Automated Cherry-Picking for Back-Porting Commits
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Solution Overview
Problem
Manual cherry-picking and back-porting of commits in software development is time-consuming, error-prone, and often results in merge conflicts, delaying software releases and requiring additional resources.
Innovation Solution
A system and method that proactively build a history list of commits, classify intermediary commits as intersecting or empty, and automatically determine which commits to back-port with the target commit, optimizing the process to reduce unnecessary commits and minimize conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual cherry-picking is used to back-port commits, then developers can selectively apply commits, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs self-service by automatically analyzing commit histories, classifying intermediary commits as intersecting or empty, and generating finalized history lists without requiring manual developer intervention. The automated cherry-picking process eliminates manual guesswork and reduces errors while maintaining selective commit application.
Solution Approach 2:
The system performs preliminary actions by building preliminary history lists, classifying intermediary commits, and generating candidate history lists before finalizing the back-port process. This proactive analysis determines which commits need to be backported in advance, eliminating the need for manual review during the actual back-porting operation.
2Reliability
If all intermediary commits are backported, then the target commit can be applied, but unnecessary commits increase complexity and potential conflicts
Solution Approach 1:
The system extracts only the necessary intermediary commits by classifying them as either intersecting or empty. Empty commits that do not affect the target commit are removed from the history list, while only intersecting commits are retained. This extraction eliminates unnecessary complexity and reduces potential merge conflicts while maintaining reliability.
Solution Approach 2:
The system applies local quality by differentiating between intersecting and empty intermediary commits based on their specific characteristics. Intersecting commits that modify files relevant to the target commit are retained, while empty commits are discarded. This selective retention optimizes the back-port process by including only necessary commits.
3Productivity
If automated cherry-picking is implemented, then speed and efficiency improve, but system complexity increases
Solution Approach 1:
The system segments the complex back-porting process into distinct manageable stages: building preliminary history lists, classifying intermediary commits as intersecting or empty, creating candidate history lists, and generating finalized history lists. This segmentation simplifies the automated system by breaking down the complex task into clear, sequential operations with well-defined criteria.
Data Source
AI summary
A system includes a memory and a processor in communication with the memory. The processor builds a preliminary history list of a plurality of commits including intermediary commits and a target commit, instantiates a target file list of files modified by the target commit, and instantiates an intermediary file list for each intermediary commit. Additionally, the processor classifies each intermediary commit as an intersecting commit or an empty commit. Responsive to classifying a respective intermediary commit as an empty commit, the processor removes the respective intermediary commit from the preliminary history list to create a candidate history list. Responsive to classifying a respective intermediary commit as an intersecting commit, the processor retains the respective intermediary commit in the candidate history list. Additionally, the processor joins the respective intermediary file list with the target file list and creates a finalized history list.