Solutions Archives | Page 3 of 3 | Technical Debt for Policymakers

July 13, 2018July 24, 2018

Demodularization can help control technical debt

Last updated on July 24th, 2018 at 07:26 pm

Modularity is a widely accepted design approach for complex systems. But because modularity can be implicated in the accumulation and persistence of technical debt, temporary demodularization can help control technical debt, and longer-term demodularization can reduce the rate of accumulation of technical debt.

Modularization is a widely used approach to complex system design

Two shipping containers resting on a “spine car” — Two shipping containers resting on a “spine car,” a kind of rail car used for shipping containers. The container on the left is a so-called tank container, used for bulk cargo. Various types of tank containers are available for transporting different types of cargo, such as wine, oils, ammonia, and even cryogenic liquids. The steel frame around the tank provides compatibility with the standard container profile, which makes the tank compatible with equipment built to handle standard shipping containers. This functions as an interface between the tank and the container-handling equipment. Photo (cc) Mr Snrub at the English language Wikipedia

Since the 1970s, modular design of systems has been de rigueur in both software and hardware. More than that, modularization has been demonstrated to be an essential feature of maintainable, adaptable, and extensible systems [Parnas 1979] [Sullivan 2001]. And we now understand that modularization is a foundational attribute of loose coupling in systems, which enables system designers and maintainers to work in parallel, with independence, on system elements, rendering systems economical at levels of complexity beyond what is achievable with tighter coupling [Orton 1990].

Eliminating duplication is one reason why modularization reduces maintenance and enhancement costs. Modularization enables system designers to create a single system element that provides needed functionality to other parts of the system. Because there is then only one system element that provides that capability, adapting it in response to a new need, or to correct a defect, need be done only once.

That’s a big deal. If that capability were provided in multiple system elements, adaptation would be necessary for each of those elements. Moreover, the multiplicity of elements opens the possibility that adaptation might not be performed consistently, which could create further problems. Eliminating duplication is a most useful property of modularization.

Modularization provides many other advantages. For example, modularization shortens time-to-market for new capabilities. When extending the system by adding new capability, we sometimes need access to capabilities present in existing modules. In modularized systems, since those modules are already in a form that permits invocation by system components, we can access them easily. We have no need to recreate them for the new capability we’re implementing. They exist, and they’re already tested and ready to go. In this way, modularization shortens time-to-market for new capability.

Modularization has a dark side

It’s a little bit of a story to show how the dark side of modularization works, so let’s give a name to the modular system element whose duplicates have been excised. Since it’s now unique, I’ll call that modular system element “U.” Any system element that interacts with U is now indirectly coupled to every other system element that interacts with U. And that’s where the trouble comes in.

When we’re implementing a new capability N, and N needs access to U, we gain the advantages described above. But suppose N needs U to do something just a little bit unusual — a little bit differently from what U now does. Sometimes we can extend U in ways that accommodate N without disturbing U’s existing “client base” — the system elements that are already interacting with U. There’s no problem then. But let’s suppose that what N now needs U to do would disturb U’s client base if we implement the changes in the “correct,” most elegant way — the way we would do it if we were starting fresh. Sadly, in that case, all of U’s existing clients would have to be modified, and then re-tested. So let’s suppose that we don’t have time or resources to do all that work. We requested them, but we were denied.

So instead, we found a way to extend U in a less elegant, less maintainable, but still reliable way that doesn’t disturb U’s existing clients, and does meet N’s needs. We do that instead, promising ourselves that we’ll go back someday, when we’re granted the time and resources, and “fix” U so that it serves both its existing clients and N in the “correct” way.

That’s one form — exactly — of what we call technical debt. In this scenario we’ve illustrated one way in which modularization leads to technical debt formation.

So a natural question arises: would it make sense instead to create a new system element — call it U2 — that meets N’s needs, and also meets the needs of U’s existing client base, if only they knew about U2 and could be altered to use U2? My proposed answer to that question is: “Yes it would, in many cases.” To create such a U2 would be demodularization — that is, a violation of modularity — and that is indeed heresy. It also creates a different technical debt: the obligation to convert U’s clients to become U2 clients someday, and then to delete U. But it might be the right approach.

When would demodularization help?

Under what conditions would demodularization be sensible? Here are three possibilities.

When a new and necessary adaptation is incompatible with existing forms

The scenario above is one situation in which demodularization can help. Demodularization helps when adding new capability, or adapting to a new need, requires a change to a shared module, and that change is incompatible with the existing uses of that module. Demodularization is than a useful technique, provided that the technical debt that results is retired with due dispatch.

When retiring technical debt requires an incompatible adaptation

A second situation arises during technical debt retirement operations. During technical debt retirement, it might be necessary to alter a shared module in a way that would be incompatible with the needs of its existing client base. In that case, the approach used above can be useful. First, create a successor (“U2”) to the original shared module (“U”) in a form that isn’t burdened with the technical debt that’s being retired. Then, at the same time, or over an extended period, convert all the clients of U to use the successor U2. In the meantime, the demodularization comprises a technical debt. When the conversion is complete, the original technical debt will have been retired. Finally delete the original shared module U, thereby retiring the technical debt that consisted of the demodularization.

This approach entails some risk. In the interim period before U is retired, when demodularization is still in place, changes to both U and U2 might be required. When that happens, duplication of effort can occur. This approach is useful, though, provided the interim period of demodularization is short compared to the anticipated intervals between incidents that require alterations to U and U2. There is risk, of course, that the resources committed to finally retiring U might become unavailable after U2 is in place. In that case, the technical debt portfolio will have been expanded to no good end. To manage this risk, the artifice of secured technical debt can prove useful.

Partial demodularization helps when adaptations are focused

In some instances, portions of a shared system element — call it “U” — evolve very rapidly, while most of the rest of U remains stable. Technical debt can accumulate rapidly if the element remains unitary — that is, in one piece. However, in some cases we can segregate the rapidly evolving portion of U into a smaller unit — call it “S.” If we provide S as a separate shared system element, those portions of the system that are experiencing rapid evolution can access S separately, without disturbing the system elements that require access only to the stable portions of U.

Such segregation might require a bit of duplication, because there might be pieces of S that are needed by U, and which must therefore be duplicated in U. Likewise, there might be pieces of U that are needed in S, and which must therefore be duplicated in S.

But the segregation might be worthwhile, because changes in S usually require testing S and S’s clients. Testing can be expensive in time and resources, and because test coverage isn’t always 100% (read: test coverage is rarely 100%), changes in S entail some operational risk. Segregating S reduces that risk by protecting U’s clients from changes in S.

Later, when the rapidly evolving S stabilizes, it can be re-integrated into its former residence in U. Until that point, its segregation — and the attendant duplications — might constitute a technical debt.

Conclusion

Accepting modularization as an inviolable design principle is one cause of unnecessary accumulation of technical debt. It makes retiring legacy technical debt more difficult. Be prepared to violate modularity, but do so judiciously.

References

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

[Sullivan 2001] Kevin J. Sullivan, William G. Griswold, Yuanfang Cai, and Ben Hallen. “The structure and value of modularity in software design,” in ACM SIGSOFT Software Engineering Notes, 26:5, 99-108, 2001.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

Crowdsourcing debt identification

I have often expressed the view that the people of the organization know where much of their technical debt is, or they can find it fairly quickly. To exploit this resource, what’s needed is a systematic method for gathering what they know to produce a database that can serve as a starting point for further investigation. We might call this part of the debt identification process “crowdsourcing debt identification.”

A crowd. Crowds are powerful when they coordinate their actions.

When an organization first undertakes to manage its technical debt, one of the many initial tasks is identifying its existing technical debt. There are tools for executing some of this task, at least for software assets, and they are useful. But because they’re in an early stage of development, and because many non-software assets also carry technical debt, human assistance is required. And that’s the place where crowdsourcing can help.

For example, if you ask engineers for examples of technical debt in the assets they work on regularly, they can rattle off a few examples without hesitation. But a few days later, while working on whatever task has focus that day, they’ll realize that they could have mentioned another painful item. And they’ll want to report it. Gathering that kind of information is very helpful to the debt identification effort. That’s crowdsourcing in action.

But investment is required for crowdsourcing to be effective. We must educate the people who will be doing the reporting, and we must give them tools to make reporting quick and easy.

Reporting issues

Crowdsourcing debt identification will produce a stream of “incident reports” by Debt Reporters (DRs) that must be interpreted by people we might call Debt Report Administrators (DRAs), who then recast the reports for later investigation by experts in the assets involved. Common difficulties that add to workload of DRAs include:

Inconsistent definitions of technical debt

Lack of uniformity in understanding what technical debt is and isn’t can cause DRs to report as potential debt items some artifacts that aren’t manifestations of technical debt, or worse, they might fail to report items that are.

Only education of the DRs about the organizational definition of technical debt can enhance consistency.

Repeated reporting of previously reported debt items

Unaware that an item has been previously reported, DRs might file reports unnecessarily. Some of these duplications are easily identified, but if the language used in the report is different enough, identifying duplicates can take time.

We can reduce duplication by making available descriptions of previously reported items in multiple forms.

Inconsistent descriptions of debt items

DRA must be able to recognize when two different DRs use different language to describe the same debt item. If they do not, then the debt report database will contain an unrecognized duplication.

The asset expert must then address this situation.

Failure to report known debt items

Some people, pressed by the urgency of their “own work,” might not report debt items they know about, or might hurriedly file low-quality reports. A high incidence of this behavior is an indicator of a deeper organizational issue: namely, that some people do not regard technical debt management as a worthy activity.

Tracking report quality and report frequency is one way to determine how much regard the people of the organization have for the debt management effort.

Report format and content

The act of reporting a potential technical debt item must not be burdensome — it must be easy. A Web-based form is a minimum. Users must be able to prefill some fields common to all their reports, and save the result as a template. Fields they might want to prefill include their personal identity and the asset identity. DRs might need several templates, depending upon the number of assets with which they interact. Switching from one template to another must also be easy.

Several authors have proposed report templates, Below is one due to Foganholi, et al. [Foganholi 2015]. (TD is technical debt)

ID	TD identification number
Date	Date of TD identification
Responsible	Person or role who should fix this TD item
Type	Design, documentation, defect, testing, or other type of debt
Project	Name of project or software application
Location	List of files/classes/methods or documents/pages involved
Description	Describes the anomaly and possible impacts on future maintenance
Estimated principal	How much work is required to pay off this TD item on a three-point scale: High/Medium/Low
Estimated interest amount	How much extra work will need to be performed in the future if this TD item is not paid off now on a three-point scale: High/Medium/Low
Estimated interest probability	How likely is it that this item, if not paid off, will cause extra work to be necessary in the future on a three-point scale: High/Medium/Low
Intentional	Yes/No/Don’t Know
Fixed by	Person or role who really fix this TD item
Fixed date	Date of TD conclusion
Realized principal	How much work was required to pay off this TD item on a three-point scale: High/Medium/Low
Realized interest amount	How much extra work was needed to be performed if this TD item was not paid off at moment of detection, on a three-point scale: High/Medium/Low

While this template might be useful for tracking the technical debt item, it contains fields that aren’t needed for crowdsourcing debt identification. A simplified template for crowdsourcing debt identification might look like this:

Identifying Report Title	Your identifier for this report
Date	Date of report (prefilled)
Type	Drop down menu of debt types, including “other”
Project	Name of the project sponsoring the work which led to your observation of the debt item
Location of debt item	List of assets involved, including specific location within complex assets
Description	Describe the debt item including Whether your current effort has created it and if so, how Possible impact on present or future maintenance or enhancement efforts Whether it has led to, or is a result of, contagion How it’s affecting your work
Intentional	Yes/No/Don’t Know

Asset experts then receive these reports and take one or more of the following actions:

Seek further information from the DR.
Reject the report as not involving technical debt. (Rejection data is used to assess the effectiveness of the education program)
Attach the report to a new or existing debt item, incorporating relevant information from the report into the debt item’s data.

What the asset experts produce, which contains information like that suggested by Foganholi, et al. will be the basis of further analysis and eventual retirement of the debt item.

Conclusions

Investment in ease-of-use for the reporting process is essential for at least three reasons:

The reporting responsibility might be seen as an addition burden beyond the current workload.
In many organizations, reporting on technical debt might be seen as a secondary responsibility.
Unless technical debt retirements rapidly become common occurrences, reporting might be seen as a waste of effort. The reporting itself must therefore be easy.

These phenomena all exert negative pressure on report quality and tend to suppress report frequency. Ease-of-use can mitigate these effects.

References

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

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

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, 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

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

June 29, 2018December 11, 2018

Leverage points for technical debt management

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

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

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

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

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

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

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

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

What are leverage points?

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

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

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

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

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

Changing systems that have delays in feedback loops

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

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

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

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

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

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

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

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

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

How to shorten delays in feedback controlling technical debt

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

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

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

Indirect effects can add to delayed recognition of technical debt

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

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

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

Conclusions

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

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

Synergy between the reification error and confirmation bias

References

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

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

Available: here; Retrieved: June 28, 2018

Cited in:

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

Available: here; Retrieved: June 2, 2018.

Cited in:

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

Order from Amazon

Cited in:

Leverage points for technical debt management

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

Using SMART goals for technical debt reduction

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

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

Prof. George T. Doran (1939-2011), creator of the S.M.A.R.T acronym for setting management objectives. Watch a 2010 interview of Prof. Doran at YouTube.

SMART is so embedded in management culture that many assume without investigation that expressing technical debt reduction goals directly using the SMART pattern will produce the desired results. Also embedded in management culture is the aphorism, “You get what you measure.” [Ariely 2010] [Bouwers 2010] Combining these two ideas in a straightforward way, one might express the technical debt reduction goal as, “Reduce the burden of technical debt by 20% per year for each of the next five years.”

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

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

Setting a direct SMART goal for technical debt reduction is problematic

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

The direct approach assumes measurability

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

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

The direct approach assumes a static principal

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

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

The direct approach focuses on MPrin, not MICs

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

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

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

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

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

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

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

Incurring technical debt is sometimes the responsible thing to do

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

How to set SMART goals for technical debt

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

Cited in:

Using SMART goals for technical debt reduction

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

Available: here; Retrieved: June 4, 2018

Cited in:

Using SMART goals for technical debt reduction

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

Cited in:

Using SMART goals for technical debt reduction

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

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

Available: here; Retrieved: June 28, 2018

Cited in:

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

Available: here; Retrieved: June 2, 2018.

Cited in:

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

Order from Amazon

Cited in:

Leverage points for technical debt management

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

Zero tolerance and work-to-rule deliveries create an adversarial culture

Last updated on February 1st, 2018 at 07:29 am

Defining technical debt at the level of specificity needed for project objectives is difficult. Confronted with this difficulty, some internal customers of technologists adopt a zero-tolerance approach to technical debt, without specifically defining technical debt. Post-delivery — sometimes much, much, post — when technical debt is discovered or recognized, technologists are held responsible, even in cases when no one could have predicted that a specific artifact would eventually come to be regarded as technical debt. This sets up an adversarial dynamic between technologists and their internal customers.

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

And that’s when the trouble begins.

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

Requiring deliverables to be totally free of technical debt contributes to formation of an adversarial culture, wherein the adversaries are the technologists and their internal customers. Shedding that adversarial culture, once it sets in, can be difficult. Compelling employees, vendors, or contractors to deliver work that’s free of all technical debt is therefore unlikely to succeed. Whether work is performed in-house by employees, or is outsourced, or is performed in-house by contractors, deliverables that meet the minimum possible interpretation of the objectives of the effort are almost certainly burdened with unacceptable levels of technical debt. What can we do to prevent this?

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

We must accept that any other forms of technical debt that remain at the end of a given project, or any constructions that later come to be recognized as technical debt, are just the “cost of doing business.” We’ll get to them, but unfortunately, not this time.

References

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

Cited in:

Using SMART goals for technical debt reduction

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

Available: here; Retrieved: June 4, 2018

Cited in:

Using SMART goals for technical debt reduction

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

Cited in:

Using SMART goals for technical debt reduction

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

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

Available: here; Retrieved: May 9, 2017.

Cited in:

Zero tolerance and work-to-rule deliveries create an adversarial culture

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

Available: here; Retrieved: June 28, 2018

Cited in:

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

Available: here; Retrieved: June 2, 2018.

Cited in:

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

Order from Amazon

Cited in:

Leverage points for technical debt management

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

Useful projections of MPrin might not be attainable

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

Summary

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

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

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

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

References

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

Cited in:

Using SMART goals for technical debt reduction

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

Available: here; Retrieved: June 4, 2018

Cited in:

Using SMART goals for technical debt reduction

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

Cited in:

Using SMART goals for technical debt reduction

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

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

Available: here; Retrieved: May 9, 2017.

Cited in:

Zero tolerance and work-to-rule deliveries create an adversarial culture

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

Available: here; Retrieved: June 28, 2018

Cited in:

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

Available: here; Retrieved: June 2, 2018.

Cited in:

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

Order from Amazon

Cited in:

Leverage points for technical debt management

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

MPrin for missing or incompletely implemented capability
In some instances, technical debt is actually a missing or incompletely implemented capability. If we retire the debt by completing the implementation, the MPrin is the cost of that effort, plus any training, testing, and lost revenue. If we retire the debt by halting or withdrawing the capability, the MPrin is the total cost of removal, plus testing and lost revenue.
MPrin when standards or regulations change
The Metaphorical Principal of a technical debt that’s incurred as a result of a change in standards or regulations, internal or external, is the cost of bringing all affected assets into full compliance. Properly accounted for, however, the MPrin should include ripple effects, which are the changes in other assets that are required to keep them compatible with the assets that are directly affected.
MPrin in platform component upgrades
Platform component upgrades often trigger the need to make changes in whatever sits atop the platform, to maintain compatibility with the platform. Those changes obviously contribute to MPrin. But less obvious are the contributions that arise from deferring the upgrade.
MPrin in a development project
The MPrin of an asset that is subjected to new development or enhancement has some special characteristics. For an existing asset, new development can lead to duplication of capability. For new assets, unanticipated opportunities can transform into technical debt components that were not viewed as technical debt, without ever changing them in any way.
Examples of MPrin in four scenarios
Some examples might help to clarify the differences between the principal of financial debts and the Metaphorical Principal of a technical debt. The examples to come in the next four posts are designed to illustrate the unique properties of MPrins of technical debts.
The metaphorical principal of a technical debt
The principal amount of a financial debt and the metaphorical principal of a technical debt have very different properties. They are so different that it’s wise to avoid using the term “principal” to refer to the metaphorical principal of a technical debt. We use the term MPrin.

November 15, 2017November 28, 2017

How technical debt can create more technical debt

Last updated on November 28th, 2017 at 11:29 am

Summary

Although the MPrin of a given can increase or decrease even if instances of that debt remain untouched, the total volume of all other technical debt can change as well. 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. When formulating technical debt management policy, it's essential to understand these mechanisms and risks.

Decisions to defer technical debt retirement must take into account phenomena that, if left unattended, can lead to (a) increasing volume of a given , (b) increasing volume of other existing classes of technical debt, and (c) generating new classes of technical debt. An example can illustrate this behavior.

Suppose Working at a computer we have a fleet of desktop computers, running a mix of operating systems. A few of these systems are running Windows 8 and the rest are running Windows 10. We’d like to upgrade the Windows 8 machines to Windows 10, but we cannot, because some of their users need access to a (fictional) scriptable application called CRUSH that is not available for Windows 10. CRUSH for Windows 10 is promised “shortly.” Instead of asking our CRUSH users to find an alternative to CRUSH, we defer the Windows 8 upgrade, in the hope that CRUSH for Windows 10 will soon arrive. Meanwhile, other Windows 8 users are happy to continue using Windows 8, and some of them have acquired — and have grown fond of using — another similar (fictional) scriptable package called REMOTE, which is also unavailable for Windows 10. Worse, the CRUSH user community is continuing to grow. Thus, by deferring the Windows 8 upgrade, we have made space for additional problems preventing the upgrade to Windows 10. The technical debt associated with the Windows 8 upgrade now includes Windows 8 itself, and all the scripts, documents, and knowledge that are accumulating for both CRUSH and REMOTE.

The lesson here is not to ban scriptable applications, nor to compel desktop users to adhere to an enterprise standard. Both options create numerous problems. The point of the example is merely that deferring debt retirement can enable formation of new instances of existing technical debt (the growth of the CRUSH user community and the assets they continue to develop) and new, unrelated debt (the introduction of REMOTE). Thus, if we make a decision to defer retirement of a class of technical debt, we must consider all costs of such deferment, including expansion of the total volume of technical debt, and all its consequences, as expressed as metaphorical interest charges and MPrin.

Some of the new technical debt that forms when we leave existing debt in place is closely related to the existing debt. For example, once we’ve implemented some part of an asset in a way that we now acknowledge contains a form of technical debt, we tend to apply that same approach when we undertake extensions or enhancements, rather than using what everyone might acknowledge is a superior approach. Martini and Bosch have identified a phenomenon they call debt contagion [Martini 2015], whereby creating new system elements in forms compatible with elements already identified as debt effectively causes debt propagation. This practice helps us maintain some degree of uniformity in the asset, recognizing that in doing so we’re increasing the MPrin of a given class of technical debt. These future expansions of MPrin can be difficult to predict at the time we first incur the debt, or at any time.

However, some forms of technical debt are far less discriminating with respect to the kinds of technical debt they spawn. Debt with this property tends to be associated with the processes used to develop or maintain technical assets. In “A policymaker’s definition of technical debt,” we cite Pugh’s example of acceptance test debt as a form of technical debt [Pugh 2010].

But acceptance test debt can reduce the ability of the organization to retire technical debt. In the absence of automated acceptance tests, testing system components from which technical debt has recently been removed is less efficient and reliable than it would be if automated acceptance tests were available, which retards debt retirement activity and which might even prevent the organization from attempting debt retirement in some circumstances. In a future post, we shall describe how a deficient regime of reviews and inspections can also lead to incurring new technical debt, or to elevated levels of legacy technical debt.

Our final example illustrates how interfaces — which, ironically, were conceived to insulate one portion of an asset from others — can act so as to propagate technical debt. This example could apply to either hardware or software. Given a system S composed of several modules, suppose that module M of S provides services of some kind to several other modules of S. M does contain some technical debt, of a form whose retirement would simplify M’s interface. Because that change would require changes to the modules that use M’s services, retiring the debt has been deferred. Meanwhile, new modules are being introduced into S, and, of course, they must use M’s existing interface. The MPrin of the technical debt associated with M’s interface thus expands.

Unless we provide an alternate version of M (call it M’) or an alternate interface to M, this process of MPrin expansion can repeat whenever new modules are introduced into S. But even if we do provide M’ or an alternate interface to M, engineers must consciously refrain from using the older approaches. Some will refrain, but some might not. Those who are under severe schedule pressure, or those who cannot or will not learn the new approaches, or those who are directed not to use the new approaches, might continue to use the familiar approaches as long as they are able. The MPrin associated with M can thus continue to expand, albeit perhaps at a reduced rate.

Technical debt, left in place, can grow and spawn new forms of technical debt. Make technical debt retirement a priority.

References

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

Cited in:

Using SMART goals for technical debt reduction

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

Available: here; Retrieved: June 4, 2018

Cited in:

Using SMART goals for technical debt reduction

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

Cited in:

Using SMART goals for technical debt reduction

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

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

Available: here; Retrieved: May 9, 2017.

Cited in:

Zero tolerance and work-to-rule deliveries create an adversarial culture

[Martini 2015] A. Martini and J. Bosch. “The danger of architectural technical debt: Contagious debt and vicious circles,” Working IEEE/IFIP Conf. Softw. Arch., 2015.

Cited in:

Debt contagion: how technical debt can create more technical debt

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

Available: here; Retrieved: June 28, 2018

Cited in:

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

Available: here; Retrieved: June 2, 2018.

Cited in:

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

Order from Amazon

Cited in:

Leverage points for technical debt management

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

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

Cited in:

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

MPrin for missing or incompletely implemented capability
In some instances, technical debt is actually a missing or incompletely implemented capability. If we retire the debt by completing the implementation, the MPrin is the cost of that effort, plus any training, testing, and lost revenue. If we retire the debt by halting or withdrawing the capability, the MPrin is the total cost of removal, plus testing and lost revenue.
MPrin when standards or regulations change
The Metaphorical Principal of a technical debt that’s incurred as a result of a change in standards or regulations, internal or external, is the cost of bringing all affected assets into full compliance. Properly accounted for, however, the MPrin should include ripple effects, which are the changes in other assets that are required to keep them compatible with the assets that are directly affected.
MPrin in platform component upgrades
Platform component upgrades often trigger the need to make changes in whatever sits atop the platform, to maintain compatibility with the platform. Those changes obviously contribute to MPrin. But less obvious are the contributions that arise from deferring the upgrade.
MPrin in a development project
The MPrin of an asset that is subjected to new development or enhancement has some special characteristics. For an existing asset, new development can lead to duplication of capability. For new assets, unanticipated opportunities can transform into technical debt components that were not viewed as technical debt, without ever changing them in any way.
Examples of MPrin in four scenarios
Some examples might help to clarify the differences between the principal of financial debts and the Metaphorical Principal of a technical debt. The examples to come in the next four posts are designed to illustrate the unique properties of MPrins of technical debts.
The metaphorical principal of a technical debt
The principal amount of a financial debt and the metaphorical principal of a technical debt have very different properties. They are so different that it’s wise to avoid using the term “principal” to refer to the metaphorical principal of a technical debt. We use the term MPrin.

November 15, 2017May 21, 2019

Policy implications of the properties of MPrin

Last updated on May 21st, 2019 at 05:38 pm

Formulating sound policy vis-à-vis technical debt requires a thorough understanding of the distinction between the MPrin associated with a technical debt and the principal amount of a financial debt. There are three fundamental differences between them.

MPrin can change spontaneously

For most financial debts, a formula determines the principal amount. Voluntary actions of the debtor can also affect the principal amount. For example, the debtor might make periodic payments on an installment loan, or new purchases on a credit card account. By contrast, MPrin of a technical debt can change absent any action by the “borrower.” For example, changes in regulations, standards, or technologies can all cause changes in MPrin. More: “How MPrin can change spontaneously”

Technical debt can create more technical debt

Technical debt left in place can create more technical debt without the knowledge or consent of the debtor organization. By contrast, the principal amount of a financial debt can grow, but law or regulation requires notification—and in some cases consent—of the debtor. More: “How MPrin can change spontaneously”

Projecting MPrin with useful precision might not be possible

The cost of retiring a technical debt can depend on how the asset bearing the debt has changed over the life of the debt. And it can depend on what other projects the enterprise is executing debt retirement time. These factors are difficult to predict. By contrast, projecting the principal amount of a financial debt is formulaic. More: “Useful projections of MPrin might not be attainable”

A pole full of wires. Technical debt is everywhere.

The policy implications of these properties of MPrin can be profound. The possibility of spontaneous change in MPrin implies a need for investments in market and technological intelligence focused specifically on potential effects on technical debt. Moreover, existing technical debt can cause the creation of new instances of that debt or other debts. This “contagion” implies a need for awareness of what kinds of technical debt are most likely to exhibit this phenomenon. Finally, the difficulty of projecting MPrin implies that typical reliance on analytical modeling of enterprise asset evolution in preference to human judgment may be misplaced. A wiser course might be investment in employee retention programs focused on the individuals who can provide the necessary wisdom.

This is just a sketch of the problems policymakers confront when dealing with the properties of MPrin. I’ll be addressing them in more detail in future posts.

References

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

Cited in:

Using SMART goals for technical debt reduction

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

Available: here; Retrieved: June 4, 2018

Cited in:

Using SMART goals for technical debt reduction

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

Cited in:

Using SMART goals for technical debt reduction

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

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

Available: here; Retrieved: May 9, 2017.

Cited in:

Zero tolerance and work-to-rule deliveries create an adversarial culture

[Martini 2015] A. Martini and J. Bosch. “The danger of architectural technical debt: Contagious debt and vicious circles,” Working IEEE/IFIP Conf. Softw. Arch., 2015.

Cited in:

Debt contagion: how technical debt can create more technical debt

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

Available: here; Retrieved: June 28, 2018

Cited in:

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

Available: here; Retrieved: June 2, 2018.

Cited in:

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

Order from Amazon

Cited in:

Leverage points for technical debt management

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

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

Cited in:

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

MPrin for missing or incompletely implemented capability
In some instances, technical debt is actually a missing or incompletely implemented capability. If we retire the debt by completing the implementation, the MPrin is the cost of that effort, plus any training, testing, and lost revenue. If we retire the debt by halting or withdrawing the capability, the MPrin is the total cost of removal, plus testing and lost revenue.
MPrin when standards or regulations change
The Metaphorical Principal of a technical debt that’s incurred as a result of a change in standards or regulations, internal or external, is the cost of bringing all affected assets into full compliance. Properly accounted for, however, the MPrin should include ripple effects, which are the changes in other assets that are required to keep them compatible with the assets that are directly affected.
MPrin in platform component upgrades
Platform component upgrades often trigger the need to make changes in whatever sits atop the platform, to maintain compatibility with the platform. Those changes obviously contribute to MPrin. But less obvious are the contributions that arise from deferring the upgrade.
MPrin in a development project
The MPrin of an asset that is subjected to new development or enhancement has some special characteristics. For an existing asset, new development can lead to duplication of capability. For new assets, unanticipated opportunities can transform into technical debt components that were not viewed as technical debt, without ever changing them in any way.
Examples of MPrin in four scenarios
Some examples might help to clarify the differences between the principal of financial debts and the Metaphorical Principal of a technical debt. The examples to come in the next four posts are designed to illustrate the unique properties of MPrins of technical debts.
The metaphorical principal of a technical debt
The principal amount of a financial debt and the metaphorical principal of a technical debt have very different properties. They are so different that it’s wise to avoid using the term “principal” to refer to the metaphorical principal of a technical debt. We use the term MPrin.

November 13, 2017December 11, 2018

Balance technical debt and engineering resources

Last updated on December 11th, 2018 at 09:42 am

Improving organizational effectiveness in technical debt management—or avoiding incurring new technical debt—should create significant savings, and many competitive advantages. These benefits should arise from the reductions in metaphorical interest charges that result from retiring technical debt. But these benefits become available to the organization only if engineering capacity increases relative to the burdens presented by the remaining reduced levels of technical debt. After the technical debt management program is in place, if the balance between engineering resources and the burdens imposed by the remaining technical debt becomes more favorable, then organizational effectiveness will improve. But if the balance becomes less favorable, as a result of reductions in engineering resources, organizational effectiveness won’t improve, even at lower levels of technical debt.

Flooding from Hurricane Katrina in New Orleans, 2005. Any levee humans can build can be overtopped or undermined by the forces of Nature. So it is with technology. Any technology humans can devise to attain mastery over technical debt can be overcome or undermined by organizational policy and organizational politics. To master technical debt, technology is not enough—we must also deal with policy and politics.

Unfortunately, it’s possible to adopt advanced technical debt management practices while at the same time reducing engineering capacity to a level such that engineering effectiveness is no better than it was before the technical debt management program was initiated. The reason for this is that the engineering process is not the sole cause of technical debt. Improving the engineering process to eliminate technical causes of technical debt leaves non-technical causes in place. That’s why technological solutions to the technical debt management problem might not be sufficient to produce benefits in organizational effectiveness and agility.

The focus of research in technical debt management has been on technology—recognition of technical debt, its measurement, representation, retirement, and so on. Progress on improving the engineering process has been significant, especially in software engineering, where a clear “research roadmap” has been developed [Izurieta 2017]. It’s reasonable to assume that effective tools for automating or partially automating technical debt detection and retirement will be widely available and very generally effective in the not-too-distant future, at least for software. But progress has not been confined to debt detection and retirement. Avoiding technical debt formation to the extent possible is much preferable, and in some contexts, it’s practical even today, as Trumler and Paulisch suggest [Trumler 2016].

But it’s also reasonable to ask whether such developments will have much impact on the limiting effects of carrying technical debt, even in software. Given the necessary resources, much of the technical debt now extant could be retired. That is, debt retirement rates are determined only by the will and the capacity to invest in debt retirement. Currently, the levels of will and capacity for such activity are insufficient. But if new methods for managing technical debt become available, one might wonder whether organizations will apply resources sufficient to ensure that they actually experience a reduction in the limiting effects of technical debt.

The open question is this: will technological developments alone suffice to gain control of the problem of technical debt? Perhaps not. Organizations could exploit the advancements in technical debt management to execute reductions in engineering staffing—and therefore cost—while they divert savings to other parts of the enterprise, thereby allowing technical debt to remain at levels that, although much reduced, are nevertheless sufficient to compromise the effectiveness of that reduced engineering staff.

For example, schedule pressure is widely recognized as contributing to technical debt formation and persistence. If engineering groups become more adept at managing and preventing technical debt, but marketing and sales groups do not improve their own intelligence and planning processes and consequently demand new capabilities with even shorter timelines than they now do, the enterprise might not benefit from the new technical debt management capabilities, even though the burden of technical debt has been reduced.

Until we have evidence of significant change in the behavior of non-technologists—or even acknowledgment that their behavior contributes to debt formation—we can expect the effects of non-technical causes of technical debt to persist, and possibly even to grow.

This blog focuses on the non-technical etiology of technical debt formation and persistence, and approaches for managing it. Watch this space.

References

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

Cited in:

Using SMART goals for technical debt reduction

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

Available: here; Retrieved: June 4, 2018

Cited in:

Using SMART goals for technical debt reduction

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

Cited in:

Using SMART goals for technical debt reduction

Available: here; Retrieved: July 7, 2018

Cited in:

Crowdsourcing debt identification

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

Cited in:

Undercounting nonexistent debt items

[Izurieta 2017] Clemente Izurieta, Ipek Ozkaya, Carolyn Seaman, and Will Snipes. “Technical Debt: A Research Roadmap: Report on the Eighth Workshop on Managing Technical Debt (MTD 2016),” ACM SIGSOFT Software Engineering Notes, 42:1, 28-31, 2017. doi:10.1145/3041765.3041774

Cited in:

Balance technical debt and engineering resources

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

Available: here; Retrieved: May 9, 2017.

Cited in:

Zero tolerance and work-to-rule deliveries create an adversarial culture

[Martini 2015] A. Martini and J. Bosch. “The danger of architectural technical debt: Contagious debt and vicious circles,” Working IEEE/IFIP Conf. Softw. Arch., 2015.

Cited in:

Debt contagion: how technical debt can create more technical debt

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

Available: here; Retrieved: June 28, 2018

Cited in:

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

Available: here; Retrieved: June 2, 2018.

Cited in:

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

Order from Amazon

Cited in:

Leverage points for technical debt management

[Orton 1990] J. Douglas Orton and Karl E. Weick. “Loosely Coupled Systems: A Reconceptualization,” The Academy of Management Review, 15:2, 203-223, 1990.

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Parnas 1979] David L. Parnas. “Designing Software for Ease of Extension and Contraction,” IEEE Transactions on Software Engineering, vol. SE-5, no. 2, March 1979, 128-138.

Available: here; Retrieved: July 13, 2017

Cited in:

Demodularization can help control technical debt

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

Cited in:

Available: here; Retrieved: July 11, 2018.

Cited in:

Demodularization can help control technical debt

[Trumler 2016] Wolfang Trumler and Frances Paulisch. “How ‘Specification by Example’ and Test-driven Development Help to Avoid Technical Debt,” IEEE 8th International Workshop on Managing Technical Debt. IEEE Computer Society, 1-8, 2016. doi:10.1109/MTD.2016.10

Cited in:

Balance technical debt and engineering resources

Modularization is a widely used approach to complex system design

Modularization has a dark side

When would demodularization help?

When a new and necessary adaptation is incompatible with existing forms

When retiring technical debt requires an incompatible adaptation

Partial demodularization helps when adaptations are focused

Conclusion

References

Other posts in this thread

Related posts

Share this:

Reporting issues

Inconsistent definitions of technical debt

Repeated reporting of previously reported debt items

Inconsistent descriptions of debt items

Failure to report known debt items

Report format and content

Conclusions

References

Other posts in this thread

Related posts

Share this:

Mitigating the risk of undercounting nonexistent debt items

References

Share this:

What are leverage points?

Changing systems that have delays in feedback loops

How to shorten delays in feedback controlling technical debt

Indirect effects can add to delayed recognition of technical debt

Conclusions

References

Other posts in this thread

Share this:

Setting a direct SMART goal for technical debt reduction is problematic

The direct approach assumes measurability

The direct approach assumes a static principal

The direct approach focuses on MPrin, not MICs

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

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

Incurring technical debt is sometimes the responsible thing to do

How to set SMART goals for technical debt

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

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

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

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

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

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

References

Other posts in this thread

Share this:

References

Other posts in this thread

Share this:

References

Related posts

Share this:

References

Related posts

Share this:

MPrin can change spontaneously

Technical debt can create more technical debt

Projecting MPrin with useful precision might not be possible

References

Related posts

Share this:

References

Related posts

Share this: