Cluster-Representative Software Testing for Faster Error Feedback
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Solution Overview
Problem
Software development processes face challenges in efficiently identifying and addressing errors introduced by continuous changes to source code, which can compromise code integrity and stability.
Innovation Solution
A method involving clustering tests into multiple clusters based on similarity metrics, designating a cluster-representative test for each cluster, and executing a culled test suite on modified source code to provide quick feedback on potential errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a full test suite is executed on every code change, then error detection coverage is maximized, but testing time and computational resources increase significantly
Solution Approach 1:
The patent segments the full test suite into multiple clusters of related tests. By grouping tests that are likely to be affected together, the system can execute only relevant clusters for each code change, reducing overall testing time while maintaining comprehensive error detection coverage through strategic selection of representative tests from each cluster.
Solution Approach 2:
The patent applies partial action by executing only a subset of tests (those most likely to detect errors) rather than the complete test suite. The system identifies and runs a minimal sufficient set of tests based on code change analysis, achieving effective error detection without the overhead of running all tests.
2Reliability
If more tests are included in the test suite, then error detection capability improves, but resource consumption and execution complexity increase
Solution Approach 1:
The patent segments the test suite into organized clusters, making the large set of tests more manageable and less complex to execute. This segmentation allows the system to selectively run only relevant clusters, reducing execution complexity while preserving error detection capability through intelligent test selection.
Solution Approach 2:
The patent changes the parameter of test selection from exhaustive to selective based on code change characteristics. By analyzing code changes and matching them against test clusters, the system dynamically determines which tests to run, transforming the execution process from a static full-suite approach to a dynamic, optimized subset approach.
3Productivity
If a reduced test suite is used for quick feedback, then testing speed improves, but error detection coverage may be insufficient
Solution Approach 1:
The patent implements partial action by running a reduced but strategically selected set of tests that are most likely to detect errors related to the current code change. This minimal sufficient set provides quick feedback while maintaining adequate error detection coverage for the specific changes made.
Solution Approach 2:
The patent incorporates feedback mechanisms where test results and code change patterns are analyzed to improve future test selections. The system learns from previous test executions and code changes, refining its ability to select the right subset of tests that provide both speed and coverage.
Data Source
AI summary
In some implementations, the techniques described herein relate to a method including: executing, by a processor on a collection of source code, a test suite comprising a plurality of tests; clustering, by the processor based on a result of executing the test suite, the plurality of tests into a plurality of test clusters; designating, by the processor for a particular test cluster within the plurality of test clusters, a cluster-representative test; and executing, by the processor on a subsequent collection of source code, a culled test suite, the culled test suite comprising a subset of the plurality of tests including the particular test cluster's cluster-representative test.