Test Result Stability Scoring for Integration Testing
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Solution Overview
Problem
Automated testing environments often produce inconsistent results, making it difficult to identify code errors and reducing the effectiveness of regular testing, as unstable test results can erroneously report failures due to variations in test environments and over time.
Innovation Solution
A method to determine a test result stability score by evaluating the consistency of test results, which involves running the test protocol multiple times to refine the score, and re-running tests on previous code versions to account for time-variant changes, allowing for more accurate assessment of new code versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If tests are run multiple times to improve result consistency, then test reliability is improved, but testing time and resources are increased
Solution Approach 1:
The system performs preliminary actions by running tests multiple times beforehand to establish a stability score before making acceptance decisions. This preliminary testing phase allows the system to predict test outcome stability and avoid unnecessary re-testing, thereby resolving the contradiction between improving reliability through repeated testing and minimizing time loss.
Solution Approach 2:
The system implements feedback mechanisms by analyzing test result patterns and generating stability scores that inform future testing decisions. The feedback loop allows the system to learn from previous test outcomes and adjust testing frequency, ensuring reliable results while optimizing testing time and resources.
2Measurement precision
If tests are run frequently to detect errors early, then error detection capability is improved, but resource consumption is increased
Solution Approach 1:
The system changes the parameter of testing frequency dynamically based on calculated stability scores. When stability is high, testing frequency is reduced; when stability is low, testing frequency is increased. This adaptive parameter adjustment allows the system to maintain high error detection capability while optimizing resource consumption.
Solution Approach 2:
The system applies partial testing action by running tests a sufficient number of times to establish stability rather than continuously. This partial action approach provides adequate error detection capability without the excessive resource consumption of constant re-testing.
3Productivity
If unstable test results are accepted to speed up the process, then testing speed is improved, but false negatives increase
Solution Approach 1:
The system replaces the mechanical approach of always running tests with a computational approach using stability scores and predictive analysis. This substitution allows the system to quickly determine whether full testing is necessary or if results can be trusted based on historical stability data, thereby maintaining high productivity while minimizing false negatives.
Solution Approach 2:
The system performs preliminary analysis of test stability before making acceptance decisions. By evaluating historical test results and calculating stability scores in advance, the system can quickly determine whether to accept results without full re-testing, thus maintaining high testing speed while ensuring reliability through the preliminary stability assessment.
Data Source
AI summary
Software code is tested to determine relative performance of the software code. Successfully executing a test may be used to flag problems in a code repository to be corrected or criteria for accepting code for acceptance to the repository. To further evaluate test results, the test may be run several times on the same code version to determine a test result stability score describing how the test results may vary in different executions, such as a frequency that the code passes the test. Based on the score, additional tests may be run to further refine test result stability score. To evaluate time-variance of testing, when a code version fails a test, a previous version of the code may be run with the same test and the results compared to determine if a new regression was introduced.