Use AI coding assistants as contributors to a team’s engineering system, not as substitutes for its standards or shared understanding. Keep people accountable for accepting changes, validate behavior with tests and review, apply security checks where relevant, and record the intent and ownership of non-obvious work in ordinary engineering artifacts. The evidence does not show one universal effect on code quality or long-term knowledge retention; results vary by task and setting.
What does the evidence say about AI-assisted code quality?
There is no single result that establishes whether AI coding improves code quality in every team. Studies measure different tasks and outcomes, so use them to set expectations—not to promise production results.
| Evidence | What was studied | Reported result | How to interpret it |
|---|---|---|---|
| GitHub controlled task study, 2025 | 202 valid participants with at least five years of Python experience built API endpoints for a fictional restaurant-review web server. Unit tests and blinded developer reviews assessed the work. GitHub reported statistically significant ratings on this single task. | Participants with Copilot access were 53.2% more likely to pass all ten unit tests and 5% more likely to receive code approval. GitHub also reported improvements of 3.62% in readability, 2.94% in reliability, 2.47% in maintainability, and 4.16% in concision. | This was a bounded task with experienced Python developers, not a test of every language, codebase, or production workflow. These company-reported findings do not guarantee equivalent results elsewhere. |
| Open-source repository study, 2024 preprint | Song, Agarwal, and Wen analyzed GitHub open-source project data using a generalized synthetic control method. | The authors reported 6.5% higher project-level productivity, 5.5% higher individual productivity, 5.4% more participation, 41.6% higher integration time, and no change in measured code quality. | These results concern the analyzed open-source projects, not enterprise teams in general. The increased integration time is a reminder that faster contribution does not necessarily mean lower total delivery effort. |
The studies are not contradictory: one measured a particular coding task under controlled conditions, while the other analyzed collaboration and project outcomes in open-source repositories. They show why teams should measure both quality and the work required to integrate changes.
Why do team practices matter as much as the assistant?
DORA’s 2025 report describes AI as an amplifier: “AI’s primary role is as an amplifier, magnifying an organization’s existing strengths and weaknesses.” The report says the greatest returns come from strategic focus on the underlying organizational system, rather than tools in isolation. Its companion capability model describes seven capabilities, with implementation strategies, team tactics, and ways to monitor progress.
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This is organizational guidance, not proof that any single practice independently causes better code or preserves knowledge. The practical implication is to assess the workflow around an assistant: whether teams have clear standards, can review changes effectively, and can integrate and support what they ship. DORA’s 2024 report says it heard from more than 39,000 professionals across organizations of varied sizes and industries globally; that is the report’s stated respondent reach, not the sample size for every finding or a direct measure of AI’s causal impact.
What should teams require before AI-assisted code is merged?
Use the same team-owned acceptance standards for AI-assisted changes as for other changes. A fluent explanation from an assistant is not evidence that a change is correct, maintainable, or secure. Set review expectations according to the change’s behavior and risk.
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Validate behavior
- Ask the author to describe the intended behavior and relevant edge cases in the pull request.
- Run the project’s tests and add or revise tests that demonstrate the changed behavior. A passing suite is evidence about the cases it covers, not proof that all behavior is correct.
- Review whether the implementation fits the surrounding design and local conventions, rather than assessing only whether it runs.
Review design and maintainability
- Check whether the change introduces unnecessary complexity, duplication, unclear abstractions, or assumptions that future maintainers will struggle to recover.
- Inspect dependency changes and their purpose, scope, and compatibility with team policy.
- Ask a reviewer to evaluate the code itself. Do not treat generated summaries or explanations as a replacement for examining the diff.
Apply security scrutiny to security-relevant changes
- Review authentication, authorization, data handling, input validation, cryptography, and other threat-relevant logic with the team’s established security process.
- Use the security checks appropriate to the code and repository, including dependency or static-analysis checks where the team already relies on them.
- Do not infer security from functionality: code that passes tests or appears to work can still expose a vulnerability.
A CCS 2024 qualitative study combined 27 interviews with analysis of Reddit discussions. It found that professionals used coding and general-purpose AI assistants for security-related work such as generation, threat modeling, review, and vulnerability detection, while also reporting mistrust and checking suggestions. The authors observed a mismatch between reported scrutiny and security outcomes, and noted that functionality may serve as a proxy for security. This qualitative sample does not establish how common these practices are across all developers; it does reinforce the need to assess security outcomes rather than confidence or apparent functionality alone.
How can teams keep knowledge from being lost?
Code is part of a team’s knowledge, but a diff alone may not explain why a change was made, what constraints shaped it, or who can answer questions later. Make the reasoning and context discoverable where teammates already work.
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- Pull request descriptions: Record the problem, intended behavior, important trade-offs, and any assumptions that are not obvious from the diff.
- Tests: Express important expected behavior in executable form so that future changes can reveal when those expectations break.
- Decision records: For consequential or hard-to-reverse choices, capture the options considered and the reason for the decision in the team’s normal decision-record format.
- Ownership information: Keep code ownership and escalation paths current so a teammate knows whom to involve when a change touches unfamiliar or sensitive areas.
- Review conversations: Resolve significant design questions in a durable place, or link the outcome from the pull request, instead of leaving the only explanation in an informal exchange.
These are practical engineering recommendations, not interventions shown by the studies above to cause better knowledge retention. They help make context available to reviewers and future maintainers without relying on one developer’s memory. The open-source study’s larger reported gains for core developers than for peripheral contributors, which the authors suggest may relate to deeper project familiarity, makes shared codebase understanding a relevant concern; it does not prove that any particular documentation or handoff method solves it.
How should a team introduce an assistant without confusing speed with success?
- Set the boundary. Decide which repositories and kinds of work may use the assistant, and apply existing rules for sensitive code and data handling. Clarify who owns the resulting changes.
- Define acceptance criteria. Agree what reviewers must verify for behavior, tests, design, security-sensitive logic, dependency changes, and local conventions. Make the criteria team-owned rather than dependent on a tool’s claims.
- Keep changes reviewable. Ask contributors to present a focused diff with enough context to understand intent. Reviewers need to be able to inspect the change, not just its description.
- Evaluate the whole workflow. Include drafting, testing, review, rework, and integration time in the assessment. The open-source study’s reported rise in integration time alongside productivity gains illustrates why code-generation speed alone is an incomplete measure.
- Check continuity as well as output. Look at whether another teammate can explain the change, find its rationale, and safely modify it later. Treat this as a local evaluation question, not an outcome established by the cited studies.
Which measures reveal whether the approach is working?
Choose a small set of measures that reflects your team’s risks and compare them over time against a meaningful baseline. Do not use output volume or coding speed as a stand-in for quality.
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- Quality: Track defects, test failures, escaped issues, and rework associated with changes.
- Flow: Track change lead time alongside review and integration time, so faster drafting does not conceal a growing merge or review bottleneck.
- Review outcomes: Observe what reviewers ask to change, whether changes meet team standards, and where security or design concerns arise.
- Continuity: Check whether teammates outside the original authoring pair can locate the rationale and explain or safely modify the code. Onboarding friction can also reveal missing context.
These are suggested local measures, not figures established by the cited studies. Interpret changes cautiously: tasks, contributors, project familiarity, and workflow can all affect the result. A tool comparison should also consider validation evidence, review burden, security controls and data handling, project-specific context, preservation of rationale and shared ownership, and fit with the team’s workflow.
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