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Automation-assisted technical debt retirement

Last updated on July 16th, 2021 at 04:23 pm

Collapse of the I-35W bridge in Minneapolis, Minnesota
The I-35W Bridge collapse, day 4, Minneapolis, Minnesota, August 5, 2007. The proximate cause of the collapse was underweight gusset plate design, which made the bridge vulnerable to the increased static load due to concrete road surfacing additions over the years, and to the weight of construction equipment and supplies during a repair project that was then underway [NTSB 2008].

When we conduct maintenance or technical debt retirement projects involving assets that must remain operational during project execution, we risk stressing the asset in ways that extend beyond its safe operation envelope. The National Transportation Safety Board found that this occurred in the case of the I-35W bridge collapse. These effects are more difficult to imagine in software systems, but they can occur when we shift load from the systems undergoing modification to other systems. Those load-bearing systems can then become overloaded. Or these effects can occur when we shift load not from one asset to another, but from one time window to another on the same asset. The result can be high loads in some time windows.

Photo by Kevin Rofidal, United States Coast Guard, courtesy Wikimedia Commons.

When we transform assets to retire some of the technical debt they carry, service disruptions are sometimes necessary. To minimize service disruptions while technical debt retirement efforts are underway, it’s advantageous to automate some procedures. Automation-assisted technical debt retirement provides two important benefits: reduced disruption of operations and reduced incidence of errors.

The meaning of automation

I’m using the concept of automation a bit loosely here. I don’t mean to imply that these procedures are autonomous. What I mean is that engineers have available tools for performing many operations with a minimum of thought. For example, in this sense of automated, an engineer can issue a command such as, “Test Module Alpha Using Test Suite Delta.” That command executes a predefined set of tests. Following execution, the appropriate engineers receive the results. The tool also archives those results appropriately. If the results are anomalous, engineers can then take appropriate action.

Benefits of automation-assisted technical debt retirement

The more obvious benefit of automated procedures is speed. For example, an asset removed from service for testing can be returned to service more quickly if the testing is automated. And if trouble erupts during operations of a newly transformed asset, engineers can swap the untransformed asset back into place quickly. So-called roll-out and roll-back tools are just a few of the many elements of a set of practices collectively known as continuous delivery [Humble 2010].

The second benefit of this kind of automation is error avoidance. For example, inconsistent or incomplete testing can fail to find errors and defects, and that leads to rework and further disruptions. Performing tests incorrectly, finding “defects” that aren’t there, is another way to generate trouble. Automated procedures are much less prone to error if we maintain, test, and certify them periodically. For example, consider subjecting a module to a particular test suite. With automation assistance, engineers needn’t remember (or take time to look up) how to prepare the asset for tests. They  needn’t remember (or take time to look up) how to run the tests, or what the members of the test suite are. Long advocated as an essential element of sound engineering practice, test automation can avoid some of these problems. But it’s far short of a panacea [Bach 1999].

Other automation opportunities

In some situations, we can automate debt retirement itself. When we can retire instances of the technical debt in question by performing an automated transformation on an asset, the transformation is faster and more reliable.

A most important practice associated with automation-assisted technical debt retirement is automation-assisted regression testing. Investments in thorough and focused regression testing have potentially shockingly high returns in the debt retirement context. They can be just as valuable during development and routine maintenance.

To perform a regression test on an asset that has undergone a change is to examine its behavior under a specified set of conditions. Such investigations can determine whether those changes caused the asset to misbehave. So a regression test determines whether the asset has regressed as a result of the change. Automated or automation-assisted regression tests help the project team detect problems in assets that they’ve transformed. And that’s much better than having the business units that depend on those assets encounter problems during operations [Ge 2014].

Many of these same regression tests can also be useful during enhancement and ongoing maintenance of the asset. Often, investing in automated regression tests in advance of the debt retirement project can enhance development and maintenance performance relative to those assets. Later, when the debt retirement project begins, the previously obtained results of regression tests will already be available.

Last words

For some debt retirement projects, specially created automated regression tests might be beneficial. Assign engineers to continual automation tool development for debt retirement projects. That’s probably the best way to support these needs.

These automation capabilities are unlikely to be available commercially, because they’re so specialized to the asset being tested. Because general applicability is unnecessary, building them in-house is both practical and economical.  If people with the necessary skills are unavailable, acquire them. We can justify these investments economically if we take into account the savings from reduced service disruptions during technical debt retirement projects.

References

[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).

Available: here; Retrieved: January 2, 2019

Cited in:

[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.

Available: here; Retrieved: January 1, 2019

Cited in:

[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.

Cited in:

[NTSB 2008] National Transportation Safety Board. “Board Meeting Executive Summary: Collapse of I-35W Highway Bridge, Minneapolis, Minnesota, August 1, 2007,”, November 13, 2008.

Available: here; Retrieved: January 3, 2019.

Cited in:

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Undercounting nonexistent debt items

Last updated on July 10th, 2021 at 08:55 am

Sherlock Holmes and Doctor Watson, in an illustration by Sidney Paget
Sherlock Holmes and Doctor Watson, in an illustration by Sidney Paget, with the caption, “Holmes gave me a sketch of the events.” In 1892 The Strand magazine published this illustration 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 isn’t there, and failing to notice it. It’s an example of absence blindness.

Original book illustration, courtesy Wikimedia Commons.

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. 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.

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.

Selection bias and absence blindness

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 data 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 face a problem. We can’t be certain that we haven’t changed something important. And so, when a survey design doesn’t mitigate the effects of selection bias, we can expect an elevated probability of failing to note any missing regression tests.

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

[Bach 1999] James Bach. “Test Automation Snake Oil!” (1999).

Available: here; Retrieved: January 2, 2019

Cited in:

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

Cited in:

[Ge 2014] Xi Ge and Emerson Murphy-Hill. “Manual Refactoring Changes with Automated Refactoring Validation,” Proceedings of the 36th International Conference on Software Engineering. ACM, 2014.

Available: here; Retrieved: January 1, 2019

Cited in:

[Humble 2010] Jez Humble and David Farley. Continuous delivery: reliable software releases through build, test, and deployment automation, Pearson Education, 2010.

Cited in:

[NTSB 2008] National Transportation Safety Board. “Board Meeting Executive Summary: Collapse of I-35W Highway Bridge, Minneapolis, Minnesota, August 1, 2007,”, November 13, 2008.

Available: here; Retrieved: January 3, 2019.

Cited in:
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