Code Commit Defect Prediction for Targeted Software Testing
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Solution Overview
Problem
Existing software testing methods are often non-exhaustive, leading to undetected defects in computer programs, and developers tend to skip additional testing unless explicitly required, complicating the decision to release new versions.
Innovation Solution
A defect prediction system that analyzes historical defect occurrence patterns in a code base to provide an objective measure of defect likelihood, generating predictions and recommendations for corrective actions, and prioritizing code review and testing based on risk metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test suites are used to validate software, then defects can be identified, but the testing is non-exhaustive and developers bypass writing additional tests
Solution Approach 1:
The defect prediction system performs preliminary analysis of code commits before software release by examining historical defect patterns, code changes, and developer behavior to identify files at high risk of containing defects, enabling proactive quality assurance rather than reactive testing
Solution Approach 2:
The system replaces manual judgment about testing completeness with an automated machine learning model that objectively predicts defect likelihood based on multiple features including historical defect data, code metrics, and commit patterns, eliminating developer bias and inconsistency
2Reliability
If developers are explicitly required to write additional tests, then defect detection improves, but the process becomes more complex and time-consuming
Solution Approach 1:
The system segments the software codebase into individual files and analyzes each file independently for defect risk, allowing targeted testing of only high-risk files rather than requiring comprehensive testing of entire codebase or manual review of all code changes
Solution Approach 2:
The defect prediction system acts as an intermediary between code commits and software release, providing objective risk assessments that guide testing priorities and resource allocation, reducing the need for complex manual testing processes while maintaining high defect detection rates
3Reliability
If comprehensive testing is performed to ensure all functions work as expected, then software reliability improves, but release timing becomes delayed and productivity decreases
Solution Approach 1:
The system performs preliminary defect risk assessment on code commits before they reach the testing phase, identifying high-risk files that require focused testing attention, thereby reducing the scope and time of comprehensive testing while maintaining software reliability
Solution Approach 2:
The system dynamically adjusts testing priorities and resource allocation based on predicted defect risk scores for different files, concentrating testing efforts on high-risk areas and reducing or eliminating testing on low-risk files, optimizing the balance between reliability and release timing
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for predicting a defect within a computer program comprising: accessing a code base of the computer program, the code base of the computer program comprising a plurality of computer program files; training the defect prediction system, the training including performing a historical analysis of defect occurrence patterns in the code base of the computer program; analyzing a commit of the computer program to identify a likelihood of defect occurrence within each of the plurality of files of the computer program; and, calculating a defect prediction metric for each of the plurality of files of the computer program, the defect prediction metric providing an objective measure of defect prediction for each of the plurality of files of the computer program.