Code Change Analysis for Early Conflict Warning in Distributed Teams
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Solution Overview
Problem
In distributed software development environments, conflicts arise due to asynchronous code synchronization among multiple developers, leading to inefficiencies and difficulties in merging code versions.
Innovation Solution
A code analysis method using artificial intelligence to detect conflicts by training a random forest binary classification model, analyzing code changes, and warning developers about potential conflicts through a server-based system that identifies overlapping code blocks and their authors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If developers synchronize code frequently with trunk and branches, then code conflicts are reduced, but development efficiency and stability are improved
Solution Approach 1:
The system performs preliminary code analysis before code merging by training a random forest binary classification model to predict potential conflicts. This preliminary action identifies code blocks that may cause conflicts, allowing developers to address them before actual merging occurs, thus preventing conflicts rather than resolving them after they arise.
Solution Approach 2:
The system implements a feedback mechanism where the trained model predicts code conflicts based on collected code information, and this prediction feedback is provided to developers to guide their development decisions. The feedback loop continuously improves the model's accuracy in predicting conflicts, enabling better preventive actions.
2Productivity
If code changes are made without synchronization, then development speed is improved, but code conflict accumulation increases
Solution Approach 1:
The system performs preliminary analysis of code changes before they are committed by collecting code information and using the trained random forest model to predict potential conflicts. This preliminary action allows developers to understand the conflict risk of their changes before integrating them, enabling informed decisions about synchronization timing and conflict resolution strategies.
3Measurement precision
If AI-based conflict detection is implemented, then code conflict prediction capability is improved, but system complexity increases
Solution Approach 1:
The system introduces a random forest binary classification model as an intermediary between code changes and conflict detection. This intermediary model processes code information and provides conflict predictions, acting as a mediator that translates complex code analysis into actionable conflict risk assessments without requiring direct complex analysis of all code interactions.
Data Source
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AI summary
This application provides a code analysis method and a related device. The method includes: A server obtains identity information of a changed file and a location of a changed code block in the changed file, where the changed code block is located in a terminal of a distributed system, and the distributed system includes a plurality of terminals configured to provide code compilation. The server determines a base version of the changed file based on the identity information of the changed file. The server further determines, based on the location of the changed code block in the changed file, whether the base version of the changed file includes a target code block that overlaps the location of the changed code block, and warns at least one terminal in the distributed system if the base version includes the target code block. In the foregoing method, the warning may be executed in time when it is determined that a code block has been modified. In this way, during collaborative development, a developer may learn of a modification status of code in time, facilitating the developer to determine whether the modified code affects a program compiled by the developer.