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Measure maintenance after the initial implementation, not just how quickly the first version was written. Compare AI-assisted changes with a credible control, then track active time spent on review, rework, bug fixing and later adaptation—alongside code quality and whether a different developer can safely evolve the code. Keep initial delivery speed separate: faster implementation alone does not show that maintenance effort fell.
Define what counts as maintenance effort
Before collecting data, specify the outcome your team means by “maintenance effort.” A useful primary measure is total active engineering time spent maintaining an accepted change during a fixed follow-up period. Report that alongside initial implementation time, not folded into it.
Decide in advance whether the maintenance measure includes code review, rework, bug fixes, incident remediation, dependency updates and later feature adaptation. Classify these activities separately where feasible. Otherwise, a shift from bug fixing to review work—or from developer time to reviewer time—can disappear inside one total.
- Use active time, not elapsed calendar time, as the labor measure. Calendar time can reflect waiting, queueing and competing priorities as well as engineering work.
- Report effort per accepted change and total effort. The first helps compare changes of different volumes; the second shows the overall burden. Do not treat either as a measure of value by itself.
- Set the follow-up window before the comparison begins. Use the same window for both groups and make it long enough to observe the maintenance work relevant to your release cycle.
- Record the work category and task difficulty. Raw counts of follow-up changes or lines of code are not substitutes for effort or correctness.
Compare AI-assisted work with a credible control
The central question is counterfactual: what maintenance would the same kind of change have required without the AI-assisted workflow? A simple before-and-after comparison can be misleading if tasks, developers, repositories, or tool versions changed at the same time.
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Randomly assign comparable tasks or developers to an AI-enabled workflow and a control workflow. Keep the task definition, review expectations and follow-up period consistent. Record both whether the tool was available and whether it was actually used, so assignment and exposure are not conflated.
When you are evaluating a rollout
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Do not treat different study designs as equivalent. A controlled experiment, an organizational field experiment and an observational analysis of adoption answer different questions and support different levels of causal confidence.
Track labor, code outcomes and who bears the work
Use several measures because no single metric captures maintenance. A practical evaluation should combine observed work with artifact quality and the experience of the developers maintaining the code.
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- Active maintenance time: record review, rework, bug fixing and feature adaptation separately when possible.
- Follow-up changes: count and classify changes by purpose; record size as context, not as a proxy for quality or effort.
- Resolution and defect outcomes: track maintenance-ticket and escaped-defect time to resolution, with severity and task difficulty.
- Review distribution: measure reviewer time and whether work concentrates among senior or core maintainers.
- Independent evolution: ask a developer who did not author the initial change to make a defined follow-on change; measure both completion time and correctness.
- Quality and maintainability: choose definitions in advance for any complexity, code-smell or structural measures. Treat them as supporting indicators rather than direct measurements of labor.
- Developer sentiment: collect perceived effort or confidence as a separate subjective outcome, not as a replacement for observed work.
Google Research’s 2025 study illustrates triangulation across architectural complexity, maintenance activity and developer sentiment. It examined more than 1,200 C++ and Java projects and 7,200 survey responses. Its measures included propagation cost, decoupling level and structural anti-patterns; changes, lines of code and active coding time split between feature work and bug fixing; and survey responses. In that dataset, greater propagation cost and structural anti-patterns were associated with more lines of code spent on bug fixing. That association does not establish that a particular tool caused the complexity or the maintenance work.
Use maintainability scores as supporting evidence
A code-quality score can make artifact comparisons more repeatable, but it cannot tell you how much engineering time was spent. In the controlled maintainability study, researchers used CodeScene CodeHealth alongside an evolution task performed by another developer.
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The paper describes CodeScene as a commercial tool. Its file-level CodeHealth score runs from 1 to 10: 10 means no detected code smells, and aggregate scores are weighted by file size. Because the score penalizes detected smells, it is an indicator under that tool’s definitions—not a direct measure of maintenance labor or a universal measure of maintainability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available studies show—and what they do not
The findings do not support a universal claim that AI coding tools either reduce or increase maintenance effort. They do show why initial speed, downstream work and the distribution of review need to be measured separately.
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| Study | Design and scope | Finding relevant to maintenance | What not to infer |
|---|---|---|---|
| Borg et al., Empirical Software Engineering, 2026 | Preregistered two-phase experiment with 151 participants, 95% of whom were professional developers. Participants built a Java web-app feature with or without AI; a different group then evolved the resulting code without AI. The experiment took place in late 2024. | AI use was associated with a 30.7% median reduction in initial task completion time. For the follow-on evolution task, the study found no significant treatment-control difference in completion time or code quality. | The initial speed result is not evidence of lower downstream maintenance. The follow-on result is bounded to this task and participant pool, and predates the current coding-agent wave. |
| Google Research, 2025 | Analysis of more than 1,200 C++ and Java projects and 7,200 survey responses, combining architectural, activity and sentiment measures. | Greater propagation cost and structural anti-patterns were associated with more lines of code devoted to bug fixing in the dataset. | The association does not by itself establish causation or estimate the effect of adopting an AI coding tool. |
| Xu et al., 2025 | Observational analysis of open-source projects after Copilot adoption. | The study reported more rework; core developers reviewed 6.5% more code and experienced a 19% decline in original-code productivity. | These are study-specific observational findings, not universal causal estimates for organizations or current agent products. |
| Cui et al., Microsoft Research, 2025 | Three organizational field experiments involving 4,867 developers, measuring task completion with an AI coding assistant. | The combined result was a 26.08% increase in completed tasks, with a standard error of 10.3%. Less experienced developers had higher adoption and greater reported productivity gains. | Task throughput is not a long-term maintenance-effort estimate; the result should not be read as evidence that maintenance costs fell. |
Taken together, the controlled experiment directly tested whether other developers could evolve AI-assisted code and found no significant difference for its follow-on task. The open-source study highlights a possible distributional effect in which more review and rework land on experienced maintainers. The field experiments provide evidence about task throughput, not long-term maintenance. These results are useful for shaping a local evaluation, not for predicting a guaranteed outcome in every team.
Interpret the results without mistaking speed for savings
Report initial implementation time separately from downstream maintenance, and show the component measures rather than relying on one headline number. A change can be faster to implement yet require more reviewer attention or rework later. Conversely, a code-quality indicator can improve without proving that active maintenance hours declined.
Do not use lines of code, commit counts, accepted completions or developer sentiment alone as evidence that maintenance effort fell. They can help describe activity or experience, but they do not establish the labor required to keep code correct and adaptable.
When publishing or sharing a result, state the workflow, tool generation and version, population, task types, comparison design and follow-up window. Those details determine what the result applies to—and whether another team can reasonably compare it with its own.
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