Pull Request Review Checklists Using Rule-Based Diff Analysis
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Solution Overview
Problem
Existing software repository checklists for pull requests are generic and often ignored by users, as they are not relevant to the specific changes made, leading to potential bugs and inefficiencies in code review.
Innovation Solution
A system that dynamically generates a checklist based on specific information associated with each pull request, using rule-based review to ensure each item is relevant to the changes, by comparing file identifiers and content with predefined rules, and allowing customization for different teams and projects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic checklists are used for all pull requests, then the review process is simple and quick, but the checklist items are not relevant to specific changes leading to potential bugs
Solution Approach 1:
The system performs preliminary analysis of the pull request content (comparing file identifiers and content changes) before generating the checklist. This allows the checklist to be customized based on the specific changes made, ensuring relevance and improving code review quality without requiring complex manual configuration.
Solution Approach 2:
The checklist generation system automatically analyzes the pull request itself to determine what checklist items are needed. The system serves itself by extracting information from the code changes and autonomously generating appropriate checklist items, eliminating the need for external manual intervention while maintaining high relevance.
2Reliability
If detailed rule-based review is implemented, then checklist items are highly relevant to changes, but the review process becomes more complex and time-consuming
Solution Approach 1:
The system extracts only the necessary information from the pull request (file identifiers and content changes) to generate checklist items. By taking out only the relevant data needed for checklist generation rather than analyzing the entire codebase, the system maintains high checklist relevance while minimizing the time investment required.
3Reliability
If manual review of each checklist item is required, then code quality is thoroughly checked, but user efficiency decreases and the process becomes cumbersome
Solution Approach 1:
The system provides feedback to users through the generated checklist, highlighting specific areas that need attention based on the actual changes made. This targeted feedback approach maintains thorough code quality checking while improving user convenience by showing only the relevant items that need review rather than a generic list of all possible issues.
Data Source
AI summary
In some implementations, a system may receive, from a software repository, a difference file indicating changes, to software code, that was submitted to the software repository. The system may parse the difference file to determine files, associated with the software code, affected by the changes and to determine content associated with the changes. The system may apply rules, from a rule dictionary, based on comparing identifiers, associated with the files, with identifiers included in the rules and based on comparing the content, associated with the changes, with content included in the rules. The system may generate software review checklist items based on applying the rules and output the software review checklist items for display. The system may receive, based on input from a user, confirmation of the changes and commit the changes to the software repository based on the confirmation.