacy debt incurred intentionally. Photo “The General, Union Station, Chattanooga, Tenn.,” Detroit Publishing Co., publisher, ca. 1907. Courtesy U.S. Library of Congress.
As I’ve defined incremental technical debt, it’s any debt we incur in the course of the current work. That definition works well for most incremental technical debt. For example, if we recognize at the end of the project that we should have done something a bit differently, we’ve incurred incremental technical debt. This is one of the four forms of technical debt identified by Fowler in his 2x2 technical debt matrix [Fowler 2009].

But we must be a bit more careful, because some incremental technical debt is actually legacy debt incurred intentionally.

Legacy technical debt is debt that was incurred earlier, and which we’ve inherited as part of the asset. Sometimes we’re aware of legacy technical debt; sometimes we haven’t actually realized yet that it is indeed technical debt. In any case, the technical artifacts that comprise the legacy technical debt can impose constraints on any new development. Unless we retire the legacy debt, however we modify an asset must be compatible with the assets as they are.

Sometimes technical debt can be both legacy and incremental

Although the two kinds of technical debt — legacy and incremental — might seem at first to be mutually exclusive, there’s a subset of legacy technical debt that can be incurred in the course of executing the current project.

Here’s a physical example:

After the United States Civil War, the state of the U.S. rail system was a bit chaotic. Most of the rail lines in the northeast and western regions of the country used what is called standard gauge rail beds: rails that are separated by 1,435 mm (4 ft ​8 1⁄2 in). Most of the South was using a broader gauge: 1,524 mm (5 ft). These conflicting gauges comprised a legacy technical debt. The debt was finally retired over a two-day period beginning on Monday, May 31, 1886, when all the southern railroads coordinated to convert from a 5-foot gauge to 4 feet 9 inches [Southern Railfan 1966].

In the years immediately before the legacy debt was retired, any expansion or repair of the southern rail network that was compatible with the broader gauge, which was about to be retired, would have added to — or maintained — the legacy technical debt. It would thus have comprised newly incurred technical debt that would have also added to the legacy technical debt. Thus, in some situations, some newly incurred technical debt can be legacy technical debt.

Here’s a software example:

A software development team is engaged in a project to enhance the capabilities of the Marigold product, which is one product in the Garden Flowers personal productivity suite. Unfortunately, the original architecture of the suite didn’t anticipate the course that the suite has since taken, and it now comprises legacy technical debt. However, because changing the suite architecture isn’t in the charter of the Marigold enhancement team, they’ll be creating new technical artifacts that are compatible with the current architecture, but which will someday be modified or replaced when the Garden Flowers architecture is revamped or replaced. Thus, some of the new technical debt now being incurred by the Marigold team will be added to the legacy technical debt associated with the Garden Flowers architecture.

Moreover, the Marigold team might incur other technical debt in the course of its activities, if, for example, it fails to complete its task, or completes it in some suboptimal way. In that case it will be incurring incremental technical debt that it probably should retire soon after (if not before) delivery of the Marigold enhancements. Thus, in the same project, it would be incurring both (a) purely incremental technical debt, and (b) incremental technical debt that’s also legacy technical debt.

Why legacy debt incurred intentionally matters

Any program of rational technical debt management entails measuring — or at least estimating — the volume of technical debt incurred in the course of executing each project. The goal is to limit the debt incurred, so as to get control of the total technical debt outstanding.

But with legacy technical debt, as in the example above, we can’t always control the debt we incur. In some projects, it’s necessary to incur additional legacy technical debt because the work we do must be compatible with existing assets. We want to limit incremental technical debt, but we can’t always avoid incurring incremental debt that’s also legacy debt.

This distinction is important for both policy formation and management intervention. For instance, if purely non-legacy incremental technical debt is incurred, we might want to address it immediately, or perhaps commit to addressing it immediately after delivery. Alternatively, if we can obtain good data about a particular kind of legacy technical debt that’s growing because of the need to keep new development compatible with existing debt-ridden assets, we can use that data to elevate the priority of retiring the legacy debt before it grows even larger.

So when we ask projects to report their incremental technical debt, we want them to distinguish between legacy debt incurred intentionally, and incremental debt that was incurred for reasons specific to the project. Having data about both kinds of incremental technical debt is a necessity if we want to take appropriate management action to maintain control of the technical debt portfolio.

References

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.

Available: here; Retrieved: July 30, 2018

Cited in:

[Conroy 2012] Patrick Conroy. “Technical Debt: Where Are the Shareholders' Interests?,” IEEE Software, 29, 2012, p. 88.

Available: here; Retrieved: August 15, 2018.

Cited in:

[Cunningham 1992] Ward Cunningham. “The WyCash Portfolio Management System.” Addendum to the Proceedings of OOPSLA 1992. ACM, 1992.

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Southern Railfan 1966] Southern Railfan. “The Days They Changed the Gauge,” 1966.

Available: here; Retrieved: July 26, 2018.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

Cited in:

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The Principal Principle: Focus on MICs

Last updated on December 11th, 2018 at 06:56 am

When some organizations first realize that their performance is being limited by technical debt, they begin by chartering a “technical debt inventory.” Their goals are to determine just how much technical debt they’re carrying, where it is, how much retiring it all will cost, and how fast they can retire it. That’s understandable. It’s not too different from how one would approach an out-of-control financial debt situation. Understandable, but in most cases, ineffective. With technical debt we need a different approach, because technical debt is different from financial debt. With technical debt, we must be guided by what I call the Principal Principle, which is:

The Metaphorical Principal (MPrin) of a technical debt, which is the cost of retiring it, isn’t what matters most. What usually matters most is the Metaphorical Interest Charges—the MICs.

The door to a bank vault
The door to a bank vault. One way to know that technical debt differs from financial debt is that banks aren’t involved in any way. Treating technical debt as if it had anything in common with financial debt—beyond our own sense of obligation—is a shortcut to real trouble. Remember the Principal Principle.

With technical debt, MICs can vary dramatically. For assets that aren’t being maintained or enhanced, the MICs can be Zero for extended periods. For retiring assets, their technical debt can vanish when the asset is retired. For other assets, MICs can be dramatically higher—beyond the total cost of replacing the asset.

Most people regard MICs as being restricted to productivity problems among engineers. I take a different approach. I include in MICs anything that depresses net income—lost or delayed revenue, increased expenses, anything. For instance, if technical debt causes a two-month delay in reaching a market, its effect on revenues can be substantial for years to come. I regard all of that total effect as contributing to MICs.

So the Principal Principle is that a focus on Principal can be your undoing. Focus on MICs. Drive them to Zero and keep them there.

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Leverage points for technical debt management

Last updated on December 11th, 2018 at 10:40 am

Adopting a program of technical debt management entails significant change to the system we call the enterprise. The problem can seem so daunting that we don’t know where to begin. The places to begin are the places where the change agents have greatest leverage—what systems analysts call leverage points. Consider this scenario.

You’re sitting in the kickoff meeting of the new Technical Debt Management Task Force. The CEO is talking about how she realized that the company had a technical debt problem. It was when the Marigold project went through delay after delay, and was finally declared done, with multiple objectives waived. She’s saying something about, “we were trying to do backflips with millstones around our necks. So I want this task force to show us how to get rid of the millstones, and then get rid of them.”

McMurdo Station, Antarctica, as seen from nearby Observation Hill
McMurdo Station, Antarctica, as seen from nearby Observation Hill. The United States Antarctic Program, a unit of the National Science Foundation, operates the station. It can house as many as 1258 people in Summer. Photo (cc) Gaelen Marsden courtesy .

OK, you think. But how? We’re a global enterprise with thousands of engineers and operations on every continent. Except maybe Antarctica. No wait, we’re there, too. McMurdo I think. We have software we don’t even know much about, acquired long ago along with the companies that built it. And we’re building new systems or modifying old ones all the time, trying to move everything to the cloud while enhancing data security. Where do we begin to look for the millstones of technical debt?

Have you been in that meeting? If not, can you imagine being in that meeting? Meetings like that are happening around the globe. We’re all in the same soup.

It turns out that the answers to the millstone questions are available, but the pioneers and deep thinkers who have shown the way aren’t working on technical debt. Their field is called systems analysis. They work on problems like the collapse of the North Atlantic fishery, urban deterioration, unemployment, poverty, climate change, and the causes of the Great Recession of 2008—really difficult problems. Although the technical debt problem isn’t quite that challenging, it’s challenging enough to justify taking a look at the methods of systems analysis.

And when we do that, we immediately encounter a concept many call leverage points.

What are leverage points?

Leverage points are places in complex systems where a small change in one thing can produce big changes in system behavior. In a brilliant 1997 article, Donella Meadows describes what she calls “places to intervene in a system.” [Meadows 1997] She followed this article, making improvements each time, in 1999 [Meadows 1999] and 2008 [Meadows 2008]. Let me summarize Meadows’ work here.

To alter the behavior of a complex system, intervene at one or more of 12 categories of leverage points. For example, one category is called “Rules.” It consists of the incentives, punishments, and constraints that govern the behavior of the people and institutions that comprise the system. By adjusting the system’s rules, we can alter overall system behavior.

One more thing: the leverage points form an ordered hierarchy, ordered by effectiveness. Acting at a higher-level leverage point is more effective than acting at a lower-level leverage point. And more difficult, too. The ordering of the categories is a bit fuzzy, because every situation has its own quirks, but generally, the order is as given in the list below.

In a moment I’ll give an example of using leverage point #9, Delays, to bring about change in the way the enterprise deals with technical debt. But first, here’s a brief summary of the leverage points in increasing order of leverage; not enough to truly understand what they are, but probably enough to pique your interest. As I write posts that illustrate interventions at these leverage points, I’ll link to them from here.

  1. Numbers: Constants and parameters such as subsidies, taxes, and standards
  2. Buffers: The sizes of stabilizing stocks relative to their flows
  3. Stock-and-Flow Structures: Physical systems and their nodes of intersection
  4. Delays in feedback loops
  5. Balancing Feedback Loops: The strength of the feedbacks relative to the impacts they are trying to correct
  6. Reinforcing Feedback Loops: The strength of the gain of driving loops
  7. Information Flows:  The structure of who does and does not have access to information
  8. Rules: Incentives, punishments, and constraints
  9. Self-Organization: The power to add, change, or evolve system structure
  10. Goals: The purpose or function of the system
  11. Paradigms: The mind-set out of which the system—its goals, structure, rules, delays, parameters—arises
  12. Transcending Paradigms

Changing systems that have delays in feedback loops

When we use feedback to control systems, and there are delays in the feedback, we can potentially create destructive system behavior. And that can happen when we try to control technical debt.

Whenever we try to control a quantity in an enterprise process, we must (a) set a target value for that quantity; then (b) measure its current value; and then (c) take action as appropriate to move the current value toward the target value. Systems analysts (and control theorists) call that arrangement a feedback loop. The action taken to move the current value to the target value is sometimes called the control signal. Under certain conditions, the feedback works as expected.

For example, to control the profitability of the enterprise, we can examine its net income, say, quarterly. And at the end of each quarter we can make adjustments if net income isn’t in the target range.

Feedback loops generally work pretty well, but under some conditions, oscillations can develop. One of those troublesome situations occurs when there’s a delay in the loop that’s of the same order as (or longer than) the time the system takes to respond to adjustments. Meadows uses the example of adjusting the water temperature of a shower when there’s a long delay between making the adjustment and feeling its effects. Overcorrection is almost inevitable, and that’s what causes system oscillation.

So let’s suppose that we’re trying to control the rate of accumulation of technical debt. One approach is to set a target for TDnew, the new technical debt generated in a project. To be fair to all projects, we decide to normalize this quantity according to the project budget B. So we set targets for each project’s N = TDnew/B, and we require that projects estimate N, on an ongoing basis, with a goal of having N in some target range when the project is complete.

One problem with this approach is that we rarely identify accurately all the technical debt we’ve incurred until some time has passed after project delivery. With time, as the newly produced assets go into production and learning accumulates, we acquire the wisdom needed to identify more of the technical debt we created. This is one source of delay in this feedback loop.

So let’s assume that this happens for several projects, and management decides that delayed recognition of incurred technical debt is a common occurrence. To account for this, management lowers the target ranges for N for future projects. This causes project managers and project sponsors to include in their project plans additional effort directed at retiring more of their incremental technical debt before their projects complete, to enable them to project lower values of N. They must therefore identify as much of the incremental technical debt as they can, and retire it, to meet the lower targets for N.

But recall that technical debt identification sometimes requires time and experience using the newly produced asset. And the reverse process also occurs. Technical artifacts that we thought were technical debt prove to be useful in unexpected ways, and actually turn out not to be debt items after all. As a result, some of the incremental technical debt that got retired before the project was completed actually should not have been retired. Eventually, people realize that this happens with uncomfortable frequency, and so the targets for N are raised once more.

Oscillations thus set in. Long delays inevitably cause them. To prevent oscillations, shorten the delays.

How to shorten delays in feedback controlling technical debt

With technical debt, we can shorten delays in several ways.

  • If the asset is meant for human use, involve representatives of the user population in the development and design process as soon as practical. Have them exercise the asset, or prototypes, early. Listen to their suggestions. Observe how they use the asset.
  • If the asset must interact with non-human assets, exercise it early and often. Don’t think of this as testing, though it might look very much like testing. What you’re actually doing is searching for shortcomings in how the asset interacts with non-human assets, in design and implementation in an asset that already works.
  • Subject the asset to multiple reviews all along the development trajectory. Don’t wait for final release to review it.

These practices expose technical debt items early—potentially, during initial design—thereby reducing delays in identifying what is and what is not technical debt. They help to advance the date at which we uncover missing capabilities or capabilities designed or implemented in awkward ways. No surprise, I’m sure, but these practices are consistent with Agile approaches to technological development.

Indirect effects can add to delayed recognition of technical debt

Most of the argument above assumed that the incremental technical debt associated with the project was incurred within the asset undergoing development or maintenance. But technical debt can occur in other assets as well. When the development team is unaware of such “remote” or “indirect” incremental technical debt, recognition of that new incremental technical debt can be significantly delayed. The project’s N will appear to be smaller that it actually is, until that remote incremental technical debt is recognized.

This form of delay is likely to occur when the debt incurred is asset-exogenous. Recall the example of line extension of mobile phones. In that example, the enterprise incurs technical debt in one set of products as a result of the introduction of a different product. In some cases, the newly incurred technical debt is immediately evident. When it is not, delays can be substantial.

This effect is by no means rare. Any organizational change can potentially add to the technical debt portfolio—reorganizations, acquisitions, expansions, wholly new products, and much more.

Conclusions

Interventions at the leverage points of an organization can produce the changes we want with a minimum of effort. Some subtlety is involved, because Meadows’ leverage points are expressed at a high level of abstraction.  But applying them to the problem of technical debt management is a promising approach.

Bookmark this post. I’ll be linking to more examples of using leverage points to manage technical debt. So far:

References

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.

Available: here; Retrieved: July 30, 2018

Cited in:

[Conroy 2012] Patrick Conroy. “Technical Debt: Where Are the Shareholders' Interests?,” IEEE Software, 29, 2012, p. 88.

Available: here; Retrieved: August 15, 2018.

Cited in:

[Cunningham 1992] Ward Cunningham. “The WyCash Portfolio Management System.” Addendum to the Proceedings of OOPSLA 1992. ACM, 1992.

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Meadows 1997] Donella H. Meadows. “Places to Intervene in a System,” Whole Earth, Winter 1997.

Available: here; Retrieved: June 28, 2018

Cited in:

[Meadows 1999] Donella H. Meadows. “Leverage Points: Places to Intervene in a System,” Hartland VT: The Sustainability Institute, 1999.

Available: here; Retrieved: June 2, 2018.

Cited in:

[Meadows 2008] Donella H. Meadows and Diana Wright. Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing, 2008.

Order from Amazon

Cited in:

[Southern Railfan 1966] Southern Railfan. “The Days They Changed the Gauge,” 1966.

Available: here; Retrieved: July 26, 2018.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

Cited in:

Other posts in this thread

Using SMART goals for technical debt reduction

Last updated on May 9th, 2019 at 02:17 pm

Attempting to reduce the enterprise burden of technical debt by setting so-called “SMART goals” in the obvious way can often produce disappointing results. SMART, due to George T. Doran [Doran 1981], is a widely used approach for expressing management goals. “SMART” is an acronym for “Specific, Measurable, Attainable, Realistic, and Time-boxed,” though the last three words (the “ART”) are chosen in various alternative ways. Doran himself used “assignable, realistic, and time-related.”

Prof. George T. Doran (1939-2011), creator of the S.M.A.R.T acronym for setting management objectives
Prof. George T. Doran (1939-2011), creator of the S.M.A.R.T acronym for setting management objectives. Watch a 2010 interview of Prof. Doran at YouTube.
SMART is so embedded in management culture that many assume without investigation that expressing technical debt reduction goals directly using the SMART pattern will produce the desired results. Also embedded in management culture is the aphorism, “You get what you measure.” [Ariely 2010]  [Bouwers 2010] Combining these two ideas in a straightforward way, one might express the technical debt reduction goal as, “Reduce the burden of technical debt by 20% per year for each of the next five years.”

There is ample support for a claim that this “direct” approach to applying the SMART technique will be ineffective. The fundamental issue is that so much of employee behavior affects technical debt indirectly that it overwhelms the effects of employee behaviors that affect technical debt directly. The result is that although the direct approach does cause some employees to adopt desirable behaviors, their impact is not significant enough compared to the effects of the behaviors of employees who see little connection between their own activities and the burden of technical debt, or who are subject to competing constraints on their behaviors that then cause them to act in ways that increase technical debt.

That’s why it’s necessary for management to develop a series of SMART goals that affect behaviors that have indirect effects on technical debt. In the first part of this post, “Setting a direct SMART goal for technical debt reduction is problematic,” I explore the problems inherent in the direct approach. In the second part, “How to set SMART goals for technical debt,” I provide examples of SMART goals that touch on behaviors that have indirect effects on technical debt.

Setting a direct SMART goal for technical debt reduction is problematic

Let’s begin by exploring some of the problems with the direct approach. In this section, I assume that management has set a SMART goal for the enterprise in the form, “Reduce the burden of technical debt by 20% per year for each of the next five years.” But there’s nothing special about the numbers. My comments below apply to the form of the goal, rather than the specific numbers.

The direct approach assumes measurability

To attain a goal of a 20% reduction in technical debt in a given year, we must be able to measure the level of technical debt at the beginning of the year and the level at the end of the year, presumably with confidence in the 90% range or better. Such a measurement with the precision required might not be possible. Moreover, in most cases the probability that such a measurement is possible is low. For these reasons, setting periodic goals for total technical debt is not a useful management tool.

Consider a simple example. One form of technical debt—and it’s a common form—is missing or incompletely implemented capability. In some instances, the metaphorical principal (MPrin) of a given instance of this debt in the current year can change spontaneously to a dramatically larger value in the following year (or even the following week), due to changes in the underlying asset unrelated to the technical debt, or due to debt contagion, or due to any number of other reasons. When this happens, the technical debt retirement effort for that year can appear to have suffered a serious setback, even though the technical debt retirement teams might have been performing perfectly well.

The direct approach assumes a static principal

With most financial debts, the principal amount is specified at the time of loan origination. Moreover, we can compute the principal at any time given the mathematical formulas specified in the loan agreement.

By contrast, in many cases, the metaphorical principal amount of a technical debt might be neither fixed nor readily computable. We can estimate the MPrin of a given kind of technical debt at a given time, and we can even make forward projections of those estimates. But they are merely estimates, and their error bars can be enormous. See “Policy implications of the properties of MPrin” and “Useful projections of MPrin might not be attainable.”

The direct approach focuses on MPrin, not MICs

Objectives expressed in terms of the volume of technical debt—the total MPrin—are at risk of missing the point. Total MPrin is not what matters most. What matters is MICs—the total cost of carrying the debt. Even more important is the timing of arrival of the MICs.

And like MPrin, MICs can vary in wild and unpredictable ways. For example, the MICs for a piece of technical debt borne by an asset that isn’t undergoing maintenance or enhancement can be zero; in a later time period, when that asset is undergoing enhancement, the MICs can be very high indeed. See  “MICs on technical debt can be unpredictable” for a detailed discussion.

Priority setting for technical debt retirement is most effective when it takes into account the timing of MICs. For example, if we know that we must enhance a particular asset by FY21 Q3, and if we know that it bears technical debt that adds to the cost of the enhancement, retiring that debt in FY20 would be advisable. On the other hand, if that form of technical debt has no effect on MICs for the foreseeable future, retiring that technical debt might not be worth the effort.

The direct approach fails to distinguish legacy technical debt from incremental technical debt

Unless policies are already in place governing the formation of new technical debt—what I call incremental technical debt—technical debt retirement programs might encounter severe difficulty meeting their goals. The technical debt retirement program might simply be unable to keep up with the formation of new technical debt resulting from new development or from ongoing maintenance and enhancement of existing assets.

The direct approach fails to anticipate the formation of enterprise-exogenous technical debt

Technical debt can sometimes form as a result of innovations, changes in standards, or changes in regulations that occur entirely external to the enterprise. I call such technical debt enterprise-exogenous. When this happens, the technical debt retirement effort can appear to have suffered a serious setback, even though the technical debt retirement teams might have been performing perfectly well. Before initiating a technical debt reduction program, it’s wise to first deploy a program that’s capable of retiring technical debt at a pace that at least equals the pace of formation of enterprise-exogenous technical debt.

Incurring technical debt is sometimes the responsible thing to do

At times, incurring technical debt is prudent. In some situations, accepting the debt you’ve incurred—even for the long term—might be called for. Because strict goals about total technical debt can lead to reluctance to incur debt that has a legitimate  business purpose, whatever goals are set for total technical debt must be nuanced enough to deal with these situations. Goals for total technical debt that adhere strictly to the SMART goal pattern sometimes lack the necessary level of nuance.

How to set SMART goals for technical debt

SMART goals can work for technical debt management, but we must express them in ways that are more closely related to behavioral choices. Here are some examples of SMART goals that can be effective elements of the technical debt management program. Some of these examples are admittedly incomplete. For example,  I offer no proof of assignability, attainability, or realism, because they can vary from organization to organization, or because the goal in question must be distributed across multiple organizational elements in ways peculiar to the organization.

At least 30% of incremental technical debt will be secured technical debt at the end of Year 1; 60% by the end of Year 2

Incremental technical debt is technical debt that’s incurred in the course of work currently underway or just recently completed. Because it’s so well understood, its MPrin can be estimated with higher precision than is usually possible with legacy technical debt. That precision is needed for defining the collateral and resources used to secure the debt.

A secured technical debt, like a secured financial debt, is one for which the enterprise reserves the resources needed to retire the debt. However, unlike a financial debt, the resources required to retire a technical debt might not be purely financial. Beyond financial resources, they might include particular staff, equipment, test beds, and downtime. The commitment might extend beyond the current fiscal period. Secured technical debt is a powerful means of driving down total technical debt burden, but it might require modification of internal budget management processes and fiscal reporting. Policymakers can help in designing and deploying the necessary changes.

Within one year, at least 50% of all incremental technical debt will be retired within one year of its origination; 70% within 18 months

This goal also exploits the fact that incremental technical debt can be estimated with relatively high precision. As a goal, it builds on the goal above by requiring that the resources pledged to retire incremental debts actually be expended as intended.

Within one year, all engineers and their direct supervisors will be educated in basic technical debt concepts

The educational materials will be developed in the next five months and piloted with 10% of the technical staff within seven months. The material will include an online proficiency test that 90% of engineers will have successfully passed within a year.

Within one year, 90% of project plans will include projections of the MPrin of the incremental technical debt they expect to generate for each delivery cycle

This information is useful for making forward projections of resources needed to secure incremental technical debt. Tracking the accuracy of these projections helps project planners improve their estimates.

Within one year, initiate a practice of identifying the top five forms of legacy technical debt, ranked by the volume of the contagion

Debt contagion is the propagation of a given form of technical debt by creating new system elements or assets in forms compatible with elements already identified as technical debt. By examining the body of incremental technical debt created enterprise-wide in a given time period (say, by fiscal quarter), we can determine the portion of that incremental debt that results from contagion, for each type of contagious legacy technical debt. This data is needed to identify the most contagious forms of legacy technical debt. They are prime candidates for debt retirement.

Within one year, initiate an industrial intelligence practice that is responsible for projecting the formation of enterprise-exogenous technical debt

This group must have a sophisticated grasp of the technologies in use within the enterprise that already bear enterprise-exogenous technical debt, or which could be subject to the formation of enterprise-exogenous technical debt. Its responsibilities cover enterprise products and services, as well as enterprise infrastructure. It issues advisories as needed, and an annual forecast. The group is also responsible for recommending and monitoring participation in industrial standards organizations. The group reports to the CIO or CTO.

References

[Ariely 2010] Dan Ariely. “You are what you measure,” Harvard Business Review 88:6, p. 38, 2010.

Available: here; Retrieved: June 4, 2018

Cited in:

[Bouwers 2010] Eric Bouwers, Joost Visser, and Arie van Deursen. “Getting What You Measure: Four common pitfalls in using software metrics for project management,” ACM Queue 10: 50-56, 2012.

Available: here; Retrieved: June 4, 2018

Cited in:

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.

Available: here; Retrieved: July 30, 2018

Cited in:

[Conroy 2012] Patrick Conroy. “Technical Debt: Where Are the Shareholders' Interests?,” IEEE Software, 29, 2012, p. 88.

Available: here; Retrieved: August 15, 2018.

Cited in:

[Cunningham 1992] Ward Cunningham. “The WyCash Portfolio Management System.” Addendum to the Proceedings of OOPSLA 1992. ACM, 1992.

Cited in:

[Doran 1981] George T. Doran. “There’s a S.M.A.R.T. Way to Write Management’s Goals and Objectives”, Management Review, 70:11, pp. 35-36, 1981.

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Meadows 1997] Donella H. Meadows. “Places to Intervene in a System,” Whole Earth, Winter 1997.

Available: here; Retrieved: June 28, 2018

Cited in:

[Meadows 1999] Donella H. Meadows. “Leverage Points: Places to Intervene in a System,” Hartland VT: The Sustainability Institute, 1999.

Available: here; Retrieved: June 2, 2018.

Cited in:

[Meadows 2008] Donella H. Meadows and Diana Wright. Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing, 2008.

Order from Amazon

Cited in:

[Southern Railfan 1966] Southern Railfan. “The Days They Changed the Gauge,” 1966.

Available: here; Retrieved: July 26, 2018.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

Cited in:

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Exogenous technical debt

Last updated on July 24th, 2018 at 07:30 pm

Mastering understanding of exogenous technical debt—debt that arises from causes not directly related to the asset that bears the debt—is essential to controlling technical debt formation. Exogenous technical debt is particularly troublesome to those who work on the affected assets. They can’t control its formation, and they’re rarely responsible for creating it. But their internal customers and those who control resources often fail to understand this. Indeed, those who work on the affected assets are often blamed for the formation of exogenous technical debt even though they had no role in its formation, and could have done nothing to prevent its formation.

Asbestos with muscovite.
Asbestos with muscovite. Asbestos is a family of six minerals that occur naturally in fibrous form. The fibers are all known carcinogens. Until 1990, it was widely used in many common building materials, including insulation, plaster, and drywall joint compound. It is now banned, but it’s present in many existing structures, including homes and offices. The installation of the ban caused these structures to incur exogenous technical debt. Photo by Aramgutang courtesy Wikipedia.

Technical debt is exogenous when it’s brought about by an activity not directly related to the assets in which the debt appears. The word exogenous comes from the Greek exo– (outside) + –genous (related to producing). So exogenous technical debt is that portion of an asset’s debt that comes about from activities or decisions that don’t involve the asset directly.

Because so much technical debt is produced indirectly, controlling its direct formation—for example, by engineering teams—isn’t sufficient for achieving enterprise control of technical debt formation. To control technical debt formation, we must track which activities produce it, including both direct and indirect effects. Allocating technical debt retirement costs to the activities that brought that debt about, even if the allocation doesn’t affect budget authority for those activities, is therefore a useful practice. Knowledge about which past activities created technical debt, and how much, is helpful for long-term reduction in the rate of technical debt formation.

When we think of technical debt, we tend to think of activities that produce it relatively directly. We often imagine it as resulting solely from engineering activity, or from decisions not to undertake engineering activity. In either case the activity involved, whether undertaken or not, is activity directly involving the asset that carries—or will be carrying—the technical debt. This kind of technical debt is endogenous technical debt. The word endogenous comes from the Greek endo– (within or inside) + –genous (related to producing).  So endogenous technical debt is that portion of an asset’s debt that comes about from activities or decisions that directly involve the asset.

More about endogenous technical debt in future posts. For now, let’s look more closely at exogenous technical debt, and its policy implications.

Examples of exogenous technical debt

In “Spontaneous generation,” I examined one scenario in which technical debt formation occurs spontaneously—that is, in the absence of engineering activity. Specifically, I noted how the emergence of the HTML5 standard led to the formation of technical debt in some (if not all) existing Web sites, in the sense that they didn’t exploit capabilities that had become available in HTML5. Moreover, some sites whose developers had elected to emulate capabilities of the new standard by exploiting alternative technologies needed rehabilitation to remove the emulation and replace it with use of facilities in the HTML5 standard. All of these artifacts—including those that existed, and those that didn’t—comprised technical debt. This scenario thus led to the formation of exogenous technical debt.

In a second example, AMUFC, A Made-Up Fictitious Corporation, incurs technical debt when the vendor that supplies the operating system (OS) for AMUFC’s desktop computers announces the date of the end of extended support for the version of the OS in use at AMUFC. Because the end of extended support brings an end to security updates, AMUFC must retire that debt by migrating to the next version of that vendor’s OS before extended support actually ends.

In both of these examples, the forces that lead to formation of exogenous technical debt are external to both the enterprise and the enterprise’s assets. But what makes technical debt exogenous is that the forces that led to its formation are unrelated to any of the engineering work being performed on the asset that carries the debt. This restriction is loose enough to also include technical debt that arises from any change or activity external to the asset, but within the enterprise.

Exogenous technical debt arising from actions within the enterprise

Exogenous technical debt can arise from activities or decisions that take place entirely within the enterprise.

For example, consider a line of mobile devices developed and marketed by AMUFC (A Made-Up Fictitious Corporation). Until this past year, AMUFC has been developing ever more capable devices, thereby extending its line of offerings at the high end—the more expensive and capable members of the line. But this past quarter, AMUFC developed a low-end member of the line, and as often happens, price constraints led to innovations that could produce considerable savings in manufacturing costs if those innovations were applied to all members of the line. In effect, then, the designs of the previously developed models in this line of devices have incurred exogenous technical debt. The debt is exogenous because the activity that led to debt formation was not performed on the assets that now carry the debt, even though the activity that led to debt formation occurred within the enterprise. This kind of exogenous technical debt might be termed asset-exogenous. Exogenous technical debt of the kind that’s incurred by activity beyond the enterprise might be termed enterprise-exogenous.

Exogeneity versus endogeneity

For asset-exogenous technical debt, ambiguity between endogeneity and exogeneity can arise. The example above regarding the line of mobile devices produced by AMUFC provides an illustration.

For convenience, call the team that developed one of the high-end devices Team High. Call the team that developed the low-end device Team Low. From the perspective of Team High, the technical debt due to the innovations discovered by Team Low is exogenous. But from the perspective of the VP Mobile Devices, that same technical debt might be regarded as endogenous. The debt can be endogenous at VP level because it’s possible to regard the entire product line as a single asset, and that might actually be the preferred perspective of VP Mobile Devices.

Exogeneity and legacy technical debt

The technical debt portfolio of a given asset can contain a mix a technical debt that arose from various past incidents. In assessing the condition of the asset, it’s useful to distinguish this existing debt from debt that’s incurred as a consequence of any current activity or decisions. Call this pre-existing technical debt legacy technical debt.

The legacy technical debt carried by an asset is technical debt associated with that asset, and which exists in that asset in any form prior to undertaking work on that asset. For example, in planning a project to renovate the hallways and common areas of a high-rise apartment building, workers discover that beneath the existing carpeting is a layer of floor tile containing asbestos. Management has decided to remove the tile. In this context, the floor tile can be viewed as legacy technical debt. It isn’t directly related to the objectives of the current renovation, but removing it will enhance the safety of future renovations, enable certification of the building as asbestos-free, increase the property value, and reduce the cost of eventual demolition. In this situation asbestos removal amounts to retirement of legacy technical debt, and accounting for it as part of the common-area renovation would be misleading.

When contemplating efforts to retire legacy technical debt, exogeneity becomes a factor in allocating the necessary resources. If the debt in question is enterprise-exogenous, then we can justifiably budget the effort from enterprise-level accounts if appropriate. For other cases, other pools of resources become relevant depending on what actions created the debt. For example, if the exogenous technical debt arose because of a departmental change in standards, debt retirement costs can justifiably be allocated to the standards effort. If the exogenous technical debt arose from innovations in other members of the asset’s product line, those debt retirement costs can justifiably be allocated to the product line.

Policy insights

Understanding the properties of exogenous technical debt can be a foundation for policy innovations that enhance enterprise agility.

Culture transformation

Widespread understanding the distinction between exogenous and endogenous technical debt is helpful in controlling blaming behavior that targets the engineering teams responsible for developing and maintaining technological assets.

Understanding of asset-exogenous technical debt helps non-engineers understand how their actions and decisions can lead to technical debt formation, even when there is no apparent direct connection between those actions or decisions and the assets in question.

Resource allocation

Data about the technical debt creation effects of enterprise activities is helpful in allocating technical debt retirement costs. For example, when we know all the implications of reorganization, including its impact on internal data about the enterprise itself, we can charge data-related activity to the reorganization instead of to general accounts of the Information Technology function. This helps the enterprise understand the true costs of reorganization.

Similarly, data about enterprise-exogenous technical debt helps planners understand how to deploy resources to gather external intelligence about trends that can affect internal assets. Such data is also useful for setting levels of support and participation in industrial standards organizations or in lobbying government officials.

Knowing the formation history of exogenous technical debt provides useful guidance for those charged with allocating the costs of retiring technical debt or preventing its formation.

References

[Ariely 2010] Dan Ariely. “You are what you measure,” Harvard Business Review 88:6, p. 38, 2010.

Available: here; Retrieved: June 4, 2018

Cited in:

[Bouwers 2010] Eric Bouwers, Joost Visser, and Arie van Deursen. “Getting What You Measure: Four common pitfalls in using software metrics for project management,” ACM Queue 10: 50-56, 2012.

Available: here; Retrieved: June 4, 2018

Cited in:

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.

Available: here; Retrieved: July 30, 2018

Cited in:

[Conroy 2012] Patrick Conroy. “Technical Debt: Where Are the Shareholders' Interests?,” IEEE Software, 29, 2012, p. 88.

Available: here; Retrieved: August 15, 2018.

Cited in:

[Cunningham 1992] Ward Cunningham. “The WyCash Portfolio Management System.” Addendum to the Proceedings of OOPSLA 1992. ACM, 1992.

Cited in:

[Doran 1981] George T. Doran. “There’s a S.M.A.R.T. Way to Write Management’s Goals and Objectives”, Management Review, 70:11, pp. 35-36, 1981.

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Meadows 1997] Donella H. Meadows. “Places to Intervene in a System,” Whole Earth, Winter 1997.

Available: here; Retrieved: June 28, 2018

Cited in:

[Meadows 1999] Donella H. Meadows. “Leverage Points: Places to Intervene in a System,” Hartland VT: The Sustainability Institute, 1999.

Available: here; Retrieved: June 2, 2018.

Cited in:

[Meadows 2008] Donella H. Meadows and Diana Wright. Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing, 2008.

Order from Amazon

Cited in:

[Southern Railfan 1966] Southern Railfan. “The Days They Changed the Gauge,” 1966.

Available: here; Retrieved: July 26, 2018.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

Cited in:

Other posts in this thread

Useful projections of MPrin might not be attainable

Last updated on November 28th, 2017 at 11:32 am

SummaryExpect the unexpected with technical debt retirement efforts. Technical debt retirement efforts can conflict with ongoing operations, maintenance of existing capabilities, development of new capabilities, cyberdefense, or other technical debt retirement efforts. Although these conflicts are technical in nature, resolving them can involve business priorities at any level. Planners must be aware of these potential conflicts, and coordinate with their leaders. Policymakers can make important contributions to the enterprise mission if they can devise guidelines and frameworks for resolving these conflicts as closely as possible to the technical level.

For planning purposes, it’s necessary from time to time to project the size of the MPrin for given class of technical debt. The need arises when planning debt retirement, or when preparing debt retirement options for determining resource allocations. Although retiring some kinds of technical debt is straightforward, other kinds of debt can be intertwined with each other. Still others might appear to be easily retired, but actual retirement efforts expose unanticipated entanglements. Moreover, debt retirement efforts can sometimes interact with other debt retirement efforts, operations, maintenance, cyberdefense, and new development in both expected and unexpected ways. For these reasons, making estimates of the MPrin with enough precision to be useful can be notoriously difficult.

A tangle of cordage
A tangle of cordage on board ship. Different kinds of technical debt can be tangled with each other, and untangling them can affect various other engineering efforts. Preparing an asset for a debt retirement effort by doing some preliminary untangling might be wise before trying to estimate the MPrin of any affected class of technical debt.

These considerations rarely arise when planning retirement of financial debts, because money is fungible. We might indeed have other uses for financial resources, but every unit of cash is equivalent to every other. That freedom is not necessarily available when planning resource allocations for technical debt retirement.

For example, not every engineer is equally qualified to address every problem. Some people are particularly capable for certain kinds of work, and not very qualified for other kinds. The problem of scheduling specialists is notorious for generating bottlenecks. And split assignments create even more trouble. People are not fungible.

Planning retirement of a particular set of technical debt classes requires knowledge of any efforts with which that retirement effort might interact. That information might not be available or might not be known. In general, preliminary work to decouple these activities — often called refactoring — can greatly simplify technical debt retirement planning. Even before undertaking refactoring, gathering information about the entanglements of different classes of technical debt can be very helpful. Because allocating resources to such efforts can be difficult in feature-oriented cultures, policymakers can take the lead in raising the priority of such efforts.

References

[Ariely 2010] Dan Ariely. “You are what you measure,” Harvard Business Review 88:6, p. 38, 2010.

Available: here; Retrieved: June 4, 2018

Cited in:

[Bouwers 2010] Eric Bouwers, Joost Visser, and Arie van Deursen. “Getting What You Measure: Four common pitfalls in using software metrics for project management,” ACM Queue 10: 50-56, 2012.

Available: here; Retrieved: June 4, 2018

Cited in:

[Brenner 2017b] Richard Brenner. “Managing Technical Debt: Nine Policy Recommendations,” Cutter Consortium Executive Update 18:4, 2017.

Available: here; Retrieved: December 29, 2017

Cited in:

[Brown 2010] Nanette Brown, Yuanfang Cai, Yuepu Guo, Rick Kazman, Miryung Kim, Philippe Kruchten, Erin Lim, Alan MacCormack, Robert Nord, Ipek Ozkaya, Raghvinder Sangwan, Carolyn Seaman, Kevin Sullivan, and Nico Zazworka. “Managing Technical Debt in Software-Reliant Systems,” in Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research 2010, New York: ACM, 2010, 47-51.

Available: here; Retrieved: July 30, 2018

Cited in:

[Conroy 2012] Patrick Conroy. “Technical Debt: Where Are the Shareholders' Interests?,” IEEE Software, 29, 2012, p. 88.

Available: here; Retrieved: August 15, 2018.

Cited in:

[Cunningham 1992] Ward Cunningham. “The WyCash Portfolio Management System.” Addendum to the Proceedings of OOPSLA 1992. ACM, 1992.

Cited in:

[Doran 1981] George T. Doran. “There’s a S.M.A.R.T. Way to Write Management’s Goals and Objectives”, Management Review, 70:11, pp. 35-36, 1981.

Cited in:

[Fowler 2009] Martin Fowler. “Technical Debt Quadrant.” Martin Fowler (blog), October 14, 2009.

Available here; Retrieved January 10, 2016.

Cited in:

[Garnett 2013] Steve Garnett, “Technical Debt: Strategies & Tactics for Avoiding & Removing it,” RippleRock Blog, March 5, 2013.

Available: here; Retrieved February 12, 2017.

Cited in:

[Gould 1996] Stephen Jay Gould. The mismeasure of man (Revised & Expanded edition). W. W. Norton & Company, 1996.

Order from Amazon

Cited in:

[Levy 2009] David A. Levy, Tools of Critical Thinking: Metathoughts for Psychology (second edition). Long Grove, Illinois: Waveland Press, Inc., 2009.

Cited in:

[Meadows 1997] Donella H. Meadows. “Places to Intervene in a System,” Whole Earth, Winter 1997.

Available: here; Retrieved: June 28, 2018

Cited in:

[Meadows 1999] Donella H. Meadows. “Leverage Points: Places to Intervene in a System,” Hartland VT: The Sustainability Institute, 1999.

Available: here; Retrieved: June 2, 2018.

Cited in:

[Meadows 2008] Donella H. Meadows and Diana Wright. Thinking in Systems: A Primer. White River Junction, VT: Chelsea Green Publishing, 2008.

Order from Amazon

Cited in:

[Southern Railfan 1966] Southern Railfan. “The Days They Changed the Gauge,” 1966.

Available: here; Retrieved: July 26, 2018.

Cited in:

[Weinberg 1985] Gerald M. Weinberg. The Secrets of Consulting. New York: Dorset House, 1985.

Ford’s Fundamental Feedback Formula. Order from Amazon

Cited in:

[Whitehead 1948] Alfred North Whitehead. Science and the Modern World. New York: Pelican Mentor (MacMillan), 1948 [1925].

Cited in:

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