CI Queue Prioritization via Code Review Quality
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Solution Overview
Problem
Conventional continuous integration (CI) systems treat all pull requests equally, leading to inefficient resource allocation and increased costs due to poor quality pull requests that can cause instability and delay development, especially as the number of tests required increases, potentially resulting in CI server timeouts.
Innovation Solution
A classifier determines the reliability of pull requests based on contributor and reviewer quality, as well as code change size, and prioritizes 'trusted' requests in a high priority integration queue while sending 'untrusted' requests to a low priority queue, allowing for more efficient resource allocation and reduced unnecessary testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all pull requests are processed equally in CI systems, then comprehensive testing coverage is maintained, but resource allocation becomes inefficient and costs increase due to poor quality pull requests causing instability and delays
Solution Approach 1:
The patent applies local quality by differentiating the processing level for different pull requests based on their assessed quality. High-quality pull requests receive standard comprehensive testing, while low-quality pull requests are subjected to reduced or skipped testing. This selective approach optimizes resource allocation by concentrating thorough testing efforts where they are most valuable, rather than applying uniform testing to all submissions regardless of quality
Solution Approach 2:
The patent segments the CI workflow into distinct quality-based streams. Pull requests are classified into different categories (e.g., high-quality, medium-quality, low-quality) and routed through different processing pipelines. This segmentation allows the system to handle different types of pull requests with appropriate testing intensity, improving overall efficiency while maintaining stability for critical high-quality submissions
2Manufacturing precision
If comprehensive testing is performed on all pull requests, then code quality is ensured, but development time increases and CI server timeouts may occur
Solution Approach 1:
The patent implements partial action by performing only the necessary level of testing on each pull request based on its quality assessment. High-quality pull requests receive full comprehensive testing to ensure code quality, while low-quality pull requests undergo partial testing or are fast-tracked with minimal testing. This selective approach maintains adequate quality control for reliable submissions while reducing unnecessary testing time for submissions that are likely to fail or require significant revisions
Solution Approach 2:
The system performs preliminary assessment of pull request quality before committing to comprehensive testing. By evaluating factors such as contributor history, code review feedback, and automated static analysis results in advance, the system can pre-classify pull requests and determine the appropriate testing intensity. This preliminary action prevents wasting time on comprehensive testing of low-quality submissions that would likely fail anyway
3Ease of operation
If poor quality pull requests are processed without prioritization, then all submissions receive equal attention, but they become bottlenecks that delay high quality changes
Solution Approach 1:
The patent introduces asymmetry in the CI processing system by treating different pull requests differently based on their quality characteristics. Instead of symmetric equal processing, the system applies asymmetric routing where high-quality pull requests are prioritized and fast-tracked through the integration pipeline, while low-quality pull requests are either deferred, subjected to additional review requirements, or tested with reduced intensity. This asymmetric approach ensures that valuable high-quality changes are not blocked by problematic low-quality submissions
Data Source
AI summary
A system and method for optimizing CI using code review quality includes a memory, a processor in communication with the memory, a classifier executing on the processor, a high priority integration queue, and a low priority integration queue. In an example, the classifier may receive pull requests for code changes and determine reliability factors. The classifier may also determine a reliability value of the pull requests based on the reliability factors and classify each of the pull requests into a classification, such as a trusted classification and an untrusted classification. Then, the classifier sends the pull requests in the trusted classification to the high priority integration queue and the pull requests in the untrusted classification to the low priority integration queue.