Synergy between the reification error and confirmation bias

Last updated on July 10th, 2021 at 08:53 am

In deciding whether to undertake technical debt retirement projects, organizations risk making inappropriate decisions because of a synergy between the reification error and confirmation bias. Together, these two errors of thought create conditions that make committing appropriate levels of resources difficult. And when organizations do commit resources, they tend to underestimate costs. That underestimate can elevate the chance of failure in technical debt retirement projects.

The reification error and confirmation bias

As explained elsewhere in this blog, the reification error is an error of reasoning in which we treat an abstraction as if it were a real, concrete, physical thing. Because technical debt is an abstraction, we risk committing the reification error when we deal with it. (See “Metrics for technical debt management: the basics”)

Confirmation bias is a cognitive bias that causes us to favor and seek only information that confirms our preconceptions, or to avoid information that disconfirms them. (See “Confirmation bias and technical debt”)

How the reification error affects management

The reification error might be responsible, in part, for a widely used management practice that often appears in the exploratory stages of undertaking projects. Let’s start with an illustration from the physical world.

In the physical world, when we want cherries, we go to a market and check the price per pound or kilo. Then we decide how much we want. If the price is high, we might decide to buy fewer cherries. If the price is low, we might buy more cherries. We have in mind a total cost target, and we adjust the weight of the cherries to meet the target. In the physical world, we can often adjust what we purchase to match our willingness to pay.

Retiring technical debt doesn’t work like that, in part, because technical debt is an abstraction. But we try anyway; here’s how it goes. Management decides to retire a particular class of technical debt. They ask an engineer for an estimate of the cost. Sometimes Management reveals the target they have in mind if they have one; sometimes not. The estimate comes back as Total ± Uncertainty. Management decides that’s too high, or the Uncertainty is too great. They then ask the engineer to find a way to do it for less, or to reduce the Uncertainty.

Management—the “customer” in this scenario—makes this request, in part, based on the belief that adjusting the work is possible. Management hopes that the engineer can adjust the work to meet a (possibly unstated) target, in analogy to buying cherries. That thinking is an example of the reification error. In this dynamic, we rarely take into account the fact that retiring technical debt isn’t exactly like buying cherries.

How confirmation bias affects engineering estimates

Return now to the interaction between Management and the engineer/estimator. The engineer now suspects that Management does have a target in mind. Some engineers might ask what the target is. Some don’t. In any case, the engineer makes a lower estimate, which might still be too high. This process repeats until either Management decides against retiring the debt, or accepts the lowest Total ± Uncertainty.

In adjusting their estimates, engineers have a conflict of interest. That conflict of interest can compromise their objectivity through the action of confirmation bias. For technical debt retirement efforts, engineers are usually highly motivated to gain Management approval of the project. The motivation arises, in part, from the frustrating loss of engineering productivity. And since engineers typically sense that Management approval of the project is contingent on finding an estimate that’s low enough, the engineers have a preconception. That is, engineers have an incentive to convince themselves that Management’s adjustments to budget and schedule are reasonable. Because of the confirmation bias, engineers tend to seek justifications for the adjustments. And they tend to avoid seeking justifications for believing that their adjustments might not be feasible. That’s the confirmation bias in action.

How synergy between the reification error and confirmation bias comes about

Because of the reification error, Management tends to believe that retiring technical debt is a more adjustable activity than it actually is. Because of confirmation bias, engineers tend to believe that Management’s proposed cost and schedule are feasible. Too often, the synergy between the two errors of thinking provides a foundation for disaster.

Why this synergy creates conditions for disaster in technical debt retirement projects

Management usually equates estimates with commitments. Engineers don’t. Management usually forgets or ignores the upside Uncertainty. Typically, when Management accepts an estimate, the engineering team finds that it has made a commitment to deliver the work for the cost Total, with zero upside Uncertainty. Rarely does Management make this explicit. An analogous problem occurs with schedule.

By ignoring the Uncertainty, Management (the buyer) transfers the uncertainty risk to the project team. That strategy might work to some extent with conventional development or maintenance projects, where we can adjust scope and risk before the work begins. But for technical debt retirement projects, this practice creates problems for two reasons.

Adjusting the scope of debt retirement projects is difficult

First, with technical debt retirement we’re less able to adjust scope. To retire a class of technical debt, we must retire it in toto. If we retire only some portion of a class of technical debt, we would leave the asset in a mixed state that can actually increase MICs. So it’s usually best to retire the entirety of any class of technical debt, so as to leave the asset in a uniform state.

Debt retirement efforts are notoriously unpredictable

Second, the work involved in retiring a particular class of technical debt is more difficult to predict than is the work involved in more conventional projects. (See “Useful projections of MPrin might not be attainable”) Often, we must work with older assets, or older portions of younger assets. The people who built them aren’t always available, and documentation can be sparse or unreliable. Moreover, it’s notoriously difficult to predict with accuracy when or for how long affected assets will be out of production. Revenue stream interruptions, which can comprise a significant portion of total costs, can be difficult to schedule or predict. Thus, technical debt retirement projects tend to be riskier than other kinds of projects. They have wider uncertainty bands. Ignoring the Uncertainty, or trying to transfer responsibility for it to the project team, is foolhardy.

A strategy for reducing the effects of this synergy

To intervene in the dynamic between the consequences of the reification error and the consequences of confirmation bias, we must find a way to limit how their consequences can interact. That will curtail the ability of one phenomenon to reinforce the other. This task is well suited for application of Donella Meadows’ concept of leverage points [Meadows 1999]. See “Leverage points for technical debt management.”

In that post, I summarized Meadows’ concepts of using leverage points to alter the behavior of complex systems. One can intervene at one or more of 12 categories of leverage points. These are elements in the system that govern the behavior of the people and institutions that comprise the system. In that post, I sketched the use of Leverage Point #9, Delays, to alter the levels of technical debt in an enterprise.

In what follows I sketch the use of interventions at Leverage Point #8, “The strength of negative feedback loops, relative to the impacts they are trying to correct against.”

Our strategy is this:

A feedback loop that now provides budgetary control in most organizations

One feedback loop at issue in this case, illustrated above, influences managers who might otherwise overrun their budgets. It does so by triggering some sort of organizational intervention when a manager overruns his or her budget. And the feedback loop leads to increases in the size and stature of the portfolios of managers who handle their budgets responsibly.  Presumably, that’s one reason why managers compel estimators to find approaches that cost less. The feedback loop to which managers are exposed causes them to establish another feedback loop involving the engineer/estimator, and later the engineering team. That second loop causes engineers to hold down their estimates, and later to limit actual expenditures.

A diagram of effects analysis

A feedback loop that now provides budgetary control in most organizations
A feedback loop that now provides budgetary control in most organizations.

We can use a diagram of effects [Weinberg 1992] to illustrate the feedback mechanism commonly used to control the performance of managers who are responsible for portfolios of project budgets. In the diagram (above), the oval blobs represent quantities indicated by their respective captions. Each of these quantities is assumed to be measurable, though their precise values and the way we measure them are unimportant for our rather qualitative argument.

What the arrows mean

Notice that arrows connect the blobs. The arrows represent the effect of changes in the value represented by one blob on the value represented by another. The blob at the base of the arrow is the effector quantity. The blob at the point of the arrow is the affected quantity. Thus, the arrow running from the blob labeled “Actual Spend” to the blob labeled “Overspend” expresses the idea that a positive (or negative) change in the amount of actual spending on projects causes a positive (or negative) change in Overspend. When a change in the effector quantity causes a like-signed change in the affected quantity, we say that their relationship is covariant.

Because increases in Budget Authority tend to decrease Overspend, all other things being equal, the relationship between Budget Authority and Overspend is contravariant. We represent a contravariant relationship between the effector quantity and the affected quantity as an arrow with a filled circle on it.

Finally, notice that the arrow from Overspend (effector) to Promotion Probability (affected) has a filled Delta on it. This represents the idea that as Overspend increases, it negatively affects the probability that the manager will be promoted at some point in the future. The Delta indicates a delayed effect; that the Delta is filled indicates a contravariant relationship. (An unfilled Delta would indicate a delayed covariant effect.)

Loops in the diagram of effects

This diagram, which contains a loop connecting Budget Authority, Overspend, and Promotion Probability, has the potential to “run away.” That is, as we go around the loop, we find self-re-enforcement, because the loop has an even number of contravariant relationships. It works as follows:

As Overspend increases, after a delay, the Probability of Promotion decreases. This causes reductions in Budget Authority because, presumably, the organization has reduced faith in the manager’s performance. Reductions in Budget Authority make Overspend more likely, and round and round we go.

Similarly:

As Overspend decreases, after a delay, the Probability of Promotion increases. This causes increases in Budget Authority because, presumably, the organization has increased faith in the manager’s performance. Increases in Budget Authority make Overspend less likely, and round and round we go.

Fortunately, other effects usually intervene when these self-re-enforcing phenomena get too large, but that’s beyond the scope of this argument. For now, all we need observe is that managers who manage their budgets effectively tend to rise in the organization; those who don’t, don’t.

The result is that managers limit spending to avoid overspending their budget authority. And that’s one reason why they push engineers to produce lower estimates for technical debt retirement projects.

How this feedback loop overlooks important drivers of technical debt formation

To break the connection between the managers’ reification error and the engineers’ confirmation bias, our intervention must cause the managers and the engineers to make calculations differently. We can accomplish this by requiring that they consider more than the mere cost of retiring the class of technical debt under consideration. They must estimate the consequences of not retiring that technical debt, and they must also estimate costs beyond the cost of retiring the debt. In what follows, I’ll use the shorthand TDBCR to mean the class of Technical Debt Being Considered for Retirement.

Specifically, estimates for technical debt retirement projects cover only the cost of performing the work required to retire the TDBCR. Management then decides whether, when, and to what extent to commit resources to execute the project. The primary considerations budgetary.

Since the debt retirement project can potentially provide benefits beyond the manager’s own portfolio, failing to undertake the project can have negative consequences. Mnagers who decline to undertake debt retirement projects are responsible for the consequences. But accountability for these decisions is rare. That’s the heart of the problem. So let’s look at some examples of relevant considerations.

Adjustments that would support these feedback loops to gain control of technical debt

In allocating resources for a technical debt retirement project, there are considerations beyond the cost of retiring the debt. A responsible decision is possible only if other kinds of estimates are also available. Here are some examples of the estimates we need:

  • The effects of retiring TDBCR on the cost of executing any other development or maintenance effortsy
  • The effects of retiring TDBCR on revenue and market share for all existing assets that directly produce revenue and which could be affected by retiring TDBCR
  • The revenue that would become available (and timing thereof) from any new products or services that become possible because of retiring TDBCR
  • The effects of retiring TDBCR on the cost of executing other technical debt retirement efforts

And these items might not be related to anything for which the decision maker is responsible. But the feedback loop we now use to influence the decision maker excludes considerations that are affected by the decision maker’s decisions. Until we install feedback loops that cause the decision maker to consider these indirect consequences, or until we make decisions at levels that include these other consequences, the effects of the decision maker’s decisions are uncontrolled, and might not lead to decisions optimal for the enterprise.

References

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Other posts in this thread

The Principal Principle: Focus on MICs

Last updated on July 7th, 2021 at 03:16 pm

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.

When some organizations first realize that technical debt is limiting their performance, they begin by chartering a “technical debt inventory.” They try to determine how much technical debt they’re carrying, where it is, and how much retiring it would cost. They really want to know 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. It might be understandable, but in most cases, inventorying all technical debt is ineffective. For technical debt we need a different approach, because technical debt is different from financial debt. With technical debt, we must be follow 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.

Why MICs matter more

MICs on technical debt can vary dramatically. For assets that aren’t undergoing maintenance or enhancement, the MICs can be Zero for extended periods. And for retiring assets, any technical debt they carry can vanish when the asset passes out of service. For other assets, MICs can be dramatically higher—beyond the total cost of replacing the asset.

Most people regard the sole effect of MICs as reduction in engineering productivity. I take a different approach. I include in MICs anything associated with technical debt and which depresses net income. That would include lost or delayed revenue, increased expenses—anything. For example, suppose technical debt causes a two-month delay in reaching a market. IN that case, 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 instead. Drive them to Zero and keep them there.

Other posts in this thread

Separating responsibility for maintenance and acquisition

Last updated on July 8th, 2021 at 01:28 pm

Separating responsibility for maintenance and acquisition or development of technical assets can lead to uncontrolled growth of technical debt. The problem arises when we measure without regard for technical debt the performance of the business acquisition function or the performance of the development organization. In that circumstance, technical debt is likely to expand unchecked. To limit such expansion, policymakers must devise performance measures that hold these organizations accountable for technical debt resulting from their actions.

Software systems

Road damage in Warwick, Rhode Island, resulting from historic storms in March 2010
Road damage in Warwick, Rhode Island, resulting from historic storms in March 2010 [NOAA 2013]. The storms were so severe that the floodwaters overtopped the gauge’s measurable range. Moreover, the National Weather Service (NWS) lacked a database of measurable ranges for flood gauges. Quoting the NWS report: “A lesson learned here was that maximum recordable river levels should be known by NWS staff, not only so staff aren’t surprised when this type of issue arises, but also to notify USGS personnel so that they can install a temporary gage and remove or elevate threatened equipment.” Technical debt, if ever I’ve seen it.
For systems consisting solely of software, separating responsibility for maintenance and acquisition or system development is risky. It enables the acquiring organization to act with little regard for the consequences of its decisions vis-à-vis maintenance matters [Boehm 2016]. This is unfortunate—it increases the rate of accumulation of new technical debt. And it increases the lifetime of legacy technical debt. This happens more frequently when the acquiring organization doesn’t suffer the MICs associated with the technical debt.

For example, a focus on performance of the organization that’s responsible for acquisition biases them in favor of attending to the direct and immediate costs of the acquisition. They are likely to have little or no regard for ongoing maintenance issues. The maintenance organization must then deal with whatever the acquired system contains (or lacks).

An analogous mechanism operates for organizations that develop, market, and maintain products or services. When there are software elements in their respective infrastructures, separation of the development function from the maintenance function enables the development function to act independently of the maintenance consequences of its decisions.

Systems that include hardware

But the separation-of-responsibilities mechanism that leads to uncontrolled technical debt isn’t restricted to software. Any technological asset that has ongoing maintenance needs (and most of them do) can potentially present this problem.

For example, in the United States, and many other countries, two streams of resources support publicly-owned infrastructure [Blair 2017]. The funding stream covers construction, operations and maintenance, and repairs. Its usual sources are taxes, tolls, licenses, other user fees, sale of ad space, and so on. The financing stream covers up-front construction costs, to bridge the period from conception through construction, until the funding stream begins delivering resources. The financing stream usually comes from bond sales.

Although legislatures, or agencies they establish, control both streams of resources, the effects of the streams differ fundamentally. The financing stream is dominant during construction and the early stages of the asset’s lifecycle. The funding stream is dominant after that—when maintenance and operations are most important. Legislators and agencies are generally reluctant to supply funding because of the impact on taxpayers and users. Legislators and agencies find financing much more palatable. For this reason, among others, funds for U.S. infrastructure maintenance are generally insufficient, and technical debt gradually accumulates.

So it is with technological assets in organizations. For accounting purposes, capital expenses are treated differently from operational expenses. The result is that operational expenses can have a more significant impact on current financial results than capital expenses do. This leads organizations to underfund operations and maintenance, which contributes to technical debt accumulation.

Last words

Control of new technical debt accumulation and enhancement of technical debt retirement rates is possible only if we can somehow hold accountable the acquisition or development organizations for the MICs that result from their actions. Securitization of the debt incurred, as I’ll address in a forthcoming post, is one possible means of imposing this accountability. But reserves are also required, because some of the debt incurred might not be known at the time the asset is acquired or created.

Separating responsibility for maintenance and acquisition or system development is actually a form of stovepiping. See “Stovepiping can lead to technical debt” for more on stovepiping.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Other posts in this thread

Zero tolerance and work-to-rule create adversarial cultures

Last updated on July 8th, 2021 at 01:16 pm

Defining technical debt explicitly enough for project objectives is difficult. Confronted with this difficulty, some internal customers of technologists adopt a zero-tolerance approach to technical debt. But rarely do they define technical debt explicitly. Post-delivery, when customers discover technical debt, they hold technologists responsible. They do so even in cases when no one could have predicted that a specific artifact would eventually become technical debt. This establishes an adversarial culture in which technologists contend with their internal customers. To defend themselves, technologists sometimes adopt a work-to-rule approach to their work.

How adversarial cultures develop

Delayed or cancelled flights can indicate that pilots or others are engaged in a work-to-rule action
Trouble at the airport. When airline pilots engage in work-to-rule actions, the immediate result can be large numbers of delayed or cancelled flights. The longer-term result might be beneficial to pilots, the airline, and the public, but only if labor peace can be restored, and the damage to the flying public can be overcome. So it is with work-to-rule deliveries as a way of dealing with zero-tolerance technical debt policies. The organization must overcome the adversarial culture that results from indiscriminate attempts to control technical debt. Technologists do gain some measure of protection by working to rule, but the longer-term benefit of the organization’s learning to manage technical debt arrives only if the adversarial culture can be overcome. Image (cc) Hotelstvedi courtesy Wikimedia.
And that’s when the trouble begins.

Within this adversarial dynamic, technologists try to protect themselves against future recriminations by “working to rule.” They perform only work that the internal customer specifies. If they find something else that needs work, they perform that work only if they successfully obtain the customer’s approval. Some customers continue to adhere to zero-tolerance policies with respect to technical debt. But engineers cannot meet such non-specific requirements. Because technologists are “working to rule,” they use the ambiguity of the zero-tolerance requirement to assert that they performed all explicitly specified work. This level of performance is analogous to the work-to-rule actions of some employees involved in labor disputes with their employers. In these actions, employees are literally in compliance with the requirements of the employer, but only literally [LIBCom 2006].

Preventing formation of an adversarial culture

Requiring that deliverables be free of technical debt contributes to formation of an adversarial culture. In such cultures the adversaries are the technologists and their internal customers. Shedding that adversarial culture can be difficult once it sets in. Compelling employees, vendors, or contractors to deliver work that’s free of all technical debt is therefore unlikely to succeed. Whether employees or contractors perform the work in-house, or the work is outsourced, the results can be problematic. In the context of an adversarial culture, deliverables that meet the minimum possible interpretation of the stated objectives are almost certain to carry unacceptable levels of technical debt. What can we do to prevent this?

To avoid creating an adversarial culture, we can specify in project objectives the total removal of some kinds of technical debt. To ensure steady progress, develop a statement of objectives that includes complete retirement of at least one well-defined class of technical debt. Emphasize debt classes that have the highest anticipated MICs in the near term. Address other well-defined classes of technical debt on a best-effort basis.

We must accept as the “cost of doing business,” any other forms of technical debt that remain at the end of a given project. We must also accept that some artifacts might later become technical debt, even though they aren’t at present. We’ll get to them, but unfortunately, not this time.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[LIBCom 2006] “Work-to-rule: a guide.” libcom.org.

Available: here; Retrieved: May 9, 2017.

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Other posts in this thread

Self-sustaining technical knowledge deficits during contract negotiations

Last updated on July 7th, 2021 at 07:56 pm

Avoid technical knowledge deficits in important contract negotiations
Contract negotiations can be complex

Enterprises that grow by acquisition inevitably acquire the technological assets of the organizations they acquire. And most enterprises acquire technological assets by other means as well. The contract negotiation teams need technical knowledge to accurately project the effects of these acquisitions on enterprise technical debt. But as total enterprise technical debt grows, the capacity of enterprise technologists to support new contract negotiations declines. That leads to a self-sustaining cycle of technical knowledge deficits. Hiring more technologists is the way to break the cycle. And policymakers and strategic planners are likely the most effective possible advocates for adopting that solution.

Negotiating contracts with significant technological content relating to acquiring assets can require special expertise. In the asset acquisition process, assessing the technical debt status of the assets is often necessary. Such contracts include contracts with vendors that provide outsourcing services or subcontracting work, or contracts with organizations to be acquired. Moreover, the technical debt in question includes more than just the debt the asset carries as it stands in its pre-acquisition context. It also includes the debt that the asset will carry after insertion into the asset portfolio of the acquiring enterprise.

These two debts—pre-acquisition and post-acquisition—can differ. The main source of difference is that the interfaces, standards, and approaches of the acquiring organization likely differ from those prevailing within the vendor organization or the acquired organization. Making a valid assessment of the post-acquisition levels of technical debt requires knowledge of the interfaces, standards, and approaches of both organizations.

A vicious cycle

The enterprise negotiation team therefore requires the services of technologists. These technologists must be familiar with the modifications the acquired assets will need after the acquisition. But problems can arise. Specifically, resource contention occurs when technical debt levels in the enterprise reach a level so serious that debt service requires the full attention of all available technologists. If that happens negotiating contracts usually gets less attention. Contract negotiation teams then experience a deficit of knowledge concerning the consequences of acquiring assets laden with technical debt. That leads to increasing levels of nonstrategic technical debt. And that condition has the potential to exacerbate the technical knowledge deficit for the negotiating teams.

This situation is an example of a dynamic widely known as a vicious cycle. After technical debt has reached a critical level, there are really only two tactics that can break the cycle—get more engineers, or suspend some work.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[LIBCom 2006] “Work-to-rule: a guide.” libcom.org.

Available: here; Retrieved: May 9, 2017.

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Other posts in this thread

Where the misunderstandings about MICs come from

Last updated on July 8th, 2021 at 12:50 pm

The differences between technical debt and financial debt are numerous and significant. We often overlook them, in part, because of the metaphor itself. Managing technical debt as we would manage financial debt is risky for two reasons. First, such an approach would most likely fail to exploit properties of technical debt that can be advantageous. Second, such an approach would likely cause us to overlook opportunities because of reticence about addressing the technical debt problem to the extent necessary for effective control.

The right tool for the wrong job
Managing technical debt using approaches drawn from finance is analogous to using the right tool for the wrong job.
The debt metaphor itself is probably at the root of the misunderstanding. The financial metaphor evokes the conventional concept of fixed or slowly varying interest rates. But the reality of technical debt involves loss of enterprise agility or lost revenue. Connecting these two ideas is intuitively challenging.

For the more familiar kinds of financial debts, the interest rate and any rules for adjusting it are set at the time of loan origination. Moreover, financial debts are unitary in the sense that each loan is a single point transaction with a single interest rate formula. For example, the interest rate formula for the most common kind of credit card balance is the same for every purchase. Technical debt isn’t unitary. Each kind of technical debt and each manifestation of that kind of technical debt is, in effect, a separate loan that can carry its own independently variable MICs.

Last words

The cost of carrying technical debt can vary with time. It can vary for a given class of technical debt, or it can vary instance-by-instance. Costs depend on the nature of the work underway on the assets that carry the debt. This fact is a source of flexibility useful for planning technical debt management programs. To manage resources, planners can exploit this flexibility to set priorities for enterprise efforts. For example, planning technical debt retirement programs to retire all instances of a given class of technical debt might not be the best choice.

When making technical debt management decisions, respect the constraints that technical debt imposes. Exploit the flexibility that technical debt provides.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[LIBCom 2006] “Work-to-rule: a guide.” libcom.org.

Available: here; Retrieved: May 9, 2017.

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Related posts

MICs can change when other debts are retired

Last updated on July 8th, 2021 at 12:49 pm

The MICs and MPrin of a particular class of technical debt can change when we retire other seemingly unrelated classes of technical debt. In most cases, we need engineering expertise to determine technical debt retirement strategies that can exploit this property.

Financial debts usually have associated interest rates that are used to compute the periodic interest charges. Typically, the interest charge on a financial debt for a given period is the periodic interest rate multiplied by the principal, and then scaled for the length of the time period. But there are no “rates” for technical debt. Their existence would imply that MICs were proportional to the analog of “principal,” which, in the case of technical debt, is the cost of retiring the debt—the MPrin. MICs depend only weakly on the cost of retiring the debt. Instead, they depend more strongly on the impact of the debt on ongoing operations.

How financial sophistication can be a handicap

Decision makers who understand the world of financial instruments at a very sophisticated level might tend to make an understandable error. They might overvalue arguments favoring technical debt management in ways analogous to how we manage financial debts. Financial sophisticates might find appealing any argument for technical debt management that parallels financial approaches. Such programs are unlikely to work, for two reasons. First, the uncertainties associated with estimating MPrin and MICs are significant. They make technical debt management decisions more dependent on engineering and project management judgment than they are on the results of calculations and projections (see MPrin uncertainties and MICs uncertainties).

Second, the familiar concept of interest rate doesn’t apply to technical debt. For technical debt, the interest charges depend on the interaction between ongoing activities and the debt, rather than the cost of retiring the debt. And that means that MICs (and MPrin) of one class of debt can change when another class is retired.

Implications of this effect

The possibility that retiring one class of technical debt can alter the financial burdens presented by another has both favorable and unfavorable implications.

MICs can change when other debts are retired
An example illustrating one way in which MICs on one kind of technical debt can change as a result of retiring another. The structure at the left represents the situation before any debt retirement occurs. The balloons labeled “A” represent instances of asset A. The balloon labeled “B” represents asset B. The orange circles represent instances of technical debts D1 and D2. The arrows connecting the As to B indicate that asset A depends on Asset B. The structure at the right represents the situation after debt retirement.
As an example of a favorable implication, consider software remodularization. Suppose we have a software asset A that depends on another software asset B. As shown in the left image of the figure, asset A, of which there are many copies, bears two classes of technical debt, D1 and D2. As shown, there is only one copy of asset B. Suppose further that an asset that bears debt D2 also bears debt D1, but an asset that bears D1 might or might not bear debt D2.

To retire D2, engineers modified B by assigning it responsibility for the tasks that formerly bore debt D2. After this change, B bears debt of type D1. Next, they retired debt D2. The right half of the figure shows the result. The system still bears debt D1. But now it’s inside B instead of A. Those modifications relieve all instances of A of both types of debt. The result is that D2 vanishes, and only a single instance of D1 remains. In this way, retiring debt D2 has reduced the MICs and MPrin for D1.

Policymakers can help

Exploiting the salutary opportunities of this property of technical debt provides an example of the risks of adhering too closely to the financial model of debt.

Many different scenarios have the property that retiring one kind of technical debt can reduce the MICs associated with other kinds. Technologists understandably tend to be more concerned with technical debt retirement strategies that emphasize short-term improvement of their own productivity. That’s why it’s so important for policymakers to provide guidance that steers the organization in the direction of enterprise benefits.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[LIBCom 2006] “Work-to-rule: a guide.” libcom.org.

Available: here; Retrieved: May 9, 2017.

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Related posts

MICs on technical debt can be difficult to measure

Last updated on July 8th, 2021 at 12:48 pm

For financial debts, creditors regularly inform debtors of interest charges, principal remaining, and other loan parameters. Laws usually require reports and explicit statements about interest charges. By contrast, for technical debt, MICs can be difficult to compute with useful precision, even if we know they’re accumulating. Many decision makers are actually unaware that MICs are accumulating. For an organization to appreciate the full financial consequences of carrying technical debt, everyone in the enterprise must appreciate the concept of MICs.

The heart of the problem

A stack of floppy disks
A stack of floppy disks. You don’t see many of these around much anymore. Very little of the software or hardware we use is as obsolete as these floppies. But much of it is obsolete, and it therefore comprises technical debt. It still works, but it’s slow and probably out of support life. On the basis of speed alone, the MICs it incurs can easily justify replacement. And some of it is vulnerable to cyberattack. One significant breach can ruin a brand.
Unlike financial debt, for technical debt there are no legally required reports or disclosures. We can sometimes estimate MICs, but most organizations don’t track the data necessary to estimate MICs with useful precision. Indeed, developing useful estimates is often technically impossible.

The difficulty of measuring MICs arises from three sources. First, people whose productivity is most directly affected by technical debt—usually engineers—often have difficulty determining with precision the extent of the impact of technical debt on their efforts.

Second, many people are unaware of the impact technical debt has on their results. For example, if a product arrives late to market, we could compute the financial costs attributable to technical debt if we knew that technical debt is partially—or wholly—responsible for the delay. Too often, those who could perform such calculations aren’t sufficiently familiar with the concept of MICs. In any case, the data they would need for calculating a useful estimate is rarely available.

This brings us to the third source of difficulty.

The terrifying opportunity

Finally, a more insidious form of the consequences of technical debt is what we might call the terrifying opportunity. This situation arises when the organization rejects (or fails to recognize) a market opportunity because exploiting it would involve modifying an existing asset. The modification is terrifying because the asset harbors a heavy load of technical debt. The debt causes decision makers to assess that the probability of success is so low that the opportunity seems terrifying. They therefore reject the opportunity. Typically, terrifying opportunities would be exploitable if the debt-bearing assets didn’t exist at all. Then we would be starting fresh. But when opportunities require modifying assets that bear technical debt, commitment requires faith that we can address the technical debt successfully.

The sense of risk isn’t a reflection on the capabilities of the technical organization. Rather, it’s a result of the challenges involved in working with assets that bear high levels of technical debt. Given past performance of the technical organization relative to these debt-bearing assets, success can seem unlikely.

Computing the cost of a terrifying opportunity requires estimating the cost of not exploiting the opportunity, a difficult task in the best of circumstances. But whatever that cost is, it’s a form of MICs that we rarely recognize.

Last words

Building expertise in estimating MICs in all their forms is advantageous to any organization that seeks to make its technical debt more manageable. By making MICs visible, we can bring about better recognition of the cost of carrying technical debt, thereby providing an appropriate motivator for retiring technical debt.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[LIBCom 2006] “Work-to-rule: a guide.” libcom.org.

Available: here; Retrieved: May 9, 2017.

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Related posts

MICs can sometimes be deferred or advanced without penalty

Last updated on July 7th, 2021 at 03:11 pm

A rehabilitated Green Line car of the Massachusetts Bay Transit Authority
A rehabilitated Green Line car of the Massachusetts Bay Transit Authority. Trolley cars still travel on surface streets in and around Boston, in medians of divided roadways. Many streets still contain buried trolley tracks. They comprise a technical debt. MICs continue to accrue due to a near-continuous need to patch roadways. The need arises because of surface decomposition from the freeze-thaw cycle and small movements of buried tracks under traffic loads. A recent sewer upgrade project in Cambridge required removal of buried tracks to replace an old sewer line. This presented an opportunity to defer street surface maintenance (MICs) to take advantage of the surface rebuilding that was included in the sewer project. I don’t know whether the city actually exploited that opportunity.

Although rescheduling financial interest payments is possible only by special arrangement, or in bankruptcy, we can often defer or advance MICs on technical debt by rescheduling work that might incur them. For some kinds of technical debt, MICs accumulate only if we perform engineering work that involves that debt. This property is especially useful when we plan to retire an asset that bears technical debt. When we remove the asset from service, the technical debt it carries vanishes.

For most conventional financial debts, interest charges accumulate until we retire the debt. Interest charges might be zero for some time periods, but they’re never negative. Failure to meet the contractual payment schedule can result in penalties and additional interest charges.

MICs are different from financial interest charges

For technical debt, we can sometimes defer or advance MICs without penalty. We can arrange to temporarily nullify the MICs on a particular class of technical debt, or particular instances of that class. To do so, we need only reschedule a project or projects. This is possible when MICs accrue only if there is a need to perform work on the assets that carry the debt. In a given fiscal period, if we perform no work on those assets, the MICs can be zero. By scheduling projects accordingly, we can arrange for MICs to be zero.

There is one caveat. As I mentioned in “Debt contagion: how technical debt can create more technical debt,” as long as a particular class of technical debt remains in place, its associated MPrin might increase. Deferring retirement of a class of technical debt can be wise only if we can control its associated MPrin or if changes in its MPrin are acceptable.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[LIBCom 2006] “Work-to-rule: a guide.” libcom.org.

Available: here; Retrieved: May 9, 2017.

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Related posts

MICs can differ for different instances of the same kind of technical debt

Last updated on July 8th, 2021 at 12:46 pm

For financial debts, the interest charges associated with a unit of debt are (usually) the same for every unit of debt incurred under the same loan agreement. But for technical debt, the MICs associated with a given instance of a class of technical debt might differ from the MICs associated with any other instance of the same class of technical debt. They can differ even if we incurred those instances of technical debt at the same time. And they can differ even if they formed as results of a single decision or sequence of events. Unlike the transactions on a credit card, the interest charges can vary for instances of the same kind of technical debt.

Why MICs can differ from instance to instance

Collapse of the I-35W bridge in Minneapolis, Minnesota
The I-35W Bridge collapse, day 4, Minneapolis, Minnesota, August 5, 2007. Underweight gusset plate design made the bridge vulnerable due to the increased static load from concrete road surfacing additions. And it was especially vulnerable due to the weight of construction equipment and supplies during a repair project that was then underway. But the root cause of the failure was that the bridge was “fracture critical.” It was vulnerable to collapse if any one of a set of critical bridge members failed. The 18,000 fracture critical bridges in the U.S. were built (or are being built) because they’re cheaper than are bridges that have zero fracture critical members [CBS News 2013]. Expedient shortcuts are among the most prolific generators of technical debt. For bridges, the MICs could include inspections, repairs, and temporary closures for inspections and repairs. Variations of design from bridge to bridge clearly could create variations in MICs from bridge to bridge. Photo by Kevin Rofidal, United States Coast Guard,  courtesy Wikimedia Commons.
For most financial debts, a single algorithm determines the interest charges for every unit of a particular class of debt. Following the technical debt metaphor, people tend to assume that the MICs on every instance of a particular class of technical debt are uniform across the entire class.

But in practice, uniformity assumptions with regard to MICs are generally invalid. Given two different instances of the same kind of technical debt, the MICs associated with modifying asset components in and around those two instances can differ significantly. For any given instance of a particular class of technical debt, MICs can depend on whether engineers must interact with that part of the asset. And when they do interact with a given asset component, MICs can also depend upon the transparency and condition of that asset component.

Two examples illustrating varying MICs

For example, an instance of technical debt might reside in a setting that relatively few local experts understand. The people who are capable of doing that work might be in high demand, or heavily committed, or expensive. Subsequent scheduling difficulty can lead to delays or service interruptions associated with completing the required work. That can result in lost revenue, which also contributes to MICs. Meanwhile, instances of the same kind of technical debt residing in other parts of the asset might be addressable by less expert staff. They might be in lesser demand, and less well compensated. Service interruptions might be shorter, and lost revenue less. The MICs associated with these two cases can therefore differ significantly.

As a second example, consider documentation deficits. Suppose an engineer needs documentation to determine how to proceed, and that documentation doesn’t exist. The engineer must then resort to alternatives that might be more time-consuming. He or she might read code or specifications, or interview colleagues. But for two instances of the same kind of technical debt, the need to refer to documentation can differ. The engineer might need documentation for one instance in one part of the asset, but not for another.

Last words

Another form of documentation deficit can be especially costly. Suppose engineers need documentation, and it does exist, but it’s out of date or incorrect. Those engineers might make costly, potentially irreversible errors when they undertake maintenance or extension activity. When testing uncovers the defects the engineers unwittingly introduced due to the defective documentation, the damage is less. But if testing doesn’t catch the defects, they might somehow find their way into production. If they do, the revenue or liability impact can be substantial. And the impact can vary from instance to instance of the technical debt in question. These effects are all forms of MICs.

So MICs can vary almost on an instance-by-instance basis. Or they might be constant across instances. It’s difficult to say. But the easy assumption—that MICs are the same for all instances of a given class of technical debt—the easy assumption is probably incorrect.

References

[Blair 2017] Hunter Blair. “No free bridge: Why public–private partnerships or other ‘innovative’ financing of infrastructure will not save taxpayers money,” Economic Policy Institute blog, March 21, 2017.

Available: here; Retrieved: January 29, 2018

Cited in:

[Boehm 2016] Barry Boehm, Celia Chen, Kamonphop Srisopha, Reem Alfayez, and Lin Shiy. “Avoiding Non-Technical Sources of Software Maintenance Technical Debt,” USC Course notes, Fall 2016.

Available: here; Retrieved: July 25, 2017

Cited in:

[CBS News 2013] CBS News and the Associated Press. “Thousands of U.S. bridges vulnerable to collapse,” May 25, 2013.

Available: here; Retrieved: November 29, 2017

Cited in:

[LIBCom 2006] “Work-to-rule: a guide.” libcom.org.

Available: here; Retrieved: May 9, 2017.

Cited in:

[McConnell 2006] Steve McConnell. Software Estimation: Demystifying the Black Art. Microsoft Press, 2006.

Order from Amazon

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:

[NOAA 2013] NOAA/National Weather Service. “The March, 2010 Floods in Southern New England,” WFO Taunton Storm Series Report #2013-01, January 2013.

Available: here; Retrieved: January 30, 2018

Cited in:

[Weinberg 1992] Gerald M. Weinberg. Quality Software Management Volume 1: Systems Thinking. New York: Dorset House, 1989.

This volume contains a description of the “diagram of effects” used to explain how obstacles can induce toxic conflict. Order from Amazon

Cited in:

Related posts