AI coding assistants still need code review
AI-generated code can speed up routine work, but plausible-looking code is not necessarily secure, maintainable or correct.
Topic diagram
Evaluate code assistance beyond speed
Key takeaways
- AI-generated code must meet the same standards as human-written code.
- Inspect security-sensitive logic and new dependencies carefully.
- Use tests and automated checks, but understand their limits.
- Measure defect rates and review effort as well as coding speed.
Why this question matters
Coding assistants can draft boilerplate, explain unfamiliar code and suggest tests. They can also introduce bugs, insecure patterns, unnecessary dependencies or code that passes a narrow example while failing in production. Teams should treat generated code like a contribution from a fast but fallible collaborator.
Review behavior, not authorship
The relevant question is whether the code meets the requirement, follows project conventions and handles edge cases. Reviewers should understand the change and remain accountable for merging it. A label saying code was generated by AI does not replace normal engineering standards.
Check security and dependencies
Inspect authentication, authorization, input validation, secrets handling, file operations and network calls carefully. Verify new packages, their maintainers and license terms. Never paste secrets or restricted source into tools that are not approved for that data.
Require tests that target failure
Ask for unit and integration tests, but inspect whether those tests actually exercise meaningful behavior. Add cases for empty values, malformed inputs, permission boundaries, timeouts and unexpected service responses. Passing tests only demonstrate the behavior covered by those tests.
Use tools as independent checks
Linters, type checkers, static analysis, dependency scanning and CI can catch classes of mistakes. They do not prove the change is safe, but they provide repeatable checks beyond a reviewer's intuition. For consequential changes, test in a controlled environment before deployment.
Keep the change small and explainable
Small diffs are easier to inspect and revert. Ask the assistant to explain assumptions and trade-offs, then verify them against the codebase. If a patch is too complex to understand, split it or rewrite it rather than merging it on trust.
Measure the actual benefit
Track cycle time, review effort, defects, rework and security findings. Faster code generation may move work from typing to review. A good adoption decision measures the whole delivery process, not the number of lines produced.
Common questions
Can AI-generated code be merged automatically?
Only within carefully bounded workflows where controls and tests match the risk. Security-sensitive and consequential changes need accountable review.
Do passing tests prove the code is correct?
No. Tests cover chosen cases. Review their assumptions and add independent cases for authorization, malformed input and failure paths.
Match review depth to the risk of the change
A documentation typo and a change to payment authorization should not receive the same review process. Use the risk of the affected system to determine required reviewers, tests and deployment controls. For authentication, permissions, cryptography, data deletion and financial logic, inspect assumptions and failure paths carefully. If the change affects a public API or stored data, check compatibility and migration behavior before release. AI assistance does not reduce the consequences of a defect.
Review the dependency tree as well as the code. A generated patch may introduce an unfamiliar library, pin an outdated version or use a package with unclear maintenance and licensing. Check whether the dependency is necessary, maintained and approved for the project. Use lockfiles and automated scans where appropriate, but review the findings rather than treating a clean scan as proof of safety.
Protect the codebase and the development process
Set rules for which repositories and data may be shared with a coding service. Confirm account settings, contractual protections and organisational approval before sending private source, secrets or customer information. Never place credentials in prompts or generated examples. Keep permissions narrow and make generated changes visible in normal version control so reviewers can inspect exactly what changed.
Measure the impact over multiple comparable tasks. Record completion time, review time, rework, defects and security findings, and note task complexity. A few fast demonstrations are not enough to establish productivity. If the assistant produces more code than the team can responsibly review, limit its use or change the workflow. The goal is a better maintained codebase and safer delivery, not maximum generated output.
Questions to ask before adopting a coding assistant
Which repositories and data may the tool access? Can the team disable access quickly? Are suggestions visible in the normal review process? What evidence shows a reduction in delivery time after review and rework are included? Who is responsible for checking dependencies, tests and security-sensitive changes?
Start with a bounded task and compare similar work with and without assistance. Record the time spent generating, reviewing, fixing and maintaining the code. If the tool makes patches larger or harder to understand, adjust its use. Adoption should follow measured engineering value, not the number of suggestions accepted.
Keep a record of what changed
For complex changes, ask the author to explain the requirement, the chosen approach and the tests that demonstrate expected behaviour. Keep the explanation with the pull request so a future maintainer can understand the decision without reconstructing the entire conversation with a tool. If a patch cannot be explained clearly, split it into smaller parts and review them separately. This also makes rollback more practical when a defect appears.
After release, compare the defect and rework rate with the baseline. A team should be willing to reduce or stop use for tasks where review costs outweigh the benefit. The right boundary may differ between a simple test fixture and code that controls identity, payments or customer data.
A practical review rubric for AI-assisted pull requests
Reviewers can use the same explicit rubric for AI-assisted and human-written changes. First, verify intent: does the patch solve the issue described, without unrelated edits? Second, verify behaviour: do tests cover normal cases, boundary conditions and plausible failures? Third, verify security: check permissions, input validation, secrets handling, dependency changes and any new network or file access. Fourth, verify maintainability: are names, abstractions and error paths understandable to someone who did not generate the patch?
Ask the contributor to identify what changed and why, but do not treat a confident explanation as proof. Compare the explanation with the diff, run the tests independently, and inspect generated files or dependencies rather than assuming they are safe. For high-impact changes, require an additional reviewer or specialist check according to the team's normal risk process.
Record defects found during review, follow-up fixes and the time spent reviewing. Over several releases, these observations help the team decide where an assistant is useful and where it creates extra work. This is more informative than counting accepted suggestions or measuring how quickly code first appears.
Founder analysis · AI coding assistants still need code review
What this means in practice
Measure a coding assistant by the quality of merged changes, not the amount of code it generates. Track accepted suggestions, defects caught before merge, regressions, review time, test quality, and security issues. Keep human review and automated checks in the loop; generated code can look plausible while introducing subtle behavior changes or dependency risks.
Sources and further reading
These references provide additional context for the frameworks discussed in this article. Check the source directly for its scope, date and limitations.
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