Context-Aware Code Review Copilot
Proof of Concept for Knowledge-Driven Delivery Intelligence The Pull Request Checklist
Inspiration
Code reviews slow teams down not because of bad code, but because context is fragmented across Jira tickets, commits, incidents, and documentation. Reviewers spend more time understanding impact than reviewing logic.
Industry discussions and engineering blogs consistently highlight that missing context during reviews leads to slower delivery and higher chances of regressions.
What it does
Context-Aware Code Review Copilot automatically brings together all relevant context for every pull request:
- Summarizes change intent from linked Jira tickets
- Highlights high-risk areas based on historical production data
- Surfaces test coverage gaps to prevent regressions
- Evaluates each change against the historical health of the system
All insights appear directly where reviews already happen, without introducing new workflows.
How we built it
We built Context-Aware Code Review Copilot as an Atlassian-native proof of concept using TypeScript for orchestration and UI, and C++ for a high-performance risk and impact analysis engine.
Atlassian Integrations
Bitbucket is the entry point. We listen to pull request webhooks to capture code diffs, touched files, commit history, and reviewers. This triggers the copilot for every PR.
Jira provides change intent and business context. We fetch linked tickets to understand why the change exists, its priority, and the type of work (feature, bug, or hotfix).
Jira Service Management supplies real production signals. We analyze past incidents, severity levels, and rollback history related to the impacted services to understand historical system stability.
Compass maps code changes to services and owners. This allows us to identify critical systems, previously stable components, and responsible teams for accurate impact analysis.
C++ Risk Engine processes all these signals efficiently. The engine is designed for high-performance analysis but uses simulated data for this proof of concept. It evaluates incident history, change patterns, and system health to generate risk assessments tailored to business needs.
Rovo generates clear, human-readable explanations—summarizing intent, explaining risk reasoning, and producing a tailored review checklist.
All insights are surfaced directly inside the Bitbucket pull request UI, keeping developers in their existing workflow.
Challenges we ran into
Defining the right scope for a proof of concept
Balancing between building enough to validate the intelligence layer without overbuilding production infrastructure required careful prioritization.
Accomplishments that we're proud of
- Established a strong system-level foundation focused on clarity and extensibility
- Built an explainable intelligence system that provides reasoning, not just scores
What we learned
Treating system health as a living signal—continuously learned from production history—leads to better review decisions and safer releases. Context isn't just about what changed, but why it changed and what broke before in similar situations.
The value lies not in complex algorithms, but in bringing the right context to the right people at the right time.
What's next for Context-Aware Code Review Copilot
- Evolve the proof of concept into a production-ready Atlassian Marketplace app
Built With
typescript c++ bitbucket jira compass rovo atlassian-forge
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