Undercounting nonexistent debt items

Last updated on August 15th, 2018 at 01:31 pm

People and companies are developing technologies for assessing the nature and volume of technical debt borne by enterprise assets. The key word is developing. Some tools do exist, and they can be helpful, but they can’t do it all. So most assessments also rely on surveys and interviews of engineers and their managers. But these tools have limitations, too. Among these limitations is undercounting nonexistent debt items in surveys about technical debt.

Sherlock Holmes and Doctor Watson, in an illustration by Sidney Paget
Sherlock Holmes and Doctor Watson, in an illustration by Sidney Paget, captioned “Holmes gave me a sketch of the events.” The illustration was originally published in 1892 in The Strand magazine to accompany a story called “The Adventure of Silver Blaze” by Sir Arthur Conan Doyle. It’s in this story that the following dialog occurs:

Gregory (Scotland Yard detective): “Is there any other point to which you would wish to draw my attention?”

Holmes: “To the curious incident of the dog in the night-time.”

Gregory: “The dog did nothing in the night-time.”

Holmes: “That was the curious incident.”

From this, Holmes deduces that the dog’s master was the villain. This is an example of looking for what is not there, and failing to notice it is an example of absence blindness.

Original book illustration, courtesy Wikimedia Commons.

It’s well known that survey results can exhibit biases. Collectively, these biases are known as response biases [Furnham 1986]. Sources of response bias include phrasing of questions, the demeanor of the interviewer, the desires of the participants to be good experimental subjects, attempts by subjects to respond with the “right answers,” selection of subjects, and more. These sources of bias are real, and we must address them when we design surveys.

But I have in mind here a set of biases more specific to technical debt. For example, when we ask subjects for examples of technical debt, they’re more likely to recall and provide examples of artifacts that exist than they are to provide examples of artifacts that don’t exist. This happens because of a cognitive bias called selection bias. The effect isn’t intentional, and it can dramatically skew results.

Selection bias is an example of a cognitive bias. In this case, selection bias acts to skew the data in such a way as to interfere with proper randomization, which ensures that the sample obtained doesn’t accurately represent the actual population of technical debt artifacts. Specifically, the data will tend to under-represent technical debt artifacts that don’t exist. Related phenomena are absence blindness and survivorship bias.

For example, regression testing is an essential step used in refactoring systems. When regression tests are unavailable, and we try to refactor a system to retire some of its technical debt, we can’t be certain that we haven’t changed something important. And so, when a survey isn’t designed to mitigate the effects of selection bias, we can expect the probability of noting any missing regression tests to be elevated.

Mitigating the risk of undercounting nonexistent debt items

It’s helpful for surveys to include questions that specifically ask subjects to report technical debt items that don’t exist, but which would be helpful if they did exist — like missing regression tests. Even more helpful: conduct brainstorming sessions for engineers in which the goal is to list missing artifacts, tools, or processes that comprise technical debt precisely because they’re missing.

References

[Furnham 1986] Adrian Furnham. “Response bias, social desirability and dissimulation,” Personality and Individual Differences 7:3, 385-400, 1986.

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How outsourcing leads to increasing technical debt

Most of the non-technical precursors of technical debt that afflict in-house work also afflict outsourced work. For example, the planning fallacy affects internal planners, but it also afflicts the bidders for contracts offered by the enterprise in the context of outsourcing. As described in “Unrealistic optimism: the planning fallacy and the n-person prisoner’s dilemma,” Boehm, et al., [Boehm 2016] call this the “Conspiracy of Optimism.” But outsourcing engineering work can introduce pathways for incurring technical debt that are specific to outsourcing.

Green fields
Green fields. Greenfield outsourcing, also known as startup outsourcing, is the outsourcing of activity that has never been performed within the enterprise. It’s especially tricky with respect to technical debt formation, because much of the expertise necessary to perform the work being outsourced is probably not resident within the enterprise. That void in enterprise expertise makes for difficulties in managing technical debt in the outsourced artifacts.

The risks of incurring technical debt associated with outsourcing are inherently elevated, even setting aside those factors that also afflict in-house activity. When most enterprises contract for development of systems or software, the criteria for acceptance rarely include specifications for maintainability or extensibility. This happens, in part, because such qualitative specifications are so difficult to define quantitatively. That’s why the condition of deliverables relative to maintainability and extensibility is so variable. Outsourced deliverables can best be described as bearing an unknown level of technical debt.

The root cause of the problem is likely a lack of a universally accepted metrics for quantifying technical debt [Li 2015]. That’s why it’s difficult to specify in the vendor contract an acceptability threshold for technical debt. And since the consequences of the presence of technical debt in deliverables potentially do not become clear during the lifetime of the contract under which the debt was incurred, years may pass before the issue becomes evident, which complicates the task of understanding the root cause of the problem.

In what follows, I use the term vendor to denote the organization to which work has been outsourced, and the term enterprise to denote the organization that has outsourced a portion of its engineering work. The retained organization is the portion of the enterprise directly relevant to the outsourced work, but which was not itself outsourced. Among the mechanisms that lead to incurring technical debt in the outsourcing context are the six mechanisms sketched below. Gauging the size of these effects by auditing deliverables or by auditing the internal processes of the vendor could be helpful in managing levels of technical debt arising from outsourcing.

This list isn’t intended to be exhaustive. Quite possibly other phenomena also contribute to technical debt formation in the context of outsourcing.

Progressive erosion of retained organization capabilities

Over time, the enterprise can expect erosion of the engineering expertise needed to manage, evaluate, understand, or, if need be, to re-insource (or backsource) the work that has been outsourced [Willcocks 2004][Beardsell 2010]. Typically, staff who formerly performed the outsourced work do move on to other work, voluntarily or not, either within the enterprise or elsewhere. Indeed, cost savings from terminations and employee buyouts often accompany — and economically justify — outsourcing decisions. But even if the enterprise continues to employ the people who formerly performed the work that is now outsourced, those employees, filling new roles, can become less familiar with the current work and therefore less able to perform it. And since they are now engaged in other assignments, their availability is limited. In the public sector, the organizations that perform the outsourced work exacerbate this phenomenon by recruiting from the agencies they serve [Kusnet 2007]. In manufacturing, Kinkel et al. suggest that, paraphrasing, outsourcing disturbs the formation of internal competence [Kinkel 2016].

In short, outsourcing engineering efforts can erode the ability of the enterprise to perform internally the work that is outsourced, or to monitor or evaluate it when performed by the vendor. Consequently, the enterprise is less able to monitor, evaluate, or retire any technical debt that accumulates in the outsourced work products. A policy that would address this risk is one that would (a) require retained organizational capability sufficient to assess the effect on technical debt of any outsourced engineering work; (b) require attention to technical debt issues in any financial models used in making the initial outsourcing decision; (c) require financial models to include the effects of technical debt and controlling technical debt when deciding whether to extend outsourcing contracts with vendors.

Stovepiping among vendors

Most multi-vendor efforts use a separation-of-concerns approach [Laplante 2007] to dividing the work. That is, each vendor is empowered to use any approach it can, consistent with its own contract and statement of work. In some cases, two or more vendors might have overlapping needs that cause them each to produce similar capabilities as elements of their respective deliverables. Sharing of their results is of course possible, but unless both of their contracts anticipate the need for sharing, sharing is unlikely. Failure to share those results that could have been shared can lead to incurring unrecognized technical debt.

Stovepiping within vendors

With regard to the efforts of a single outsource vendor, it’s possible that different teams or individuals working for that vendor might unwittingly create elements with overlapping capabilities. That duplication could lead to technical debt, or it could constitute technical debt in itself. For example, two teams working for the same vendor might have similar testing needs, and might develop testing tools independently. As a second example, in a version of stovepiping combined with temporal displacement, suppose that one team is unaware that a previous effort for the same customer had already developed a capability that it now needs. That team then might re-create that already-existing capability.

Stovepiping within vendors is less likely when the vendor operates under a single vendor technical lead, and the enterprise interacts with that single lead through a single in-house technical lead. When either side of the relationship is managed through multiple contacts, stovepiping is more likely, and new technical debt is more likely to form.

Loss of continuity in the outsourced engineering staff

Unless specified in the agreement between the vendor and the enterprise, staff turnover or reassignment within the vendor organization, between one version of the product or service and the next, can lead to technical debt in just the same way that turnover in-house can lead to technical debt. With outsourcing, however, the vendor has little internalized motivation to control this phenomenon, and the enterprise likely has less control—and less awareness—of staff assignments within the vendor organization than it does within the enterprise. Thus, loss of continuity in the outsourced engineering staff is both more likely and more likely to lead to technical debt.

Reduced coordination of engineering approaches and business objectives

Lack of coordination between engineering approaches and business planning can cause engineers to create and deploy artifacts that must be revisited later, when they learn of business plans that were unknown to the engineers at the time they produced those artifacts. See “Failure to communicate long-term business strategy.” This scenario is more likely, and possibly irresolvable, when the enterprise withholds its long-term plans from the outsourcing vendor to protect its strategy.

Hiring and retention problems

In some instances, commonly called startup outsourcing or greenfield outsourcing, the work being outsourced has never been performed within the enterprise [Delen 2007]. In these cases, typically, the enterprise must then reassign existing employees or hire new employees to interface with the outsource vendor. These roles are analogous to remote supervisors, except that the teams they supervise are not employees of the enterprise. Hiring and retaining people for these roles can be difficult, because of startup challenges both within the enterprise and within the vendor organization. Recruitment and especially retention problems in these roles can decrease the likelihood of controlling technical debt, and increase the likelihood of incurring technical debt.

References

[Beardsell 2010] Julie Beardsell. “IT Backsourcing: is it the solution to innovation?”, SMC Working Paper Series, Issue: 02/2010, Swiss Management Center University, 2010.

Available: here; Retrieved: February 15, 2018

Cited in:

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

Available: here; Retrieved: July 25, 2017

Cited in:

[Delen 2007] Guus Delen. “Decision and Control Factors for IT-sourcing,” in Handbook of Network and System Administration, Jan Bergstra and Mark Burgess, eds., Boston: Elsevier, 929-946 (2007).

Order from Amazon

Cited in:

[Furnham 1986] Adrian Furnham. “Response bias, social desirability and dissimulation,” Personality and Individual Differences 7:3, 385-400, 1986.

Cited in:

[Kinkel 2016] Steffen Kinkel, Angela Jäger, Djerdj Horvath, and Bernhard Rieder. “The effects of in-house manufacturing and outsourcing on companies’ profits and productivity,” 23rd International Annual EurOMA Conference, At Trondheim, Volume: 23, June 2016.

Cited in:

[Kusnet 2007] David Kusnet. “Highway Robbery II,” report of the National Association of State Highway and Transportation Unions (NASHTU).

Cited in:

[Laplante 2007] Phillip A. Laplante. What Every Engineer Should Know About Software Engineering. New York: CRC Press, 2007.

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:

[Willcocks 2004] L. Willcocks, J. Hindle, D. Feeny, and M. Lacity. “IT and Business Process Outsourcing: The Knowledge Potential,” Information Systems Management 21(3), (2004), 7-15.

Cited in:

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Confirmation bias and technical debt

Confirmation bias is a cognitive bias [Kahneman 2011]. 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.

Third stage ignition, sending the Mars Climate Orbiter (MCO) to Mars in December, 1998
Computer-generated image of the third stage ignition, sending the Mars Climate Orbiter (MCO) to Mars in December, 1998. The spacecraft eventually broke up in the Martian atmosphere as a result of what is now often called the “metric mix-up.” The team at Lockheed Martin that constructed the spacecraft and wrote its software used Imperial units for computing thrust data. But the team at JPL that was responsible for flying the spacecraft was using metric units. The mix-up was discovered after the loss of the spacecraft by the investigation panel established by NASA.
One of the many operational changes deployed as a result of this loss was increased use of reviews and inspections. While we do not know why reviews and inspections weren’t as thorough before the loss of the MCO as they are now, one possibility is the effects of confirmation bias in assessing the need for reviews and inspections. Image courtesy Engineering Multimedia, Inc., and U.S. National Aeronautics and Space Administration
Confirmation bias causes technical debt by biasing the information on which decision makers base decisions involving technical debt. Most people in these roles have objectives they regard as having priority over eliminating technical debt. This causes them to bias their searches for information about technical debt in favor of information that would support directly the achievement of those primary objectives. They tend, for example, to discount warnings of technical debt issues, to underfund technical debt assessments, and to set aside advice regarding avoiding debt formation in current projects.

For example, anyone determined to find reasons to be skeptical of the need to manage technical debt need only execute a few Google searches. Searching for there is no such thing as technical debt yields about 300,000 results at this writing; technical debt is a fraud about 1.6 million; and technical debt is a bad metaphor about 3.7 million. Compare this to technical debt which yields only 1.6 million. A skeptic wouldn’t even have to read any of these pages to come away convinced that technical debt is at best a controversial concept. This is, of course, specious reasoning, if it’s reasoning at all. But it does serve to illustrate the potential for confirmation bias to contribute to preventing or limiting rational management of technical debt.

Detecting confirmation bias in oneself is extraordinarily difficult because confirmation bias causes us to (a) decide not to search for data that would reveal confirmation bias; and (b) if data somehow becomes available, to disregard or to seek alternative explanations for it if that data tends to confirm the existence of confirmation bias. Moreover, another cognitive bias known as the bias blind spot causes individuals to see the existence and effects of cognitive biases much more in others than in themselves [Pronin 2002].

Still, the enterprise would benefit from monitoring the possible existence and effects of confirmation bias in decisions with respect to allocating resources to managing technical debt. Whenever decisions are made, we must manage confirmation bias risk.

References

[Beardsell 2010] Julie Beardsell. “IT Backsourcing: is it the solution to innovation?”, SMC Working Paper Series, Issue: 02/2010, Swiss Management Center University, 2010.

Available: here; Retrieved: February 15, 2018

Cited in:

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

Available: here; Retrieved: July 25, 2017

Cited in:

[Delen 2007] Guus Delen. “Decision and Control Factors for IT-sourcing,” in Handbook of Network and System Administration, Jan Bergstra and Mark Burgess, eds., Boston: Elsevier, 929-946 (2007).

Order from Amazon

Cited in:

[Furnham 1986] Adrian Furnham. “Response bias, social desirability and dissimulation,” Personality and Individual Differences 7:3, 385-400, 1986.

Cited in:

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

Order from Amazon

Cited in:

[Kinkel 2016] Steffen Kinkel, Angela Jäger, Djerdj Horvath, and Bernhard Rieder. “The effects of in-house manufacturing and outsourcing on companies’ profits and productivity,” 23rd International Annual EurOMA Conference, At Trondheim, Volume: 23, June 2016.

Cited in:

[Kusnet 2007] David Kusnet. “Highway Robbery II,” report of the National Association of State Highway and Transportation Unions (NASHTU).

Cited in:

[Laplante 2007] Phillip A. Laplante. What Every Engineer Should Know About Software Engineering. New York: CRC Press, 2007.

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:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28.3 (2002): 369-381.

Available: here; Retrieved: July 10, 2017

Cited in:

[Willcocks 2004] L. Willcocks, J. Hindle, D. Feeny, and M. Lacity. “IT and Business Process Outsourcing: The Knowledge Potential,” Information Systems Management 21(3), (2004), 7-15.

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

[Beardsell 2010] Julie Beardsell. “IT Backsourcing: is it the solution to innovation?”, SMC Working Paper Series, Issue: 02/2010, Swiss Management Center University, 2010.

Available: here; Retrieved: February 15, 2018

Cited in:

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

Available: here; Retrieved: July 25, 2017

Cited in:

[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:

[Delen 2007] Guus Delen. “Decision and Control Factors for IT-sourcing,” in Handbook of Network and System Administration, Jan Bergstra and Mark Burgess, eds., Boston: Elsevier, 929-946 (2007).

Order from Amazon

Cited in:

[Furnham 1986] Adrian Furnham. “Response bias, social desirability and dissimulation,” Personality and Individual Differences 7:3, 385-400, 1986.

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:

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

Order from Amazon

Cited in:

[Kinkel 2016] Steffen Kinkel, Angela Jäger, Djerdj Horvath, and Bernhard Rieder. “The effects of in-house manufacturing and outsourcing on companies’ profits and productivity,” 23rd International Annual EurOMA Conference, At Trondheim, Volume: 23, June 2016.

Cited in:

[Kusnet 2007] David Kusnet. “Highway Robbery II,” report of the National Association of State Highway and Transportation Unions (NASHTU).

Cited in:

[Laplante 2007] Phillip A. Laplante. What Every Engineer Should Know About Software Engineering. New York: CRC Press, 2007.

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:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28.3 (2002): 369-381.

Available: here; Retrieved: July 10, 2017

Cited in:

[Willcocks 2004] L. Willcocks, J. Hindle, D. Feeny, and M. Lacity. “IT and Business Process Outsourcing: The Knowledge Potential,” Information Systems Management 21(3), (2004), 7-15.

Cited in:

Other posts in this thread

The fundamental attribution error

Last updated on February 8th, 2018 at 01:25 pm

When we try to understand the behavior of others, we often make a particularly human mistake. We tend to attribute too much to character and disposition and too little to situation and context. This mistake is so common that it has a name: The Fundamental Attribution Error (FAE) (See “The Fundamental Attribution Error” at my other blog). And although little experimental data is available regarding its effects on technical debt, we can plausibly argue that its effects are significant — and unwelcome.

Arapaho moccasins ca. 1880-1910.
Arapaho moccasins ca. 1880-1910. An American Indian proverb advises: “Don’t judge any man until you have walked two moons in his moccasins.” Viewed from the perspective of the Fundamental Attribution Error, the proverb is a way of mitigating its risks. Photo of Arapaho moccasins, ca. 1880-1910 on exhibit at the Bata Shoe Museum, in Toronto, Canada, taken by Daderot, courtesy Wikimedia
The FAE contributes to technical debt in at least two ways. First, it distorts assessments by non-engineers of the motivations of engineers as they warn of future difficulties arising from accumulating technical debt. Second, it distorts assessments by engineers of the motivations of non-engineers as they oppose allocation of resources to technical debt retirement efforts in order to conserve resources for their own efforts or to accelerate efforts in which they have more immediate interest. The two effects are symmetrical in the large, though not in detail.

Below is a description of the effects of the FAE on engineers and non-engineers, some of whom are the internal customers of the engineers. Let’s examine the effects of the FAE that are precipitated by three different claims or positions of the parties to the exchange.

Technical debt depresses engineering productivity

This is a position often held by engineers or their managers.

Engineers notice incidents in which some of the work they must perform on an asset would be much easier or even unnecessary were it not for the technical debt that the asset carries. They sense the burden of the extra effort because they know how much easier and faster the work would be if they could retire the debt.

The internal customers of engineers don’t see these circumstances as clearly as engineers (and their managers) do. Consequently, they tend to discount engineers’ claims of depressed productivity. Some experience engineers’ complaints, requests, and warnings as whining, self-serving nest feathering, or worse. They tend to attribute engineers’ complaints to faults in the character or “work ethic” of engineers.

Instead of retiring the technical debt now, just document it for later

This is a suggestion often put forth by senior managers or the engineers’ internal customers.

The internal customers of engineers have pressing needs for immediate engineering results. They see new products or repairs to existing products as a means of achieving the objectives the enterprise sets. Focusing limited engineering resources on technical debt retirement conflicts with producing results that would help internal customers of engineers meet these more immediate objectives. As a compromise, non-engineers propose that engineers document instances of technical debt as they find them, so that they can be addressed more efficiently after engineers meet the immediate needs of internal customers.

Engineers discount the validity of this approach for three reasons. First, they don’t experience the pressures their internal customers do. To engineers, their customers’ reports of more pressing needs seem to be merely excuses to get what they want when they want it. Second, the proposed documentation work doesn’t advance the engineers’ customer’s current project toward its objectives. Instead, it actually delays the current project, even though non-engineers aren’t aware of the extent of this effect. These delays induce increases in schedule pressure — and therefore technical debt — because the customer of the current project rarely cares enough about the technical debt documentation effort to allow for the extra time it takes. Finally, because many assets evolve continuously, such documentation has a very short shelf life, which limits its value in ways non-engineers might not appreciate.

In these ways, the FAE both creates the documentation suggestion, and limits the ability of engineers to appreciate its motivation, while it also limits the ability of non-engineers to appreciate how limited the value of the documentation is.

Addressing technical debt is important, but not urgent

Senior managers or the engineers’ internal customers are most common among adherents of this belief.

When the engineering organization presents a business case for investing time and resources in addressing technical debt issues, other functions in the enterprise also make business cases of their own. Too often, these cases are evaluated against each other. Investment in one entails reduced investment in another. And since the benefits of technical debt retirement tend to become most visible to non-engineers much later than do the benefits of some other proposals, technical debt retirement projects tend perhaps more often than most to be deferred at best, or, often, rejected.

The FAE is in part responsible for the perception of non-engineers that the benefits of technical debt remediation arrive in the distant future. Engineers notice the benefits relatively immediately, because they interact with the rehabilitated assets on a daily basis. Since non-engineers don’t have these experiences, they notice the benefits only upon delivery of the results of engineering work. This mismatch of the timescales of perceptions of engineers and non-engineers prevents non-engineers from perceiving what is in daily evidence to engineers.

Both engineers and non-engineers are subject to deadlines and resource limitations beyond their control. Their ability to appreciate the challenges their counterparts face is the key to effective collaboration. Too often, neither part feels that it has the time or resources to accommodate the needs of the other.

References

[Beardsell 2010] Julie Beardsell. “IT Backsourcing: is it the solution to innovation?”, SMC Working Paper Series, Issue: 02/2010, Swiss Management Center University, 2010.

Available: here; Retrieved: February 15, 2018

Cited in:

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

Available: here; Retrieved: July 25, 2017

Cited in:

[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:

[Delen 2007] Guus Delen. “Decision and Control Factors for IT-sourcing,” in Handbook of Network and System Administration, Jan Bergstra and Mark Burgess, eds., Boston: Elsevier, 929-946 (2007).

Order from Amazon

Cited in:

[Furnham 1986] Adrian Furnham. “Response bias, social desirability and dissimulation,” Personality and Individual Differences 7:3, 385-400, 1986.

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:

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

Order from Amazon

Cited in:

[Kinkel 2016] Steffen Kinkel, Angela Jäger, Djerdj Horvath, and Bernhard Rieder. “The effects of in-house manufacturing and outsourcing on companies’ profits and productivity,” 23rd International Annual EurOMA Conference, At Trondheim, Volume: 23, June 2016.

Cited in:

[Kusnet 2007] David Kusnet. “Highway Robbery II,” report of the National Association of State Highway and Transportation Unions (NASHTU).

Cited in:

[Laplante 2007] Phillip A. Laplante. What Every Engineer Should Know About Software Engineering. New York: CRC Press, 2007.

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:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28.3 (2002): 369-381.

Available: here; Retrieved: July 10, 2017

Cited in:

[Willcocks 2004] L. Willcocks, J. Hindle, D. Feeny, and M. Lacity. “IT and Business Process Outsourcing: The Knowledge Potential,” Information Systems Management 21(3), (2004), 7-15.

Cited in:

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The Dunning-Kruger effect can lead to technical debt

Last updated on May 31st, 2018 at 07:43 am

The Dunning-Kruger effect [Kruger 1999] can lead to formation or persistence of technical debt in two ways. First, it can cause technologists or their managers to overestimate their ability to maintain the resource focus needed for retiring technical debt in a timely fashion. Second, it can cause senior managers to be reluctant to accede to resource requests of technologists and their managers in support of technical debt management programs.

Cropped detail from Charles Robert Darwin, a painting by John Collier
Cropped detail from Charles Robert Darwin, a painting by John Collier (1850-1934), given to the National Portrait Gallery, London, in 1896. Darwin writes, in The Descent of Man (1871): “… ignorance more frequently begets confidence than does knowledge …” which is the essence of the Dunning-Kruger effect. Image courtesy WikiQuote.

Kruger and Dunning conducted experiments that yielded results consistent with the following four principles (paraphrasing):

  1. Incompetent individuals, compared to their more competent peers, tend to dramatically overestimate their own ability and performance
  2. Incompetent individuals, compared to their more competent peers, tend to be less able to gain insight into their own true levels of performance
  3. Incompetent individuals can gain insight about their shortcomings, but, paradoxically, this comes about by gaining competence
  4. Incompetent individuals, compared to their more competent peers, are less able to recognize competence when they see it

The first three principles lead to distorted assessments of one’s own capabilities. The fourth principle leads to distorted assessments of the capabilities of others.

As an example of distorted self-assessment, consider a team or its managers who must undertake retirement of some types of technical debt in the course of enhancing or repairing an asset. Such a task plan seems at first to offer efficiencies, because the engineers can readily make both kinds of changes at one go. Metaphorically, if we must go to the store for milk, we can also pick up bread while we are there, rather than making two trips.

However, modifying an existing complex technological asset is unlike shopping for bread and milk. The two kinds of modifications — debt retirement and asset enhancement or repair — might seem at first to be separable, and often they are. But if they are not separable, and the two tasks are undertaken together, testing and debugging can become extremely complicated, because of interactions between defects in the two kinds of modifications. Under some circumstances, an experienced team and its managers might be more likely to anticipate these difficulties. An inexperienced team and its managers might be more likely to underestimate the difficulties, as a consequence of the Dunning-Kruger effect. Budget and schedule overruns are possible consequences of underestimating the complexity of the problem.

As an example of the fourth principle above, the Dunning-Kruger effect can cause some decision-makers to discount the warnings and resource requests of engineers and their managers. Decision-makers who are unsophisticated in matters related to technical debt must nevertheless assess the validity of the requests for resources. In making these assessments, these decision-makers may be disadvantaged for a number of reasons, including the following:

  • Decision-makers might hold any of a number of mistaken beliefs about technical debt. For example, many believe that the main causes of technical debt are poor decisions by engineering managers. And others believe that technical debt is the result of slovenly work habits of engineers. Those who hold such beliefs might be reluctant to allocate yet more resources to engineers to address the problem of technical debt.
  • If the advocates of resources for technical debt management are not fully informed about the strategic direction of the enterprise, their requests might be inconsistent with enterprise strategy. As a result of a cognitive bias [Kahneman 2011] known as the halo effect [Thorndike 1920], decision-makers might tend to discount valid portions of the technologists’ proposals, because some portions of those proposals don’t take enterprise strategy into account properly.
  • Decision-makers might be affected by unrealistic optimism [Weinstein 1996], also known as optimism bias. It’s a cognitive bias that can cause them to discount the sometimes-vivid warnings of technologists about the unfavorable consequences of failing to provide technical debt management resources.

Investigations of the degree of correlation between burdens of technical debt and the incidence of rejected or severely curtailed proposals for resources to support technical debt management programs could determine the significance of the Dunning-Kruger effect relative to the problem of technical debt. Also rewarding would be a survey of the nearly 200 known cognitive biases, to determine which of them might be most likely to affect decision-making relative to technical debt, and how best to mitigate the risks they present.

References

[Beardsell 2010] Julie Beardsell. “IT Backsourcing: is it the solution to innovation?”, SMC Working Paper Series, Issue: 02/2010, Swiss Management Center University, 2010.

Available: here; Retrieved: February 15, 2018

Cited in:

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

Available: here; Retrieved: July 25, 2017

Cited in:

[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:

[Delen 2007] Guus Delen. “Decision and Control Factors for IT-sourcing,” in Handbook of Network and System Administration, Jan Bergstra and Mark Burgess, eds., Boston: Elsevier, 929-946 (2007).

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Cited in:

[Furnham 1986] Adrian Furnham. “Response bias, social desirability and dissimulation,” Personality and Individual Differences 7:3, 385-400, 1986.

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:

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

Order from Amazon

Cited in:

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

Order from Amazon

Cited in:

[Kinkel 2016] Steffen Kinkel, Angela Jäger, Djerdj Horvath, and Bernhard Rieder. “The effects of in-house manufacturing and outsourcing on companies’ profits and productivity,” 23rd International Annual EurOMA Conference, At Trondheim, Volume: 23, June 2016.

Cited in:

[Kruger 1999] J. Kruger and D. Dunning. “Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments,” Journal of Personality and Social Psychology, 77(6), 1121-1134 (1999).

Cited in:

[Kusnet 2007] David Kusnet. “Highway Robbery II,” report of the National Association of State Highway and Transportation Unions (NASHTU).

Cited in:

[Laplante 2007] Phillip A. Laplante. What Every Engineer Should Know About Software Engineering. New York: CRC Press, 2007.

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:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28.3 (2002): 369-381.

Available: here; Retrieved: July 10, 2017

Cited in:

[Thorndike 1920] E.L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4(1), 25-29 (1920). doi:10.1037/h0071663

Cited in:

[Weinstein 1996] Neil D. Weinstein and William M. Klein. “Unrealistic Optimism: Present and Future,” Journal of Social and Clinical Psychology 15(1), 1-8 (1996). doi:10.1521/jscp.1996.15.1.1

Cited in:

[Willcocks 2004] L. Willcocks, J. Hindle, D. Feeny, and M. Lacity. “IT and Business Process Outsourcing: The Knowledge Potential,” Information Systems Management 21(3), (2004), 7-15.

Cited in:

Other posts in this thread

Non-technical precursors of non-strategic technical debt

Last updated on April 29th, 2018 at 06:36 am

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. In this group of posts I examine a variety of precursors of non-strategic technical debt that are not directly related to technology. Sources of these precursors include:

  • Communication between and among people
  • Organizational policies relating to job assignments
  • Cognitive biases [Kahneman 2011]
  • Performance management policy
  • Incentive structures
  • Organizational structures
  • Contract language
  • Outsourcing
  • …and approaches to dealing with budget depletion.

The cables of the Brooklyn Bridge are an example of non-strategic technical debt
Some of the suspension cables of the Brooklyn Bridge. Washington Roebling, the chief engineer, designed the cables to be composed of 19 “strands” of wire rope [McCullough 1972]. Each strand was to be made of 278 steel wires. Thus, the original design called for a total of 5,282 wires in each of the main cables. After the wire stringing began, the bridge company made an unsettling discovery. The wire supplier, J. Lloyd Haigh, had been delivering defective wire by circumventing the bridge company’s stringent inspection procedures. In all, Roebling estimated that 221 U.S. tons (200 metric tons) of rejected wire had been installed in the bridge. This was a significant fraction of the planned total weight of 3,400 U.S. tons (3,084 metric tons). Because they couldn’t remove the defective wire, Roebling decided to add about 150 wires to each main cable. That extra wire would be provided at no charge by Haigh [Talbot 2011]. I can’t confirm this, but I suspect that Roebling actually added 152 wires, which would be eight wires for each of the 19 strands, to make a total of 286 wires per strand, for a total of 5,434 wires. The presence of the defective wire in the bridge cables—which remains to this day—is an example of technical debt. The fraud perpetrated by Haigh illustrates how malfeasance can lead to technical debt.
I use the term precursor instead of cause because none of these conditions leads to technical debt inevitably. From the perspective of the policymaker, we can view these conditions as risks. It’s the task of the policymaker to devise policies that manage these risks.

McConnell has classified technical debt in a framework that distinguishes responsible forms of technical debt from other forms [McConnell 2008]. Briefly, we incur some technical debt strategically and responsibly, and we retire it when the time is right. We incur other technical debt for other reasons, some of which are inconsistent with enterprise health and wellbeing.

The distinction is lost on many. Unfortunately, most technical debt is non-strategic. We would have been better off  if we had never created it. Or if we had retired it almost immediately. In any case we should have retired it long ago.

It’s this category of non-strategic technical debt that I deal with in this group of posts. Although all technical debt is unwelcome, we’re especially interested in non-strategic technical debt, because it is usually uncontrolled. In these posts I explore the non-technical mechanisms that lead to formation of non-strategic technical debt. Schedule pressure is one exception. Because it’s so important, it deserves a thread of its own. I’ll address it later.

Common precursors of non-strategic technical debt

Here are some of the more common precursors of non-strategic technical debt.

I’ll be adding posts on these topics, so check back often, or subscribe to receive notifications when they’re available.

References

[Beardsell 2010] Julie Beardsell. “IT Backsourcing: is it the solution to innovation?”, SMC Working Paper Series, Issue: 02/2010, Swiss Management Center University, 2010.

Available: here; Retrieved: February 15, 2018

Cited in:

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

Available: here; Retrieved: July 25, 2017

Cited in:

[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:

[Delen 2007] Guus Delen. “Decision and Control Factors for IT-sourcing,” in Handbook of Network and System Administration, Jan Bergstra and Mark Burgess, eds., Boston: Elsevier, 929-946 (2007).

Order from Amazon

Cited in:

[Furnham 1986] Adrian Furnham. “Response bias, social desirability and dissimulation,” Personality and Individual Differences 7:3, 385-400, 1986.

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:

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

Order from Amazon

Cited in:

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

Order from Amazon

Cited in:

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

Order from Amazon

Cited in:

[Kinkel 2016] Steffen Kinkel, Angela Jäger, Djerdj Horvath, and Bernhard Rieder. “The effects of in-house manufacturing and outsourcing on companies’ profits and productivity,” 23rd International Annual EurOMA Conference, At Trondheim, Volume: 23, June 2016.

Cited in:

[Kruger 1999] J. Kruger and D. Dunning. “Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments,” Journal of Personality and Social Psychology, 77(6), 1121-1134 (1999).

Cited in:

[Kusnet 2007] David Kusnet. “Highway Robbery II,” report of the National Association of State Highway and Transportation Unions (NASHTU).

Cited in:

[Laplante 2007] Phillip A. Laplante. What Every Engineer Should Know About Software Engineering. New York: CRC Press, 2007.

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:

[McConnell 2008] Steve McConnell. Managing Technical Debt, white paper, Construx Software, 2008.

Available: here; Retrieved November 10, 2017.

Cited in:

[McCullough 1972] David McCullough. The Great Bridge: The epic story of the building of the Brooklyn Bridge. New York: Simon and Schuster, 1972.

Order from Amazon

Cited in:

[Pronin 2002] Emily Pronin, Daniel Y. Lin, and Lee Ross. “The bias blind spot: Perceptions of bias in self versus others.” Personality and Social Psychology Bulletin 28.3 (2002): 369-381.

Available: here; Retrieved: July 10, 2017

Cited in:

[Talbot 2011] J. Talbot. “The Brooklyn Bridge: First Steel-Wire Suspension Bridge.” Modern Steel Construction 51.6 (2011): 42-46.

Available: here; Retrieved: December 20, 2017.

Cited in:

[Thorndike 1920] E.L. Thorndike. “A constant error in psychological ratings,” Journal of Applied Psychology, 4(1), 25-29 (1920). doi:10.1037/h0071663

Cited in:

[Weinstein 1996] Neil D. Weinstein and William M. Klein. “Unrealistic Optimism: Present and Future,” Journal of Social and Clinical Psychology 15(1), 1-8 (1996). doi:10.1521/jscp.1996.15.1.1

Cited in:

[Willcocks 2004] L. Willcocks, J. Hindle, D. Feeny, and M. Lacity. “IT and Business Process Outsourcing: The Knowledge Potential,” Information Systems Management 21(3), (2004), 7-15.

Cited in:

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