Impact-Centric Source Code Testing With Historical Execution Analysis
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Solution Overview
Problem
The increasing size and frequent changes in software applications lead to prolonged test execution times for unit and functional tests, slowing down the software development pipeline.
Innovation Solution
Implementing impact-centric source code testing based on historical execution analysis to selectively execute tests on modified and high-risk code, providing developers with feedback during coding and code reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive test execution is performed on all source code, then software reliability is improved, but test execution time increases
Solution Approach 1:
The patent segments the source code into modified and unmodified classes, and further segments tests into impact-centric tests and non-impact tests. By analyzing code changes and determining impacted classes, the system executes tests only on relevant segments rather than comprehensively on all code, thereby reducing test execution time while maintaining reliability for modified portions.
Solution Approach 2:
The patent applies local quality by assigning different test execution strategies to different code regions. Modified classes receive impact-centric test execution based on change analysis, while unmodified classes use historical execution data to determine test necessity. This localized approach optimizes test execution time for each code region based on its specific characteristics and risk level.
2Reliability
If comprehensive test execution is performed on all source code, then test coverage is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary analysis of code changes and historical execution data before test execution. By pre-determining impacted classes and identifying high-risk areas through static analysis and historical patterns, the system prepares a targeted test execution plan that maintains comprehensive coverage of critical areas while avoiding unnecessary tests, thus improving development pipeline efficiency.
Solution Approach 2:
The patent dynamically changes test execution parameters based on code modification patterns and historical data. Test selection, execution priority, and resource allocation are adjusted according to the specific characteristics of modified classes and their impact relationships, enabling comprehensive coverage of high-risk areas while reducing overall test execution burden to maintain productivity.
3Loss of time
If impact-centric test selection is implemented, then test execution time is reduced, but test reliability may be compromised
Solution Approach 1:
The patent implements feedback mechanisms by continuously analyzing test results, code change patterns, and historical execution data. This feedback loop refines the impact-centric test selection process, identifying high-risk areas more accurately and adjusting test execution strategies to maintain reliability. The system learns from past test outcomes to improve future test selection, ensuring that reduced execution time does not compromise reliability.
Solution Approach 2:
The patent performs preliminary risk assessment and impact analysis before test execution to identify critical areas that require thorough testing. By pre-determining high-risk classes and impact relationships through static analysis and historical data, the system ensures that essential reliability checks are performed on modified code while avoiding unnecessary tests on stable portions, thus maintaining reliability with reduced execution time.
Data Source
AI summary
Systems and methods for implementing impact-centric source code testing based on historical execution analysis. The method includes receiving a source code file including modified classes and unmodified classes. The method also includes storing class names corresponding to the modified classes in a database. The method further includes determining impacted classes in the source code file based on the class names corresponding to the modified classes. The method also includes, for each of the impacted classes in the source code file, identifying at least one test. The method further includes, for each of the impacted classes in the source code file, initiating the at least one test for the impacted class. The method also includes generating a data log including results generated by the at least one test for each of the impacted classes.