Use AI coding assistants to help produce and examine changes, not to take responsibility for accepting them. Code quality and shared understanding depend on the engineering system around the assistant: team standards, appropriate tests and review, security checks, and changes whose rationale other people can understand. DORA’s 2025 report puts it plainly: “AI’s primary role is as an amplifier, magnifying an organization’s existing strengths and weaknesses.”
Does AI coding improve code quality?
There is no single result that establishes how AI assistance affects code quality across teams. Findings differ by task and setting, and the most concrete studies here measure bounded coding exercises or analyzed open-source projects—not a universal production outcome.
GitHub reported better results in a controlled coding task, while a 2024 preprint analyzing open-source projects found no change in measured code quality despite productivity gains. Those findings can coexist: they concern different settings, methods, and outcomes. Treat them as reasons to evaluate your own workflow, not as a forecast that an assistant will improve every codebase.
What the studies measured
| Study and setting | Reported result | How to interpret it |
|---|---|---|
| GitHub, 2025: randomized controlled task with 202 valid participants, each with at least five years of Python experience. Participants built API endpoints for a fictional restaurant-review web server; unit tests and blinded developer reviews assessed the results. | Participants with Copilot access were reported to be 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. | These are GitHub’s reported findings for one task and experienced Python developers. They do not establish equivalent results in other languages, codebases, or production work. |
| Song, Agarwal, and Wen, 2024 preprint: analysis of GitHub open-source repository data using a generalized synthetic control method. | The paper reports 6.5% higher project-level productivity, 5.5% higher individual productivity, 5.4% more participation, and 41.6% higher integration time, with no change in measured code quality. | These results concern the analyzed open-source projects. The paper reports larger gains for core developers than peripheral contributors and suggests deeper project familiarity as one possible explanation; it does not establish that familiarity itself caused the difference. |
The comparison matters for management: faster contribution or more code is not, by itself, evidence of better maintainability, less rework, or lower review effort.
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What should remain a human responsibility?
Make the team, not the assistant, accountable for whether a change is safe and appropriate to merge. A fluent explanation or working demo is not evidence that a change fits the design, handles failure cases, or meets security expectations. Define acceptance standards in the same way you would for code written without an assistant.
Set review expectations before adoption
Agree on what reviewers must verify for the kinds of changes your team makes. At minimum, review behavior, test evidence, security-sensitive logic, dependency changes, and fit with local conventions. Scale scrutiny to impact: a small documentation edit and an authentication change do not carry the same risk.
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Use team-owned standards rather than treating a model’s explanation as proof. Reviewers should be able to trace what changed, why it is needed, what evidence supports it, and what could go wrong.
Match evidence to the change
- Behavior: Use tests that exercise expected behavior and relevant edge cases. Passing tests are evidence about the behaviors they cover, not a guarantee that the change is correct in every situation.
- Design and maintainability: Have a reviewer assess whether the change fits existing abstractions and conventions, is understandable, and avoids unnecessary complexity.
- Security: Apply security review and checks appropriate to the threat surface, especially for authentication, authorization, data handling, input validation, and dependency changes. Code that runs is not thereby secure.
How should teams handle security and code review?
A 2024 qualitative study published at CCS combined 27 interviews with analysis of Reddit discussions. It describes professionals using coding and general-purpose AI assistants for security-related work, including code generation, threat modeling, review, and vulnerability detection. Participants expressed mistrust and described checking suggestions. The authors also observed a mismatch between reported scrutiny and security outcomes in comparisons, and noted that functionality may be used as a proxy for security.
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This study does not establish how common these practices are across all developers. It does support a practical boundary: an assistant may help with security work, but its output does not replace independent verification. Reviewers should assess security properties directly rather than infer them from a feature working or from a confident explanation.
How can teams keep knowledge from being lost?
Knowledge continuity means more than retaining code that compiles. Teammates need enough context to maintain a change, understand its constraints, and make a safe follow-up modification—even if the original author or assistant is unavailable.
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Keep that context in ordinary, reviewable engineering artifacts. For consequential changes, ask contributors to record the problem being solved, important trade-offs, assumptions, and why the chosen approach fits. Make tests express expected behavior where practical, and keep decision records and ownership information current when a change affects shared architecture or responsibilities.
These are practical engineering recommendations, not interventions proven by the studies described here. The evidence does point to project familiarity as relevant: the 2024 open-source preprint reports larger gains for core developers and offers deeper familiarity as a possible explanation. It does not test whether documentation, pairing, ownership rules, or onboarding practices preserve knowledge. Teams should adopt such practices because they make context easier to inspect and share, then assess locally whether they help.
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How should you evaluate an AI-assisted workflow?
Evaluate the complete path from suggestion to accepted change. The open-source preprint’s reported increase in integration time is a reminder that drafting faster may move effort into review or integration rather than eliminate it. Track locally chosen measures as signals, not as proof that any one tool caused a result.
Compare the workflow on six dimensions
- Quality: Do changes meet team standards, and what do tests, review findings, defects, and rework show?
- Integration and review burden: How much reviewer and maintainer effort does a change require, and where does work accumulate?
- Security and data handling: Are security checks suited to the change, and does the assistant’s use fit the team’s data-handling requirements?
- Project-specific context: Can the workflow account for local architecture and conventions, and can the contributor verify that context?
- Rationale and shared ownership: Can another teammate understand the decisions and take responsibility for maintaining the result?
- Workflow fit: Does assistance fit existing testing, review, and release practices, or create an unmeasured side path?
Choose measures that expose trade-offs
Alongside delivery measures, consider tracking defects, rework, review outcomes, change lead time, onboarding friction, and whether another teammate can explain or safely modify a sample of assisted changes. These are suggested local measures, not outcomes established by the cited studies. Interpret them together: output volume or speed alone cannot tell you whether quality and continuity have been protected.
What organizational role does AI play?
DORA’s 2025 report argues that the greatest returns come from strategic focus on underlying organizational capabilities rather than tools in isolation. Its companion capability model describes seven capabilities and provides implementation strategies, team tactics, and ways to monitor progress. This is practitioner guidance, not proof that a single practice independently causes better code quality or knowledge retention.
DORA’s 2024 report says it heard from more than 39,000 professionals at organizations of varied sizes and across industries globally. That is the report’s stated respondent reach; it should not be mistaken for the sample size behind every finding or for a direct measurement of AI’s causal effect.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The practical implication is to treat an assistant as one part of a delivery system. Establish clear standards, make review and validation routine, keep the reasoning behind important changes legible, and inspect the full workflow for both benefits and new burdens.
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