How to Use Cody by Sourcegraph for Code Review Automation
Accelerate pull request reviews using Cody by Sourcegraph — catch bugs, suggest improvements, enforce coding standards, and reduce review bottlenecks. Covers integration with GitHub/GitLab workflows and handling false positives.
Implementation Steps
- 1
Configure review rules and standards
Define: coding conventions, security patterns to flag, performance anti-patterns, and team-specific guidelines.
- 2
Integrate with PR workflow
Set up GitHub Action or GitLab CI to trigger AI review on every PR. Configure to run before human reviewers.
- 3
Triage AI suggestions
Categorize findings: critical (security, bugs), important (performance, readability), style (formatting, naming).
- 4
Calibrate sensitivity
Review false positive rate weekly. Adjust rules to reduce noise — target <10% false positive rate.
- 5
Track review metrics
Measure: review cycle time, bugs caught before merge, developer satisfaction with AI suggestions.
Expected Metrics
Ehsan's Recommendation
AI code review does not replace human reviewers — it makes them faster. Cody by Sourcegraph catches the mechanical stuff (null checks, error handling, naming conventions) so humans focus on architecture and logic. One engineering team reduced review cycle time from 48 hours to 6 hours. The key: configure it to be helpful, not noisy. A tool that flags 50 style nits per PR gets disabled within a week.
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council