Automating Code Reviews with ChatGPT: An Experiment

Code reviews are a bottleneck as teams scale: pull requests pile up, reviewers get fatigued, and important issues slip through. I ran a focused experiment to see whether ChatGPT could help: automatically analyze PR diffs, surface clear actionable comments, and integrate into a CI pipeline without generating noise. The results surprised me — not as a replacement for human reviewers, but as a reliable assistant that can catch style, logic, and security issues early.

Experiment setup: how the automation worked

The pipeline was intentionally simple so results would be repeatable. I wired a GitHub Actions workflow to trigger on pull_request and push events. The action collects the unified diff for changed files, creates a trimmed context window (to stay within token limits), and sends that payload with a focused prompt to the OpenAI API (GPT-4). Responses are parsed and posted back as review comments on the PR. Tools used:

• GitHub Actions for orchestration
• OpenAI GPT-4 via API for natural-language review
• pre-commit and ESLint/flake8 as static analysis baselines
• SonarQube/CodeQL for security-focused scans

Sample prompt sent to the model (trimmed):

You are a senior software engineer. Review the following git diff. Provide:
1) A 2–3 sentence summary.
2) A ranked list of issues (bug, security, style, test coverage) with line references.
3) Suggested fixes with concise code where appropriate.
Only comment on changed lines and provide no generic praise.

What ChatGPT did well (real examples)

Across multiple repos (Python service, React frontend, and a Node.js CLI), ChatGPT reliably flagged:

• Obvious logic bugs (off-by-one errors, incorrect boolean logic).
• Security risks (unsanitized SQL string concatenation, missing validation on user inputs).
• Testing gaps (new public function with no unit tests or mocks).
• Maintainability suggestions (naming, decomposing long functions).

Example — Python PR diff (simplified):

def get_user(id):
    query = "SELECT * FROM users WHERE id = %s" % id
    return db.execute(query)

ChatGPT review: flagged SQL injection risk and suggested parameterized query:

Issue: SQL injection risk on line 2.
Suggested fix:
def get_user(id):
    query = "SELECT * FROM users WHERE id = %s"
    return db.execute(query, (id,))

That suggestion was correct for the DB API used in this repo and saved a manual follow-up. In other cases the model suggested TypeScript types or adding edge-case tests — high-ROI comments that engineers implemented quickly.

Limitations, hallucinations, and failure modes

ChatGPT is not perfect. I observed several classes of problems:

• Hallucinated file/line references when diffs were large or context trimmed too aggressively.
• Suggestions that look idiomatic but do not compile (missing imports, wrong API calls).
• Overly verbose or low-value comments for style nitpicks already covered by linters.
• Token limits: very large diffs required chunking, which can break cross-file reasoning.

Mitigations that worked in the experiment:

• Limit the model to a single file or function scope per request for accuracy.
• Run static analysis (mypy, ESLint, CodeQL) first and let the model focus on logic and design.
• Always surface model comments as “suggestions” and require human approval before merging.
• Track costs and rate limits; use smaller models for trivial formatting advice.

Integration patterns and practical recommendations

Based on the experiment, here are repeatable patterns for safely introducing AI-assisted reviews into your workflow:

• Human-in-the-loop: post model output as draft comments or checklist items rather than blocking merges.
• Scoped analysis: restrict the model to changed functions or a single file to reduce hallucinations.
• Combine with linters and security scanners: use ChatGPT for rationale, context-aware suggestions and for explaining complex warnings.
• Use templates and role prompts: tell the model the codebase conventions and desired tone (e.g., “be concise, cite line numbers, prioritize security”).
• Monitor metrics: PR turnaround time, number of human reviewer comments reduced, false-positive rate, and developer trust scores.

Sample lightweight GitHub Action step (conceptual):

- name: Run AI Code Review
  uses: actions/github-script@v6
  with:
    script: |
      const diff = getDiffFromEvent();
      const review = await callOpenAI(diff, prompt);
      postReviewComments(review);

For teams wanting a turnkey solution, consider vendors like PullRequest, Snyk (for security), or Sourcegraph Cody for context-aware code suggestions. For custom control, build via OpenAI/GPT APIs and run inside your CI with audit logs and rate limits.

Closing thought: AI can accelerate code review throughput and surface insightful fixes, but it amplifies the importance of prompt engineering, scope control, and human oversight. Will your team treat AI as a first reviewer, an assistant, or a silent checker — and how will that choice reshape reviewer responsibility and code quality metrics?