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What counts as software maintenance cost?
Maintenance is not just the time spent typing a patch. It includes understanding existing behavior, making a change, reviewing and testing it, fixing defects, and adapting the software again as requirements or environments change. ISO 25010 defines maintainability as the effectiveness and efficiency with which a product or system can be modified to improve or correct it, or adapt it to changed requirements or environments. Borg and colleagues quote the standard’s 2011 definition in their 2026 study.
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Technical debt is one way a short-term choice can raise future costs: a design or implementation decision made for expediency may make later changes more difficult or even impossible. AI assistance does not automatically create technical debt, but generated code still needs to be judged by what it makes possible—or harder—for future maintainers.
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An assistant may help draft routine code, explain unfamiliar code, suggest tests, or propose a review fix. These are plausible ways to reduce effort on a particular task. They are not, by themselves, evidence that the full maintenance cycle became cheaper. Suggestions can require substantial checking or revision, and a quick patch can still be costly if it introduces defects or makes subsequent changes harder.
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The relevant comparison is net effort: include the time spent prompting and integrating suggestions, review and correction, test work, and later changes—not only the time to produce the first version. An organization should assess its own tasks and workflow rather than assume that a result from a different tool, team, or codebase will transfer.
What the available studies show
| Evidence | Finding | What it does—and does not—show |
|---|---|---|
| Borg et al., Empirical Software Engineering (2026) | In Phase 1, the study reported a 30.7% median reduction in completion time for the initial feature task with AI assistance. The two-phase study involved 151 participants, 95% of whom were professional developers. | In Phase 2, new developers evolved the resulting Java web application solutions without AI assistance. The researchers detected no significant difference in their completion time or code quality. This bounded result is not proof that all AI-generated code is equally maintainable. |
| GitHub randomized study (2024; updated 2025) | In a task involving one API endpoint, the final valid sample comprised 202 experienced developers. GitHub reported statistically significant gains on several tested code-quality measures, including a 2.47% improvement in maintainability ratings. | This was a company-authored study of one task, with unit tests and expert review. It did not measure maintenance costs over months or years, and a rating improvement is not a measured reduction in maintenance spending. |
| DORA / Google, 2025 | The report drew on more than 100 hours of qualitative data and responses from nearly 5,000 technology professionals. It describes AI as an amplifier of organizational strengths and weaknesses. | This supports considering the team’s delivery system, not just its choice of tool. It is not a randomized estimate of how much an individual team’s maintenance costs will change. |
| UK government trial, as reported by IT Pro in 2025 | More than 1,000 workers across 50 UK government departments tested tools from Microsoft, GitHub, and Google between November 2024 and February 2025. The report described around one hour saved per day—equivalent to around 28 working days per year—and said 15% of AI-generated code was used without edits. | These are figures from a government trial as summarized by a secondary source, not a controlled study of long-term maintenance costs. The reported editing figure also underscores that generated output often needs work before use. |
The downstream study’s null result has important boundaries: it examined a Java web application task and AI tools available in late 2024. Its authors note that autonomous coding agents were not represented in that period’s empirical results. It therefore cannot settle how today’s agents affect long-run maintenance in production systems.
Why the same tool can produce different outcomes
AI’s effect depends on the task, the quality of review and testing, the team’s feedback loops, and the practices around delivery. DORA’s 2025 report puts the organizational point plainly: “AI’s primary role in software development is that of an amplifier. It magnifies the strengths of high-performing organizations and the dysfunctions of struggling ones.” A team with reliable tests and careful review has safeguards for evaluating suggestions; a team without them may have less ability to catch mistakes before they become expensive.
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- Testing: Tests help reveal whether a change behaves as intended and whether it breaks existing behavior. Their presence does not guarantee correctness, but weak coverage makes it harder to assess generated changes.
- Review: Reviewers need to evaluate behavior, design, and fit with the surrounding code—not just whether a patch looks plausible.
- Feedback and ownership: Teams need a way to identify defects and improve their use of AI over time, with clear responsibility for the code that is merged.
- Task and tool fit: A repetitive, well-understood change is not the same as a delicate modification to poorly understood legacy code. Results on one task should not be generalized to every kind of maintenance.
How to tell whether AI is lowering your costs
Compare similar maintenance tasks done with and without AI, using the same quality bar and recording the full effort rather than just the initial coding interval. Separate these measures so a speed gain cannot hide a rise in rework or defects:
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- Immediate task time: Record elapsed or active time to complete the change, including time spent interacting with the assistant.
- Review and rework: Track reviewer effort, requested changes, corrections, and time spent investigating suggestions that were not usable.
- Correctness and regressions: Record test outcomes and defects found before release, as well as defects that escape into use.
- Maintainability signals: Where the team already uses them, track code smells, complexity, or other consistent indicators. For example, Borg and colleagues describe CodeScene’s CodeHealth metric as measuring code smells; such a signal is not itself a maintenance-cost measure.
- Later change effort: When comparable follow-up work occurs, record how long another developer takes to understand and modify the code, and whether that change introduces problems.
Use a consistent definition of “task complete,” compare like with like, and review the results over enough work to avoid letting one unusually easy or difficult task decide the outcome. If the organization cannot yet measure downstream effects, treat a quick first change as an early signal, not proof of lifecycle savings.
Using AI on legacy code
Legacy work often involves incomplete tests, hidden dependencies, and behavior that is not documented. In that setting, a proposed change may look straightforward while relying on assumptions the team has not verified. Establishing a feedback loop—such as tests that capture existing behavior—can make changes safer to evaluate before relying on generated code.
Michael Feathers’s Working Effectively with Legacy Code is a practical, non-AI-specific reference on feedback, test harnesses, dependencies, safe changes, and refactoring. Its 2004 publication means it does not cover generative AI, but those maintenance concerns remain relevant when deciding how to change older systems.
A practical adoption rule
Use AI for a maintenance task when your team can review and test the output, and your measured net effort improves without weakening the safeguards needed for future changes. If faster initial completion comes with more correction, weaker confidence, or greater later-change effort, the apparent saving may not be a real maintenance saving.
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