The concept of MICs

Last updated on July 8th, 2021 at 11:52 am

Using the term interest to refer to the metaphorical interest charges of a technical debt is risky. The risk arises from confusing the properties of financial interest with the properties of the metaphorical interest charges on technical debt. Using an alternative term that makes the metaphor obvious can limit this risk. One such term is metaphorical interest charges, or for convenience, MICs.

Loose change
Loose change. The MICs on technical debt accumulate in two ways: (a) as “loose change,” namely, small bits of lost time, delays, and depressed productivity; and (b) as major blows to enterprise vitality in the form of lost revenue, delayed revenue, and missed market opportunities. Hard to say which category does more damage.
MICs aren’t interest charges in the financial sense. Rather, the MICs of a technical debt represent the total of reduced revenue, incidental opportunity costs, and increased costs of all kinds resulting from carrying that technical debt. (Actually, now that I think of it, MICs can include financial interest charges if we find it necessary to borrow money as a consequence of carrying technical debt.) Because the properties of MICs are very different from the properties of financial interest charges, we use the term MICs to avoid confusion with the term interest from the realm of finance.

What exactly are “metaphorical interest charges?”

Briefly, MICs are variable and often unpredictable [Allman 2012]. MICs differ from interest charges on financial debt for at least eight reasons. For any particular class of technical debt:

I examine each of these properties in more detail in the posts listed above.

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Also cited in:

Related posts

Glossary and Terminology

Last updated on July 16th, 2021 at 11:23 am

Even though technical debt has been with us for a very long time—probably since the time we began inventing technologies—the study of technical debt is relatively new. Ward Cunningham coined the term technical debt in 1992, and its meaning has evolved since then. Because universally accepted definitions for the term and associated concepts have not yet emerged, it seems necessary to have a page on this site that collects definitions.

Asset-exogenous technical debt

Exogenous technical debt is asset-exogenous when it’s brought about by an activity external to an asset, but internal to the enterprise. For example, a change in standards or regulations by some body within the enterprise can cause an asset to incur an asset-exogenous technical debt.

ATD

See Auxiliary technical debt.

Auxiliary technical debt

In the context of a Technical Debt Retirement Project (DRP) that has as an objective retiring from a specified set of assets a particular kind or particular kinds of technical debt, the ATD is the collection of instances of any other kinds of technical debt other than the kind that the DRP is trying to retire. More: “Auxiliary technical debt: Rules of engagement

Class of technical debt

On occasion, we speak of classes of technical debt and instances of that class. This can be confusing, because the words class and instance have particular meanings in software engineering. That’s not the sense in which we use the terms here. In this blog, a class of technical debt is just a collection of instances of the same kind of debt. For example, consider the “ghost ramp” described in “Technical debt in the highway system.” It belongs to the class of ghost ramps. If we were maintaining the highway system of Massachusetts, it might be convenient to consider the class of ghost ramp technical debt if we want to let a contract to demolish all ghost ramps. Each ghost ramp would then be an instance of that class.

Cognitive bias

A cognitive bias is the human tendency to make systematic errors based not on evidence, but on factors related to the thought process. Psychologists have identified and demonstrated hundreds of cognitive biases, including several that could plausibly explain failures in priority setting for technical debt retirement projects.

Confirmation bias

Confirmation bias is a cognitive bias. It’s the human tendency to favor and seek only information that confirms our preconceptions, or to avoid information that disconfirms them. For example, the homogeneity of cable news channel audiences, and the alignment between preconceptions of the audience and the slant of the newscast for that channel, are results of confirmation bias. More: “Confirmation bias and technical debt

Debt contagion

If a class of technical debt is allowed to remain outstanding, its volume can increase as a consequence of seemingly unrelated actions or decisions. Moreover, its existence can cause increases in the volume of other existing classes of technical debt, and its existence can lead to the formation of new classes of technical debt. This process is called debt contagion. More: “Debt contagion: how technical debt can create more technical debt

DRP

A (technical) Debt-Bearing Asset

DRP

In this blog, I use the term DRP to mean a (technical) Debt Retirement Project. A DRP is a project that has as an objective retiring from a specified set of assets a particular kind of technical debt (or particular kinds of technical debt). Many projects have objectives of debt retirement, at some point or other. But DRPs differ from most, in that debt retirement is their primary objective—indeed, it might be their sole objective. More: “Nine indicators of wickedness

Echo release

An echo release of an asset is a release version whose primary purpose is technical debt retirement. Typically, it’s created immediately following a release version that has created some incremental technical debt, hence the term “echo release.” The echo release is then executed to retire that incremental technical debt, and not to repair defects or add capability. More: “Accounting for technical debt

Endogenous technical debt

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 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 activity or decisions that directly involve the asset. More: “Exogenous technical debt

Enterprise-exogenous technical debt

Exogenous technical debt is enterprise-exogenous when it’s brought about by an activity external to the enterprise. For example, a change in standards or regulations by some body outside the enterprise can cause an asset to incur an enterprise-exogenous technical debt.

Exogenous technical debt

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 activity or decisions that don’t involve the asset directly. More: “Exogenous technical debt

Ill-structured problem

An ill-structured problem is a problem that isn’t a well-structured problem [Simon 1973]. An example of an ill-structured problem is finding a definition for ill-structured problems. Another: designing a computer programming language. Still another, even more to the point: deciding when to retire a particular class of technical debt. NDM is more likely to be successful with ill-structured problems than is RDM.

Incremental technical debt

Incremental technical debt is either newly incurred exogenous technical debt, or technical debt that’s incurred in the course of work currently underway or just recently completed. For example, in an apartment building hallway renovation project, workmen did insert expansion joints in the sheetrock they replaced, but on the first three floors they completed, the joints were too widely separated. The remaining 22 floors were done correctly. Nine additional joints on each of the incorrect floors must be inserted eventually. The missing joints, which constitute incremental technical debt, will be inserted after the job is completed. More: “Controlling incremental technical debt

Instance of technical debt

See “Class of technical debt

Intertemporal choice

Intertemporal choice is the process by which people make decisions between options that occur at different points in time. Decisions involving intertemporal choice can be exceedingly complex, especially when options have effects that vary with time. For example, confronted with advice from technical experts regarding the urgent need to address the burden of enterprise technical debt, decision makers must consider an unpleasant possibility. To make resources available to retire the technical debt, it might be necessary to temporarily defer investment in some new products or enhancing some existing products. And if they make the recommended investments in technical debt retirement, customers won’t benefit in any visible way. So the choice reduces to one between new products and enhancements relatively sooner, versus retiring technical debt and only later attending to new products and enhancements of existing products. This dilemma is an example of what behavioral economists call intertemporal choice [Loewenstein 1992].

Key Performance Indicator (KPI)

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. More: “Metrics for technical debt management: the basics

Legacy technical debt

Legacy technical debt is technical debt associated with an asset, and which exists 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, Management discovers 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, and reduce the cost of eventual demolition. More: “Exogenous technical debt

Localizable technical debt

Localizable technical debt is technical debt that manifests itself as discrete chunks. Each instance is self-contained, and we can “point” to it as an instance of the debt in question. For example, if the organization regards Windows 10 as the current operating system for personal computers, and early versions of Windows as technical debt, the each computer that runs and earlier version of Windows is an instance of that technical debt. Each instance is discrete and localized. More: “Retiring localizable technical debt

Measure

A measure is the result of determining the value of a quantifier. For example, we might use the quantifier’s definition to determine a measure of how much human effort has been expended on an asset in the past fiscal quarter. More: “Metrics for technical debt management: the basics

Metric

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. More: “Metrics for technical debt management: the basics

MICs, or metaphorical interest charges

MICs are the metaphorical interest charges associated with a technical debt. They aren’t interest charges in the financial sense; rather, the MICs of a technical debt represent the total of reduced revenue, lost opportunities, and increased costs of all kinds borne by the enterprise as a consequence of carrying that technical debt. Because the properties of MICs are very different from the properties of financial interest charges, we use the term MICs to avoid confusion with the term interest from the realm of finance. More: “How financial interest charges differ from interest charges on technical debt

MPrin, or metaphorical principal

The MPrin of a technical debt at a give time T is the total cost of retiring that debt at time T. The total cost includes all cost factors: labor, equipment, service interruptions, revenue delays, anything. It even includes the ongoing costs of repairing defects introduced in the debt retirement process. More: “The metaphorical principal of a technical debt

Naturalistic decision-making

Naturalistic decision-making (NDM) entails situation assessment and evaluation of a single option to select a satisfactory option. [Zannier 2007] Features that define naturalistic decision-making are “time pressure, high stakes, experienced decision makers, inadequate information (information that is missing, ambiguous, or erroneous), ill-defined goals, poorly defined procedures, cue learning, context (e.g., higher-level goals, stress), dynamic conditions, and team coordination.”  [Klein 2017]

Nonstrategic technical debt

Nonstrategic technical debt is technical debt that appears in the asset without strategic purpose. We tend to introduce nonstrategic technical debt by accident, or as the result of urgency, or from changes in standards, laws, or regulations—almost any source other than asset-related engineering purposes. And at times, it appears in the asset as a result of external events beyond the boundaries of the enterprise. More: “Nontechnical precursors of nonstrategic technical debt

The planning fallacy

The planning fallacy is a cognitive bias that causes planners to underestimate costs and schedules, and over-promise benefits. They do this, in part, because they pay too little heed to past experience on similar efforts. They also rely too much on what they believe will happen on the effort they’re planning. First identified in a 1977 report by Daniel Kahneman and Amos Tversky [Kahneman 1977] [Kahneman 1979]. More: “Unrealistic optimism: the planning fallacy and the n-person prisoner’s dilemma

Policy

Organizational policy is the framework of principles that guide policymakers, decision makers, and everyone in the organization as they carry out their responsibilities. Policy might be written or not, but written policy is more likely to consistently adhered to. Interestingly, the body of organizational policy is itself subject to accumulating technical debt. More: “What is policy?

Policymaker

As I use the term in this blog, a policymaker is someone who is responsible for developing, revising, or approving organizational policies that affect technical debt management. More: “Who are the policymakers?

Quantifier

A quantifier is a specification for a measurement process designed to yield a numeric representation of some attribute of an asset or process. Quantifiers are used to obtain the values called measures, which in turn are used in computing metrics. More: “Metrics for technical debt management: the basics

Rational decision-making

Rational decision-making (RDM) is an approach to making a choice of an option from among a set of options by selecting the option that is optimal with respect to a set of quantitative criteria. [Zannier 2007] Rational choice strategies generally follow this framework: (1) Identify a set of options; (2) Identify criteria for evaluating them; (3) Assign weight to each evaluation criterion; (4) Rate the options relative to the criteria; (5) Choose the option with the highest score. Many different frameworks for implementing this strategy are available, some specialized to specific subject domains [Thokala 2016].

Refactoring

Fowler defines refactoring as “the process of changing a software system in such a way that it does not alter the external behavior of the code yet improves its internal structure” [Fowler 1999]. Although refactoring is a term specific to software development processes, the concept applies to all technological development. For example, an automobile manufacturer’s decision to alter the design of one of their model vehicles to reduce manufacturing costs can be viewed as a form of refactoring. Refactoring is a practice essential to effective technical debt management. More: “Refactoring for policymakers

Regression testing

Regression testing is a testing regimen that ensures that a previously developed and tested system still performs the same way after it has been altered or when it’s used in a new context. Regression testing is essential when we alter a system by retiring some of its technical debt.

The reification error

The reification error (also called the reification fallacy, concretism, or the fallacy of misplaced concreteness) 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 use it in the domain of logical reasoning, troubles can arise. Specifically, we can encounter trouble when we think of “measuring” technical debt. Strictly speaking, we cannot measure technical debt. We can estimate the cost of retiring it, but estimates are only approximations. And in the case of technical debt, the approximations are usually fairly rough. To regard these estimates as measurements is to risk reifying them. Then when the actual cost of a debt retirement project is dramatically larger than the estimate, the consequences for enterprise budgets can be severe. We must always regard “measurements” of technical debt as estimates—estimates that are so prone to error that we must plan for error.  The reification error is the dual of the resilience error. More: “Metrics for technical debt management: the basics.”

The resilience error

If the reification error is an error of reasoning in which we treat an abstraction as if it were a real, concrete, physical thing, the resilience error is an error of reasoning in which we treat an abstraction as if it were more flexible, resilient, and adaptable than it actually is. When we commit the resilience error with respect to an abstraction, we’re adopting a belief, usually without justification, and possibly outside our awareness. That belief is that the familiar properties of the abstraction can survive changes in the abstraction.

Specifically, if we make changes in the abstraction, we can be certain that the familiar properties of the abstraction we modified will apply in modified form. We hold this belief without fully investigating the consequences of the changes we made in the abstraction. Or we assume incorrectly that the abstraction will accommodate any changes we make to its environment. The resilience error is the dual of the reification error. We are at risk of making the resilience error when we refactor assets to reduce their burden of technical debt. More: “The resilience error and technical debt.”

Secured technical debt

A secured technical debt, like a secured financial debt, is one for which the enterprise has reserved 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. They might include particular staff, equipment, test beds, downtime, and financial resources. 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. More: “Using SMART goals for technical debt reduction

Source and target components of a metaphor

In a metaphor of the form “A is B,” the source is the element whose attributes are being attributed to the target. For example, in “my son’s room is a war zone,” the source is the war zone, and the target is my son’s room.  More: “The structure of metaphors

Super wicked problem

A subset of wicked problems can be viewed as super wicked [Levin 2012]. Levin, et al. list the following four properties of super wicked problems: (1) Time is running out; (2) Those who cause the problem also seek to provide a solution or influence the solution; (3) The central authority needed to address the problem is weak, nonexistent, or chooses not to act effectively; (4) Policy responses discount the future irrationally. I’ve come to believe that some technical debt retirement project design can be a super wicked problem. More: “Retiring technical debt can be a super wicked problem

Tame problems

A problem is a tame problem if it fails to meet at least one of the ten criteria established by Rittel and Webber [Rittel 1973] for wicked problems. Four of the criteria: it’s an ill-structured problem; it’s incompletely defined or internally contradictory; its solutions aren’t true-or-false, but good-or-bad; and there’s no way to exhaustively describe all solutions. I’m convinced that technical debt retirement project design can be a wicked problem. A tame problem is one that fails to meet at least one of the ten criteria for wickedness. Tame problems and wicked problems thus lie at opposite ends of a “Tame/Wicked” spectrum. Technical debt retirement project design problems fall somewhere on this spectrum. More: “Degrees of wickedness.”

Taylorism

Taylorism is an approach to management developed by Frederick Winslow Taylor in the early part of the twentieth century [Taylor 1913] [Kanigel 1997]. He proposed three principles of scientific management that could produce maximum efficiency. First, managers should select the person performing the work based on science. Second, organizations should decompose tasks based on scientific principles. Third, they must separate planning from execution. These principles are the basis of what became known in software engineering as the waterfall lifecycle. The approach works well for well-structured problems, but does not work well at all for ill-structured problems. Moreover, it depends for success on repeating solutions to problems already solved, which is why it proved so valuable in early manufacturing. The unsuitability of Taylorism for ill-structured problems is an important part of the basis for the Agile approach to problem solving.

TDIQ

In the context of a Technical Debt Retirement Project (DRP), we can define the Technical Debt In Question (TDIQ). If the DRP has as an objective retiring a kind of technical debt, that kind of technical debt is the TDIQ. More: “Retiring technical debt from irreplaceable assets

Technical debt

Technical debt is any technological element that hampers development, maintenance, or enhancement efforts, through its existence or through its absence. It contributes to lower productivity or to a higher probability of defects. Or it can depress velocity in many other ways. That’s why we would like to revise, repair, replace, rewrite, create, or re-engineer it for sound engineering reasons. It can be found in—or it can be missing from—software, hardware, processes, procedures, practices, or any associated artifact, acquired by the enterprise or created within it. More: “A policymaker’s definition of technical debt

Technological communication risk

Technological communication risk is the risk that, for whatever reason, knowledgeable people within the enterprise don’t communicate important knowledge to the people who need it, or the people who need it aren’t receptive to it. More: “Technological communication risk

Temporal discounting

Temporal discounting is the human tendency to give greater value to a reward (or as economists would say, to assign greater utility to a good) the earlier it arrives. An analogous process affects perceptions of inconvenience or disutility: people assign more negative values to penalties and inconveniences the sooner they arrive. If the discount rate is constant, the discounting is termed exponential discounting or rational discounting. But other forms are possible. Hyperbolic discounting is one form of discounting at a rate that is higher for near-term arrivals than for distant-term arrivals [Laibson 1997]. Humans have been observed experimentally to favor a form of temporal discounting that is well modeled as hyperbolic discounting.

Terrifying opportunity

A terrifying opportunity arises when the organization rejects (or fails to recognize) a market opportunity because exploiting it would involve modifying an existing asset or product offering that 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, and they therefore reject the opportunity. More: “MICs on technical debt can be difficult to measure

Well-structured problem

As defined by Simon [Simon 1973], a well-structured problem is a problem that has some or all of six characteristics. The first is the existence of a definite criterion for testing any proposed solution, and a mechanizable process for applying that criterion. Second, there is at least one problem space in which we can represent the initial problem state, the goal state, and all states that can be reached or considered while solving the problem. There are four more criteria, but these are the biggies. An example of a well-structured problem is the game of chess. RDM is useful for attacking well-structured problems.

Wicked problem

A problem is a wicked problem if it meets the ten criteria established by Rittel and Webber [Rittel 1973]. Four of the criteria: it’s an ill-structured problem; it’s incompletely defined or internally contradictory; its solutions aren’t true-or-false, but good-or-bad; and there’s no way to exhaustively describe all solutions. I’m convinced that technical debt retirement project design can be a wicked problem. More: “Self-sustaining technical knowledge deficits during contract negotiations.”

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Also cited in:

[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).

Order from Amazon

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kanigel 1997] Robert Kanigel. The one best way: Frederick Winslow Taylor and the enigma of efficiency. Viking Penguin, 1997.

Order from Amazon

Cited in:

[Klein 2017] Gary Klein. Sources of Power: How People Make Decisions, 20th Anniversary Edition. Cambridge, Massachusetts: The MIT Press, 1999.

Order from Amazon

Cited in:

[Laibson 1997] David Laibson. “Golden eggs and hyperbolic discounting,” Quarterly Journal of Economics 112:2, 1997, 443-477.

Available: here; Retrieved: October 25, 2018

Cited in:

[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.

Available: here; Retrieved: October 17, 2018

Cited in:

[Loewenstein 1992] George Loewenstein and Drazen Prelec. “Anomalies in Intertemporal Choice: Evidence and an Interpretation,” Quarterly Journal of Economics, 57:2, 1992, 573-598.

Available: here; Retrieved: October 12, 2018

Cited in:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

[Simon 1973] Herbert A. Simon. “The Structure of Ill Structured Problems,” Artificial Intelligence 4, 1973, 181-201.

Available: here; Retrieved: 10/16/18

Cited in:

[Taylor 1913] Frederick Winslow Taylor. The Principles of Scientific Management. New York: Harper & Brothers, 1913.

Available: here; Retrieved: October 16, 2018 Order from Amazon

Cited in:

[Thokala 2016] Praveen Thokala, Nancy Devlin, Kevin Marsh, Rob Baltussen, Meindert Boysen, Zoltan Kalo, Thomas Longrenn et al. “Multiple Criteria Decision Analysis for Health Care Decision Making—An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force,” Value in Health 19:1, 2016, 1-13.

Available: here; Retrieved: 10/16/18

Cited in:

[Zannier 2007] Carmen Zannier, Mike Chiasson, and Frank Maurer. “A model of design decision making based on empirical results of interviews with software designers,” Information and Software Technology 49, 2007, 637-653.

Available: here; Retrieved October 15, 2018

Cited in:

A policymaker’s definition of technical debt

Last updated on July 18th, 2021 at 03:32 pm

Servers like the ones that made this page available to you. Most servers would benefit from comparison with a policymaker’s definition of technical debt
Shown here are servers like the ones that made this page available to you. Cybersecurity is concerned with defending servers like these, among others.

Policymakers have in mind the best interests of the entire enterprise. They need a definition of technical debt that’s neutral relative to its causes and manifestations. Defining technical debt in terms of what caused it or where it lies in the enterprise could compromise that necessary neutrality.

That neutrality is important because it enables us to recognize technical debt in whatever form it takes. For example, suppose enterprise policy assumes that technical debt lies only in software. And suppose that the root causes of some instances of technical debt are new threats in the cybersecurity environment that render obsolete our cyberdefenses. Then enterprise policy vis-à-vis technical debt is likely to be ineffective. It might lead to decision makers focusing too much attention on the software development process and too little attention on the cybersecurity and threat intelligence processes.

A definition that’s useful for guiding policy

Here’s a cause-neutral and manifestation-neutral definition of technical debt. It’s what I call the policymaker’s definition [Brenner 2017a]:

Technical debt is any technological element that contributes, through its existence or through its absence, to lower productivity or to a higher probability of defects during development, maintenance, or enhancement efforts, or which depresses velocity in some other way. It is therefore something we would like to revise, repair, replace, rewrite, create, or re-engineer for sound engineering reasons. It can be found in—or it can be missing from—software, hardware, processes, procedures, practices, or any associated artifact, acquired by the enterprise or created within it.

Extending the technical debt metaphor just a bit, people often talk about the principal and the interest charges associated with a technical debt. These ideas are analogous to the principal and interest charges associated with a financial debt. They’re convenient concepts, but the parallels between finance and technology aren’t real, and that’s where the trouble lies. Read more

An important extension beyond conventional definitions

There’s one other generalization contained in this definition of technical debt that differs from most other definitions. It’s in the phrase “or missing from.” Our policymaker’s definition doesn’t require that the technical debt item actually exist. That is, the absence of something can constitute technical debt. My favorite example is one due to Ken Pugh, who defines acceptance test debt as “…the nonexistence or nonautomation of acceptance tests.” [Pugh 2010] If we want to include all sources of reduced organizational agility or unnecessary operating expense arising from technical debt, our definition must also address non-existence issues like those Pugh has identified.

The definition above is workable for systems of all kinds. Consider two examples of “hardware”:

But the definition also applies to anything that takes a technological form, including business plans, legislation, procedures, and microprocessor designs—almost anything.

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Also cited in:

[Brenner 2017a] Richard Brenner. “A Policy Maker’s Definition of Technical Debt,” Cutter Consortium Executive Update, February 27, 2017.

Cited in:

[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).

Order from Amazon

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kanigel 1997] Robert Kanigel. The one best way: Frederick Winslow Taylor and the enigma of efficiency. Viking Penguin, 1997.

Order from Amazon

Cited in:

[Klein 2017] Gary Klein. Sources of Power: How People Make Decisions, 20th Anniversary Edition. Cambridge, Massachusetts: The MIT Press, 1999.

Order from Amazon

Cited in:

[Laibson 1997] David Laibson. “Golden eggs and hyperbolic discounting,” Quarterly Journal of Economics 112:2, 1997, 443-477.

Available: here; Retrieved: October 25, 2018

Cited in:

[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.

Available: here; Retrieved: October 17, 2018

Cited in:

[Loewenstein 1992] George Loewenstein and Drazen Prelec. “Anomalies in Intertemporal Choice: Evidence and an Interpretation,” Quarterly Journal of Economics, 57:2, 1992, 573-598.

Available: here; Retrieved: October 12, 2018

Cited in:

[Pugh 2010] Ken Pugh. “The Risks of Acceptance Test Debt,” Cutter Business Technology Journal, October 2010, 25-29.

Cited in:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

[Simon 1973] Herbert A. Simon. “The Structure of Ill Structured Problems,” Artificial Intelligence 4, 1973, 181-201.

Available: here; Retrieved: 10/16/18

Cited in:

[Taylor 1913] Frederick Winslow Taylor. The Principles of Scientific Management. New York: Harper & Brothers, 1913.

Available: here; Retrieved: October 16, 2018 Order from Amazon

Cited in:

[Thokala 2016] Praveen Thokala, Nancy Devlin, Kevin Marsh, Rob Baltussen, Meindert Boysen, Zoltan Kalo, Thomas Longrenn et al. “Multiple Criteria Decision Analysis for Health Care Decision Making—An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force,” Value in Health 19:1, 2016, 1-13.

Available: here; Retrieved: 10/16/18

Cited in:

[Zannier 2007] Carmen Zannier, Mike Chiasson, and Frank Maurer. “A model of design decision making based on empirical results of interviews with software designers,” Information and Software Technology 49, 2007, 637-653.

Available: here; Retrieved October 15, 2018

Cited in:

Related posts

Technical debt in software engineering

Ward Cunningham, who coined the technical debt metaphor

Last updated on July 7th, 2021 at 10:47 am

Ward Cunningham, who coined the technical debt metaphor
Ward Cunningham, who coined the technical debt metaphor. Photo (cc) Carrigg Photography.

Ward Cunningham coined the technical debt metaphor in the context of developing a software asset [Cunningham 1992] [Cunningham 2011]. He observed that when the development process leads to new learning, re-executing the development project—or parts of the project—could lead to a better result. For this reason, among others, newly developed operational software assets can contain or depend upon artifacts he regarded as technical debt. In hindsight, the developers recognize they could remove these artifacts altogether. Or they could replace them with more elegant, effective, or appropriate forms. In this way, they could enhance maintainability, defensibility, and extensibility. To deploy the asset in pre-hindsight condition would entail an obligation to return to it in the future to implement the improvements. That obligation is Cunningham’s conception of the asset’s technical debt.

Fowler’s technical debt quadrant
Fowler’s technical debt quadrant. Intentionality is the vertical axis; Wisdom is horizontal.

In the decades since Cunningham coined the term, the meaning of technical debt has evolved. It now includes much more than Cunningham’s original concept. Martin Fowler developed a 2×2 matrix (which I interpret as Intentionality x Wisdom) that describes four different pathways that lead to technical debt creation. Cunningham’s concept corresponds to what Martin Fowler describes as, “now we know how we should have done it” [Fowler 2009].

At a conference in Dagstuhl, Germany (“Managing Technical Debt in Software Engineering”) in 2016, leading experts in software technical debt research developed a verbal definition of technical debt for software-intensive systems [Avgeriou 2016]:

In software-intensive systems, technical debt is a collection of design or implementation constructs that are expedient in the short term, but set up a technical context that can make future changes more costly or impossible. Technical debt presents an actual or contingent liability whose impact is limited to internal system qualities, primarily maintainability and evolvability.

With the definition of technical debt enlarged in this way, software engineers can focus on instances of software technical debt that reduce enterprise productivity and agility. But is this definition sufficient as a foundation for enterprise policy? I explore that question in “A policymaker’s definition of technical debt.”

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Also cited in:

[Avgeriou 2016] Paris Avgeriou, Philippe Kruchten, Ipek Ozkaya, and Carolyn Seaman, eds. “Managing Technical Debt in Software Engineering,” Dagstuhl Reports, 6:4, 110–138, 2016.

Available: here; Retrieved: March 10, 2017.

Cited in:

[Brenner 2017a] Richard Brenner. “A Policy Maker’s Definition of Technical Debt,” Cutter Consortium Executive Update, February 27, 2017.

Cited in:

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

Cited in:

[Cunningham 2011] Ward Cunningham. “Ward Explains Debt Metaphor” (video; here; ).

Cited in:

[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).

Order from Amazon

Cited in:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kanigel 1997] Robert Kanigel. The one best way: Frederick Winslow Taylor and the enigma of efficiency. Viking Penguin, 1997.

Order from Amazon

Cited in:

[Klein 2017] Gary Klein. Sources of Power: How People Make Decisions, 20th Anniversary Edition. Cambridge, Massachusetts: The MIT Press, 1999.

Order from Amazon

Cited in:

[Laibson 1997] David Laibson. “Golden eggs and hyperbolic discounting,” Quarterly Journal of Economics 112:2, 1997, 443-477.

Available: here; Retrieved: October 25, 2018

Cited in:

[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.

Available: here; Retrieved: October 17, 2018

Cited in:

[Loewenstein 1992] George Loewenstein and Drazen Prelec. “Anomalies in Intertemporal Choice: Evidence and an Interpretation,” Quarterly Journal of Economics, 57:2, 1992, 573-598.

Available: here; Retrieved: October 12, 2018

Cited in:

[Pugh 2010] Ken Pugh. “The Risks of Acceptance Test Debt,” Cutter Business Technology Journal, October 2010, 25-29.

Cited in:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

[Simon 1973] Herbert A. Simon. “The Structure of Ill Structured Problems,” Artificial Intelligence 4, 1973, 181-201.

Available: here; Retrieved: 10/16/18

Cited in:

[Taylor 1913] Frederick Winslow Taylor. The Principles of Scientific Management. New York: Harper & Brothers, 1913.

Available: here; Retrieved: October 16, 2018 Order from Amazon

Cited in:

[Thokala 2016] Praveen Thokala, Nancy Devlin, Kevin Marsh, Rob Baltussen, Meindert Boysen, Zoltan Kalo, Thomas Longrenn et al. “Multiple Criteria Decision Analysis for Health Care Decision Making—An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force,” Value in Health 19:1, 2016, 1-13.

Available: here; Retrieved: 10/16/18

Cited in:

[Zannier 2007] Carmen Zannier, Mike Chiasson, and Frank Maurer. “A model of design decision making based on empirical results of interviews with software designers,” Information and Software Technology 49, 2007, 637-653.

Available: here; Retrieved October 15, 2018

Cited in:

What is policy?

Last updated on July 8th, 2021 at 11:46 am

DuBrin defines policies as “… general guidelines to follow when making decisions and taking action” [DuBrin 2016]. Some policies are written, some are unwritten. Some have names or identifiers, some don’t. For organizations seeking to gain control of technical debt, written policies are probably a good idea, for two reasons:

  • Many people affected by technical debt policies are probably unfamiliar with the technical debt concept. A written policy is more likely to be communicated uniformly to everyone.
  • The effort spent devising a written policy is likely to surface ambiguities, confusions, and differing needs. That’s one of the benefits of devising written policies. Resolving these issues during the policy formation process is better for the organization than resolving them during the deployment process.

A useful policy is clear. It uses terminology that’s simple and well-defined.

Properties of technical debt policy

Achieving these goals for technical debt policy formulation can present special problems. Much of the audience for the policy is unaware or incredulous of the connection between their own behavior and technical debt formation.

Specifically:

  • The policy must address an issue—technical debt—that has mainly technological manifestations
  • The policy must provide guidance for people as they make choices
  • Some of the choices that people make will produce technological manifestations
  • The connections between the choices and the technological manifestations can be obscure, especially
    when the choices don’t appear to be technical
  • Some technical debt arises from phenomena unrelated to behavior of anyone within the organization

Said differently, technical debt policy must confront four issues:

  • Some people whose behavior we must modify are unaware of the consequences of their behavior
  • Some of those same people strongly believe that the technical debt problem results from malpractice by others
  • Current incentive structures play an important role in technical debt formation
  • Some technical debt arises from phenomena external to the organization

Effective technical debt policy must therefore include these elements:

  • An education component to help people see the connection between their choices and technical debt formation
  • A situational awareness component to alert the organization to internal and external developments that could cause technical debt formation
  • A means of allocating resources to technical debt management that holds accountable the business units whose actions contributed to technical debt formation
  • A means of committing future resources to technical debt retirement

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Also cited in:

[Avgeriou 2016] Paris Avgeriou, Philippe Kruchten, Ipek Ozkaya, and Carolyn Seaman, eds. “Managing Technical Debt in Software Engineering,” Dagstuhl Reports, 6:4, 110–138, 2016.

Available: here; Retrieved: March 10, 2017.

Cited in:

[Brenner 2017a] Richard Brenner. “A Policy Maker’s Definition of Technical Debt,” Cutter Consortium Executive Update, February 27, 2017.

Cited in:

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

Cited in:

[Cunningham 2011] Ward Cunningham. “Ward Explains Debt Metaphor” (video; here; ).

Cited in:

[DuBrin 2016] Andrew J. DuBrin. Essentials of Management, Tenth Edition. Indianapolis, Indiana: Wessex Press, 2016.

Order from Amazon

Cited in:

[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).

Order from Amazon

Cited in:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kanigel 1997] Robert Kanigel. The one best way: Frederick Winslow Taylor and the enigma of efficiency. Viking Penguin, 1997.

Order from Amazon

Cited in:

[Klein 2017] Gary Klein. Sources of Power: How People Make Decisions, 20th Anniversary Edition. Cambridge, Massachusetts: The MIT Press, 1999.

Order from Amazon

Cited in:

[Laibson 1997] David Laibson. “Golden eggs and hyperbolic discounting,” Quarterly Journal of Economics 112:2, 1997, 443-477.

Available: here; Retrieved: October 25, 2018

Cited in:

[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.

Available: here; Retrieved: October 17, 2018

Cited in:

[Loewenstein 1992] George Loewenstein and Drazen Prelec. “Anomalies in Intertemporal Choice: Evidence and an Interpretation,” Quarterly Journal of Economics, 57:2, 1992, 573-598.

Available: here; Retrieved: October 12, 2018

Cited in:

[Pugh 2010] Ken Pugh. “The Risks of Acceptance Test Debt,” Cutter Business Technology Journal, October 2010, 25-29.

Cited in:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

[Simon 1973] Herbert A. Simon. “The Structure of Ill Structured Problems,” Artificial Intelligence 4, 1973, 181-201.

Available: here; Retrieved: 10/16/18

Cited in:

[Taylor 1913] Frederick Winslow Taylor. The Principles of Scientific Management. New York: Harper & Brothers, 1913.

Available: here; Retrieved: October 16, 2018 Order from Amazon

Cited in:

[Thokala 2016] Praveen Thokala, Nancy Devlin, Kevin Marsh, Rob Baltussen, Meindert Boysen, Zoltan Kalo, Thomas Longrenn et al. “Multiple Criteria Decision Analysis for Health Care Decision Making—An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force,” Value in Health 19:1, 2016, 1-13.

Available: here; Retrieved: 10/16/18

Cited in:

[Zannier 2007] Carmen Zannier, Mike Chiasson, and Frank Maurer. “A model of design decision making based on empirical results of interviews with software designers,” Information and Software Technology 49, 2007, 637-653.

Available: here; Retrieved October 15, 2018

Cited in:

Related posts

Who are the policymakers?

Last updated on June 12th, 2021 at 02:20 pm

Organizational policy is the framework of principles that guide policymakers, decision makers, and everyone in the organization as they carry out their responsibilities. As I use the term in this blog, a policymaker is someone who’s responsible for developing, revising, or approving organizational policies. The group of policymakers also includes those who contribute to the policy development process at the content level.

Some organizational policies that don’t mention technical debt explicitly can affect the way the organization manages technical debt. For this reason, all policymakers potentially affect to technical debt management. See, for example, “Performance management systems and technical debt.” And this creates problems for organizations as they confront the problem of controlling the formation of technical debt.

The problem is that many policymakers are unaware that their work affects the rate of formation of technical debt. Many would deny it, some vociferously. Programs for managing technical debt must address the problem of educating policymakers as to their role in technical debt formation.

That’s why in this blog I try to address the needs of all policymakers relative to the effects of their decisions on technical debt management. You’ll find relatively little technical content here. But you’ll also find here find discussions of behaviors, biases, and organizational structures that aren’t usually present in discussions of technical debt management.

References

[Allman 2012] Eric Allman. “Managing Technical Debt: Shortcuts that save money and time today can cost you down the road,” ACM Queue, 10:3, March 23, 2012.

Available: here; Retrieved: March 16, 2017

Also cited in:

[Avgeriou 2016] Paris Avgeriou, Philippe Kruchten, Ipek Ozkaya, and Carolyn Seaman, eds. “Managing Technical Debt in Software Engineering,” Dagstuhl Reports, 6:4, 110–138, 2016.

Available: here; Retrieved: March 10, 2017.

Cited in:

[Brenner 2017a] Richard Brenner. “A Policy Maker’s Definition of Technical Debt,” Cutter Consortium Executive Update, February 27, 2017.

Cited in:

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

Cited in:

[Cunningham 2011] Ward Cunningham. “Ward Explains Debt Metaphor” (video; here; ).

Cited in:

[DuBrin 2016] Andrew J. DuBrin. Essentials of Management, Tenth Edition. Indianapolis, Indiana: Wessex Press, 2016.

Order from Amazon

Cited in:

[Fowler 1999] Martin Fowler, Kent Beck (Contributor), John Brant (Contributor), William Opdyke, Don Robert, Erich Gamma (Foreword). Refactoring: Improving the Design of Existing Code. Boston: Addison-Wesley Professional; first edition (July 8, 1999).

Order from Amazon

Cited in:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Kahneman 1977] Daniel Kahneman and Amos Tversky. “Intuitive Prediction: Biases and Corrective Procedures,” Technical Report PTR-1042-7746, Defense Advanced Research Projects Agency, June 1977.

Available: here; Retrieved: September 19, 2017

Cited in:

[Kahneman 1979] Daniel Kahneman and Amos Tversky, “Intuitive Prediction: Biases and Corrective Procedures,” Management Science 12, 313-327, 1979.

Cited in:

[Kanigel 1997] Robert Kanigel. The one best way: Frederick Winslow Taylor and the enigma of efficiency. Viking Penguin, 1997.

Order from Amazon

Cited in:

[Klein 2017] Gary Klein. Sources of Power: How People Make Decisions, 20th Anniversary Edition. Cambridge, Massachusetts: The MIT Press, 1999.

Order from Amazon

Cited in:

[Laibson 1997] David Laibson. “Golden eggs and hyperbolic discounting,” Quarterly Journal of Economics 112:2, 1997, 443-477.

Available: here; Retrieved: October 25, 2018

Cited in:

[Levin 2012] Kelly Levin, Benjamin Cashore, Steven Bernstein, and Graeme Auld. “Overcoming the tragedy of super wicked problems: constraining our future selves to ameliorate global climate change,” Policy Science 45, 2012, 123–152.

Available: here; Retrieved: October 17, 2018

Cited in:

[Loewenstein 1992] George Loewenstein and Drazen Prelec. “Anomalies in Intertemporal Choice: Evidence and an Interpretation,” Quarterly Journal of Economics, 57:2, 1992, 573-598.

Available: here; Retrieved: October 12, 2018

Cited in:

[Pugh 2010] Ken Pugh. “The Risks of Acceptance Test Debt,” Cutter Business Technology Journal, October 2010, 25-29.

Cited in:

[Rittel 1973] Horst W. J. Rittel and Melvin M. Webber. “Dilemmas in a General Theory of Planning”, Policy Sciences 4, 1973, 155-169.

Available: here; Retrieved: October 16, 2018

Cited in:

[Simon 1973] Herbert A. Simon. “The Structure of Ill Structured Problems,” Artificial Intelligence 4, 1973, 181-201.

Available: here; Retrieved: 10/16/18

Cited in:

[Taylor 1913] Frederick Winslow Taylor. The Principles of Scientific Management. New York: Harper & Brothers, 1913.

Available: here; Retrieved: October 16, 2018 Order from Amazon

Cited in:

[Thokala 2016] Praveen Thokala, Nancy Devlin, Kevin Marsh, Rob Baltussen, Meindert Boysen, Zoltan Kalo, Thomas Longrenn et al. “Multiple Criteria Decision Analysis for Health Care Decision Making—An Introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force,” Value in Health 19:1, 2016, 1-13.

Available: here; Retrieved: 10/16/18

Cited in:

[Zannier 2007] Carmen Zannier, Mike Chiasson, and Frank Maurer. “A model of design decision making based on empirical results of interviews with software designers,” Information and Software Technology 49, 2007, 637-653.

Available: here; Retrieved October 15, 2018

Cited in:

Related posts

References

Last updated on August 19th, 2022 at 04:46 pm

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Available: here; Retrieved: March 16, 2017

Also cited in:

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This article is probably the source of the adage “You are what you measure.” Personally, I believe it’s overstated. That is, it’s true in the large, perhaps, but not in detail. Moreover, there are some things that we are that can’t be measured. But it’s important to understand the content of this article because so many people take it as dogma. Available: here; Retrieved: June 4, 2018

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Contains an extensive discussion of the consequences of partial supervision of performance. Since technical debt can only be partially supervised, the concept is relevant to understanding the effects of performance management systems on technical debt. Order from Amazon

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[Avgeriou 2016] Paris Avgeriou, Philippe Kruchten, Ipek Ozkaya, and Carolyn Seaman, eds. “Managing Technical Debt in Software Engineering,” Dagstuhl Reports, 6:4, 110–138, 2016.

Available: here; Retrieved: March 10, 2017.

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Some libraries are upgrading their book tagging systems from barcodes to RFID tags—what is essentially a platform upgrade. When they do convert, every item in their collections becomes an instance of technical debt until it’s tagged with an RFID. A tagging technician can process about 1,000 items per day. It’s a big job. Available: here; Retrieved: November 21, 2017

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[El-Geish 2015] Mohamed El-Geish. “Broken Windows: Software Entropy and Technical Debt,” blog at LinkedIn.com, March 6, 2015

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[Foganholi 2015] Lucas Borante Foganholi, Rogério Eduardo Garcia, Danilo Medeiros Eler, Ronaldo Celso Messias Correia, and Celso Olivete Junior. “Supporting technical debt cataloging with TD-Tracker tool,” Advances in Software Engineering 2015, 4.

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[Note a] Articles and blog entries about applying Broken Windows to managing technical debt in software:

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[Note b] Articles and blog entries questioning the validity of the Broken Windows theory of crime prevention:

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