Spontaneous generation

Last updated on June 3rd, 2018 at 04:09 pm

Technical debt needn’t result from anyone’s conscious decision. In some instances, technical debt seems to appear as if by spontaneous generation. And that creates problems for technical debt management programs that assume that technical debt results from employee decisions in the form of the intentions of engineers or others.

U.S. Army soldiers, along with volunteers from the community, install roof trusses for a Habitat for Humanity home in Brainerd, Minn., July 13, 2010
U.S. Army soldiers, along with volunteers from the community, install roof trusses for a Habitat for Humanity home in Brainerd, Minnesota, July 13, 2010. Hurricane ties are in place at the top of the wall as the roof trusses are being placed. The Florida state building code was strengthened in 2002 to require hurricane ties to strengthen the connection between the roofs and walls of buildings. Homes built before 2002, and which lack hurricane ties, are therefore carrying technical debt. Retiring that debt is difficult. It involves retrofitting hurricane ties which usually requires cutting holes in the home’s siding—one for each tie—and then repairing the holes. Photo by Sgt. Nicholas Olson, courtesy  Wikimedia Commons, where you can find a larger version of this image.
Although knowing author or engineer intention relative to technical debt artifacts can be helpful when organizations plan or execute technical debt retirement programs, sound technical debt management policy must address situations in which author or engineer intention wasn’t a contributing factor in creating the debt, or intention can’t be determined, or intention is concealed. Classifications of technical debt must therefore consider business strategy and resource availability as well as author intention.

This difference in priorities contributes to tension between technologists and policymakers with respect to their definitions of technical debt.

Within enterprises of significant size, classifying technical debt is an essential step in designing programs for reducing the cost of carrying technical debt. Although the choice of classification scheme depends on one’s objectives, most classification schemes explored so far in the literature of technical debt are more suitable for use by technologists than by policymakers. But, unsurprisingly, the assistance they provide to policymakers relates mostly to policies that affect technologist behavior or resource allocation within the space of technical activities.

An example may clarify the issue. Technologists tend to create classifications of technical debt that emphasize author intentions. For example, Fowler has created a widely accepted two-dimensional [Lowy 2004] classification [Fowler 2009] that characterizes technical debt according to the Degree of Wisdom in incurring it (he calls this dimension Reckless/Prudent), and Degree of Intentionality in incurring it (he calls this dimension Deliberate/Inadvertent). See “Technical debt in software engineering” for more.

This classification, and another due to McConnell [McConnell-slides 2013] are widely accepted in the technical literature—widely, but not universally. For example, some believe that no artifact can be deemed technical debt unless its presence (or absence) was the result of a conscious decision [Adobe Blogs 2014]. Some adherents of this view would reject all of Fowler’s “Inadvertent” technical debt.

This focus on engineering intention likely arises, in part, for two reasons. First, technologists tend to have good access to their own intentions, and to the intentions of other technologists. Second, knowledge of the intentions of the people who created (or omitted) the artifacts in question can be helpful to technologists as they develop plans for retiring particular classes of technical debt.

For policymakers, both of these widely accepted classifications, while helpful, are inadequate. Intentionality with respect to technical debt formation is indeed a valuable consideration in developing technical debt policy, but because technical debt can arise for reasons unrelated to engineers’ intentions—indeed, it can arise for reasons unrelated to any enterprise activity—intention-based classifications provide inadequate guidance for policy formation.

Consider technological advancement that arises from sources external to the enterprise. For example, with the emergence of the HTML5 standard, many Web sites became obsolete, in the sense that they didn’t exploit capabilities that had become available. These sites were in need of updating to remain competitive in their markets. And capabilities that emulated the new standard, but which exploited alternative technologies, needed to be replaced. All of these artifacts—including those that existed, and those that didn’t, comprise technical debt.

Relative to technical debt management, an enterprise that devotes resources to monitoring external technology trends would have an advantage over competitors that tend to focus solely on employee behavior.

Technological advancement that occurs outside the enterprise can thus create technical debt within the enterprise. This is just one example of spontaneous generation of technical debt. Thinking about technical debt this way, you can probably identify other sources of spontaneous generation. Together, they create a need for policies that can address their management.

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

Other posts in this thread

Adopt an enterprise-wide definition of technical debt

An enterprise-wide definition of technical debt is essential because effective technical debt management requires cooperation from almost everyone. Absent a shared definition of technical debt, controversy can develop, especially among those who have previously encountered the concept—namely, among technologists. Policymakers can make invaluable contributions to the design of the cultural transformation that will enable control of technical debt.

A physical expression of shared commitment, essential to adopting an enterprise-wide definition of technical debt
A physical expression of shared commitment. Effective management of technical debt requires both a shared understanding of what it is and a shared commitment to do what’s required to get control of it.

Li et al. [Li 2015] found that defining what is and what is not technical debt remains a question at issue in the software engineering literature. Even if we restrict the discussion to software constructed in-house, opinions about what constitutes technical debt do differ. The authors found that in the literature of technical debt, “The term ‘debt’ has been used in different ways by different people, which leads to ambiguous interpretation of the term.”

This finding is perhaps the most significant for policymakers, because it suggests that implementing a technical debt management regime will require forging an organizational consensus about the meaning of the term technical debt. The people of most organizations come from a broad array of different backgrounds. Some have little knowledge of technical debt, and therefore have no preconceptions. But those who are aware of the issue, who are mainly technologists and their managers, probably interpret the term technical debtin a variety of ways. Because some of those who do have awareness of the term are likely to have strong opinions about its meaning, one can anticipate a need to resolve these differences of opinion early in the effort to gain control of technical debt.

Some technical terms, like RAID,byte, compiler, and kilowatt,have standard definitions that are widely accepted. Although the term technical debthas found wide use, there is no standard definition for it. What some people categorize as technical debt, others do not. Those who are accustomed to working with terms that have precise, widely accepted definitions might tend to assume that the term technical debtdoes have (or should have) one as well. This assumption can create some difficulty for people who do not realize that others might not share their views as to the definition of the term.

Policymakers must be aware that there is a lack of consensus about the definition of technical debt. Our definition, crafted specially for the use of policymakers, might seem unusually broad to technologists and engineers. For that reason alone, it’s advisable to become familiar with the various ways the term is used by technologists, because understanding their perspective is essential to formulating sound policy deserving of their respect.

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

Cited in:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

Other posts in this thread

Tension between policymakers and technologists

Last updated on July 24th, 2018 at 08:17 pm

Tension between policymakers and technologists, which can manifest as misalignment of their respective priorities, is a significant contributor to uncontrolled growth of technical debt. In this thread I explore sources of this tension and introduce concepts that can assist policymakers and technologists in their efforts to control the growth of technical debt.

Effective, sustainable control of technical debt is the objective of technical debt management policy. In an enterprise that has achieved this objective, technical debt serves as a strategic tool that assists in attaining and maintaining market leadership. In such an organization, technical debt does exist, and some legacy technical debt might remain in place, but technical debt growth is managed strategically, if growth occurs at all. Any technical debt that carries significant MICs, and which compromises productivity and enterprise agility, is addressed and retired with appropriate priority. In short, technical debt is addressed not solely as a technological issue, but as a component of business strategy.

Raindrops on a grapevine
Raindrops on a grapevine. We often think of tension as a negative, destructive force. But tension—in the case of raindrops, surface tension—is what holds a raindrop together. Tension gives the raindrop structure and integrity. The tension between technologists and policymakers can also be constructive. It can ensure that the enterprise manages its technical debt in ways that balance the political imperatives of both technology and strategic health.

This stance is at odds with the historical position most enterprises have adopted vis-à-vis technical debt. Historically, technical debt has been seen as a technical problem, if it has been recognized at all. Most enterprises have left the management of technical debt to the technologists. Frequently, then, the policymaker who enters the discussion about technical debt might be seen by technologists as an interloper, arriving late to the discussion, or as a less-than-knowledgeable invader attempting to seize control of a piece of the technologists’ domain. Tensions can arise between policymakers and technologists. Such tensions complicate the problem of managing technical debt.

One possible source of this tension is revealed in a study of the literature of technical debt, which is evolving so rapidly that it has itself become a focus for research. Li et al. [Li 2015] have produced a review of the software engineering technical debt literature, from which we can extract insights useful to policymakers. Although they studied only the literature relating to technical debt in software engineering, their conclusions are, at least in part, applicable to any field in which the components of the finished product are executed within software tools before being committed to operational form. This covers a wide array of knowledge-intensive endeavors, including mechanical system design, electronic design, framing of legislation, process design, architecture, and even book authorship.

In this thread, I explore the sources of the tension between the modern reality of technical debt as an enterprise issue, and the historical situation of technical debt as a technological issue. This can serve as a guide for policymakers in reframing technical debt from a technological issue dependent for resolution on enterprise resources, to an enterprise issue dependent for resolution on technological resources.

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

Cited in:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

Other posts in this thread

Organizational psychopathy: career advancement by surfing the debt tsunami

Last updated on July 24th, 2018 at 08:23 pm

During policy debates, some decision-makers and some advocates take positions that offer short-term advantages to the enterprise at the expense of incurring heavy burdens of new technical debt or allowing legacy technical debt to remain in place. Some of these decisions can be strategic, and they can benefit the enterprise. But organizational psychopathy can be the dominant contributing factor when the primary beneficiary of the strategy is the decision-maker or the advocate, and when he or she intends knowingly to move on to a new position or to employment elsewhere before the true cost of the technical debt becomes evident.

The aftermath of the 2004 Indian Ocean earthquake, 26 December 2004
The aftermath of the 2004 Indian Ocean earthquake and tsunami, 26 December 2004. Shown is what remained of Meulaboh, Sumatra, Indonesia, after it was hit by the tsunami. The photo was taken on January 10. At the lower left is a Landing Craft Air Cushion (LCAC) hovercraft vehicle, assigned to USS Bonhomme Richard, delivering supplies. LCACs are capable of transporting more supplies than helicopters in a single trip. The technical debt devastation left behind after an organizational psychopath moves on to further conquests can be just as overwhelming as the physical devastation left behind after a tsunami. Photo by U.S. Navy courtesy Wikimedia Commons.
Such decisions can be counterproductive for the enterprise in the long term. But the decision-maker or advocate nevertheless favors the decision, because he or she plans to take credit for the short-term benefits, and then move on to a new career position elsewhere to escape the technical debt problems created by the decision. In effect, the decision-maker or advocate plans to “surf the debt tsunami.”

People who adopt strategies of this kind might be following the pattern of organizational psychopathy [Babiak 2007] [Morse 2004]. Organizational psychopaths compulsively seek power and control over others. They use a vast array of tactics, but the tactic of greatest relevance to this discussion is the use of enterprise resources to advance the psychopath’s career. Technical debt provides a mechanism for borrowing future resources to enhance present performance, thus advancing the career of the psychopath. It’s especially attractive to the psychopath because the harmful consequences of technical debt can remain hidden until the psychopath has long ago moved on.

Psychopaths are better equipped than most to execute such strategies, because they can be exceedingly charming, intelligent, and charismatic. Because they are adept at deception, they are willing to conceal the truth about the technical debt they create, misrepresenting its costs and consequences, or concealing it altogether. Most important, organizational psychopaths seem to lack the internal regulators of conscience and compunction that limit the actions of non-psychopaths. For example, in a debate about a specific technical decision, the psychopath is willing to use any tools available to win the point, including using deception to destroy the career of anyone who challenges the psychopath’s position.

Babiak and Hare estimate that the incidence of psychopathy in senior positions in business is about 3-4% — between 1/30 and 1/25. However, I’m unaware of any studies of the strategic use of technical debt by these individuals. It’s reasonable to suppose that technical debt has been so employed, but the significance of this phenomenon is unknown. Serious investigation is in order.

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

[Babiak 2007] Paul Babiak and Robert D. Hare. Snakes in Suits: When Psychopaths Go to Work. New York: HarperCollins, 2007. ISBN:978-0-06-114789-0

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:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

Cited in:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

[Morse 2004] Gardiner Morse. “Executive psychopaths,” Harvard Business Review, 82:10, 20-22, 2004.

Available: here; Retrieved: April 25, 2018

Cited in:

Other posts in this thread

Unrealistic optimism: the planning fallacy and the n-person prisoner’s dilemma

Last updated on September 20th, 2018 at 03:51 pm

In a 1977 report, Daniel Kahneman and Amos Tversky identify one particular cognitive bias [Kahneman 2011], the planning fallacy, which afflicts planners [Kahneman 1977] [Kahneman 1979]. They discuss two types of information planners use. Singular information is specific to the case at hand; distributional information is drawn from similar past efforts. The planning fallacy is the tendency of planners to pay too little attention to distributional evidence and too much to singular evidence, even when the singular evidence is scanty or questionable. Failing to harvest lessons from the distributional evidence, which is inherently more diverse than singular evidence, the planners tend to underestimate cost and schedule. So for any given project, there’s an inherent tendency in human behavior to promise lower costs, faster delivery, and greater benefits than anyone can reasonably expect.

Aerial view of Hoover Dam, September 2017
Aerial view of Hoover Dam, September 2017. Under construction from 1931 to 1936, the dam was built for $48.8M ($639M in 2016 dollars) under a fixed-price contract. It was completed two years ahead of schedule. Apparently the planning fallacy doesn’t act inevitably. 112 men died in incidents associated with its construction. 42 more died of a condition diagnosed as pneumonia, but which is now thought to have been carbon monoxide poisoning due to poor ventilation in the dam’s diversion tunnels during construction. There’s little doubt that unrealistic optimism affects not only projections of budget and schedule, but also projections of risks, including deaths. Photo (cc) Mariordo (Mario Roberto Durán Ortiz), courtesy Wikimedia Commons.
But the problem is exacerbated by a dynamic described by Boehm et al. [Boehm 2016], who observe that because organizational resources are finite, project sponsors compete with each other for resources. They’re compelled by this competition to be unrealistically optimistic about their objectives, costs, and schedules. Although Boehm et al. call this mechanism the “Conspiracy of Optimism,” possibly facetiously, it isn’t actually a conspiracy. Rather, it’s a variant of the N-Person Prisoner’s Dilemma [Hamburger 1973].

Unrealistic optimism creates budget shortfalls and schedule pressures, both of which contribute to conditions favorable for creating non-strategic technical debt. And the kinds of technical debt produced by this mechanism, or any mechanism associated with schedule or budget pressure, tend to be subtle — they’re the types least likely to become evident in the short term. For example, technical debt that might make a particular kind of enhancement more difficult in the next project is more likely to appear than technical debt in the form of a copy of some code that should have been replaced by a utility routine. Copies of code are more easily discovered and more likely to be retired in the short term, if not in the current project. Awkward architecture might be more difficult to identify, and is therefore more likely to survive in the intermediate or long term.

In other words, the forms of technical debt most likely to be generated are those that are the most benign in the short term, and which are therefore more likely to escape notice. If noticed, they’re more likely to be forgotten unless carefully documented, an action that’s unlikely to be taken under conditions of schedule and budget pressure. In this way, the non-strategic technical debt created as a result of unrealistic optimism is more likely than most technical debt to eventually become legacy technical debt.

Policymakers can assist in addressing the consequences of unrealistic optimism by advocating for education about it. They can also advocate for changes in incentive structures and performance management systems to include organizational standards with respect to realism in promised benefits, costs, and schedules.

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

[Babiak 2007] Paul Babiak and Robert D. Hare. Snakes in Suits: When Psychopaths Go to Work. New York: HarperCollins, 2007. ISBN:978-0-06-114789-0

Order from Amazon

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:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Hamburger 1973] H. Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

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:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

Cited in:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

Cited in:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

[Morse 2004] Gardiner Morse. “Executive psychopaths,” Harvard Business Review, 82:10, 20-22, 2004.

Available: here; Retrieved: April 25, 2018

Cited in:

Other posts in this thread

Failure to communicate the technical debt concept

Last updated on May 25th, 2018 at 09:46 am

The behavior of internal customers and users of enterprise technological assets can contribute to technical debt formation and persistence. Because of these contributions, introducing effective technical debt management practices requires widespread behavioral changes on the part of those internal customers and users. Accepting these changes, and the initiative and creativity they require, is possible only if people understand the technical debt concept. When they do, they can appreciate the benefits of controlling technical debt, and the consequences of failing to control it. Similarly, when they do not understand or accept the technical debt concept, progress toward effective technical debt management is unlikely. Policymakers can contribute to the planning and execution of the required organizational transformation.

Even when the engineering teams are aware of the technical debt concept, and when they do try to manage technical debt, they cannot make much progress unless they have the support and understanding of their own management, their internal customers, and their customers’ managements. Everyone must understand that controlling technical debt — and retiring it — is a necessary engineering activity that has a business purpose. Everyone must understand that technical debt arises as a result of everyone’s behavior — not just the behavior of technologists.

A tensegrity 3-prism
A tensegrity three-prism. . Read about tensegrity structures.
Image (cc) Bob Burkhardt courtesy Wikimedia.
Part of the job of Management is to see that engineers have what they need to avoid incurring technical debt unnecessarily, and that they have what they need to retire elements of legacy technical debt on a regular basis. Internal customers must understand that communicating their long-term business strategies to Engineering is essential for limiting unnecessary creation of artifacts that become non-strategic technical debt. Only by understanding the technical debt concept can internal customers learn to avoid the behaviors that lead to non-strategic technical debt, and adopt behaviors that limit new technical debt.

Tensegrity structures provide a metaphor for organizations that have mastered the technical debt concept. Tensegrity structures use isolated rigid components in compression, held by a network of strings or cables in tension. The rigid components are usually struts or masts, and they aren’t in contact with each other.

The struts correspond to the users or customers of technological assets. The cables correspond to the engineering activities required to support the customers. The organization is stable relative to technical debt only when the two kinds of elements (struts and cables) work together, each playing its own role, but each appreciating the role of the other.

Advocating for cultural transformation

Advocates of any change to organizational culture are often seen as acting in their own self-interest. That’s a common risk associated with cultural transformation. It’s a risk that can lead to failure when inserting practices related to technical debt management into the culture. The risk is greatest when advocates for change are drawn exclusively from the technical elements of the enterprise. The ideal advocates for these ideas and practices are the internal customers of the technical organizations, and senior management.

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

[Babiak 2007] Paul Babiak and Robert D. Hare. Snakes in Suits: When Psychopaths Go to Work. New York: HarperCollins, 2007. ISBN:978-0-06-114789-0

Order from Amazon

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:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Hamburger 1973] H. Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

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:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

Cited in:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

Cited in:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

[Morse 2004] Gardiner Morse. “Executive psychopaths,” Harvard Business Review, 82:10, 20-22, 2004.

Available: here; Retrieved: April 25, 2018

Cited in:

Other posts in this thread

Useful projections of MPrin might not be attainable

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

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

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

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

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

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

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

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

[Babiak 2007] Paul Babiak and Robert D. Hare. Snakes in Suits: When Psychopaths Go to Work. New York: HarperCollins, 2007. ISBN:978-0-06-114789-0

Order from Amazon

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:

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

Available here; Retrieved January 10, 2016.

Cited in:

[Hamburger 1973] H. Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

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:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

Cited in:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

Cited in:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

[Morse 2004] Gardiner Morse. “Executive psychopaths,” Harvard Business Review, 82:10, 20-22, 2004.

Available: here; Retrieved: April 25, 2018

Cited in:

Related posts

Glossary and Terminology

Last updated on October 8th, 2019 at 07:27 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: “Rules of engagement for auxiliary technical debt

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: “How technical debt can create more technical debt

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

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]

Non-strategic technical debt

Non-strategic technical debt is technical debt that appears in the asset without strategic purpose. We tend to introduce non-strategic 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: “Non-technical precursors of non-strategic technical debt

The planning fallacy

The planning fallacy is a cognitive bias that causes planners to underestimate costs and schedules, and over-promise benefits, because they pay too little heed to past experience on similar efforts, and 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 erroris 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 adopt the belief—usually without justification, and possibly outside our awareness—that if we make changes in the abstraction without fully investigating the consequences of those changes, we can be certain that the familiar properties of the abstraction we modified will apply, suitably modified, to the new form of the abstraction.  Or we assume incorrectly that the abstraction will accommodate any changes we make to its environment. 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, non-existent, 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

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 could produce maximum efficiency: (1) scientific selection of the person performing the work; (2) scientific breakdown of tasks; and (3) separating 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. Its unsuitability 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) that has as an objective retiring from a specified set of assets a particular kind of technical debt (or particular kinds of technical debt), the TDIQ is the Technical Debt In Question. More: “Retiring technical debt in irreplaceable assets

Technical debt

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, and which we would therefore 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. 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 are not 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

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

[Babiak 2007] Paul Babiak and Robert D. Hare. Snakes in Suits: When Psychopaths Go to Work. New York: HarperCollins, 2007. ISBN:978-0-06-114789-0

Order from Amazon

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:

[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:

[Hamburger 1973] H. Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

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:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

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:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

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:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

[Morse 2004] Gardiner Morse. “Executive psychopaths,” Harvard Business Review, 82:10, 20-22, 2004.

Available: here; Retrieved: April 25, 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:

Who are the policymakers?

Last updated on November 21st, 2017 at 08:34 am

As we 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. Organizational policy is the framework of principles that guide policymakers, decision makers, and everyone in the organization as they carry out their responsibilities.

Some organizational policies that do not even mention technical debt can affect the way the organization manages technical debt. For this reason, all policymakers are potentially involved in formulating policy that affects the ability of the organization to manage technical debt.

References

[Adobe Blogs 2014] Adobe Blogs. “What is Technical Debt?,” Adobe Blogs, September 8, 2014.

Available: here; Retrieved February 26, 2017.

Cited in:

[Babiak 2007] Paul Babiak and Robert D. Hare. Snakes in Suits: When Psychopaths Go to Work. New York: HarperCollins, 2007. ISBN:978-0-06-114789-0

Order from Amazon

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:

[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:

[Hamburger 1973] H. Hamburger. “N-person Prisoner’s Dilemma,” Journal of Mathematical Sociology, 3, 27–48, 1973. doi:10.1080/0022250X.1973.9989822

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:

[Kahneman 2011] Daniel Kahneman. Thinking, Fast and Slow. New York: Macmillan, 2011.

Order from Amazon

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:

[Li 2015] Z. Li, P. Avgeriou, and P. Liang. “A systematic mapping study on technical debt and its management,” Journal of Systems and Software 101, 193-220, 2015.

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:

[Lowy 2004] Alex Lowy and Phil Hood. The Power of the 2x2 Matrix: Using 2x2 Thinking to Solve Business Problems and Make Better Decisions. Jossey-Bass, 2004.

Order from Amazon

Cited in:

[McConnell-slides 2013] Steve McConnell. “Managing Technical Debt”, ICSE 2013.

Available: here; Retrieved November 11, 2017

Cited in:

[Morse 2004] Gardiner Morse. “Executive psychopaths,” Harvard Business Review, 82:10, 20-22, 2004.

Available: here; Retrieved: April 25, 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:

Related posts