June 2024

CodeBatAI

An AI code review GitHub bot application streamlining PRs reviews in great detail and providing each file change summary, a walkthrough of all changes, and the aspects of changes the code has been updated within the PR description itself. It also conducts code reviews and raises change requests in a thread. Developed to optimize workflow efficiencies and enhance codebase management practices, alongside features like Issue labeler.

Role

Developer

Team

me

Tech Stack

Node JS

Github Webhooks

Meta Llama-3

Challenges

One of the primary hurdles was designing effective prompts for the bot to ensure it generated accurate and structured data outputs. Additionally, integrating and extracting JSON responses from the AI model posed another significant challenge; I had to drop the idea of using JSONs due to complexity. Commenting on specific code patches to raise requests was particularly challenging, requiring extensive reading of documentation and implementation of complex logic. Creating a review thread for detailed feedback and suggestions also proved time-consuming and required meticulous planning. Formatting and presenting detailed file-by-file changes in a cohesive manner further complicated the process. This required intricate data manipulation and careful handling of AI-generated outputs to construct clear and informative summaries that developers could readily interpret.

Goals

Implement functionality for commenting on specific code files during review.

Develop a system to generate comprehensive tables summarizing changes.

Map and list commit messages in relation to the files they affect.

Construct a data map to facilitate accurate responses from the AI model.

Render AI-generated outputs in the review thread and summaries walkthrough in comment.

Enable commenting on specific lines within code changes.

Update the pull request description to reflect insights on all types of changes made.

Key Takeaways

Implement functionality for commenting on specific code files during review.

Develop a system to generate comprehensive tables summarizing changes.

Map and list commit messages in relation to the files they affect.

Construct a data map to facilitate accurate responses from the AI model.

Render AI-generated outputs in the review thread and summaries walkthrough in comment.

Enable commenting on specific lines within code changes.

Update the pull request description to reflect insights on all types of changes made.

Outcomes

Reduced manual effort and time spent on code review processes.

Streamlined pull request reviews with detailed summaries and file-by-file changes.

Improved codebase management practices through automated code reviews.

Increased efficiency in identifying and addressing code issues and improvements.

Strengthened the integration of AI technologies into software development workflows.

Live Website