Software Test Reliability Index for False Failure Prediction
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Solution Overview
Problem
Unreliable software tests lead to false positives and negatives, hampering the speed and quality of software development by requiring manual investigation and additional testing to validate results.
Innovation Solution
A method to automatically characterize the reliability of software tests within an Integrated Development Environment (IDE) by applying a reliability index to test failures, using a software testing repository and modified tests to predict causes of inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software tests are executed to validate code, then test coverage and quality assurance are improved, but false positives and negatives occur leading to reduced development speed and increased manual investigation time
Solution Approach 1:
The system implements feedback by analyzing test failure data and providing reliability predictions back to developers. The reliability prediction module processes test results and feeds reliability scores back to help developers assess test validity, reducing manual investigation of false positives while maintaining test coverage.
Solution Approach 2:
The reliability prediction module acts as an intermediary between test execution and developer analysis. It introduces a computational layer that predicts test reliability based on historical data and test characteristics, filtering out obvious false positives before they reach developers and reducing unnecessary manual investigation.
2Measurement precision
If manual investigation is performed to validate test results, then false positives and negatives are identified, but time consumption and development efficiency deteriorate
Solution Approach 1:
The system performs preliminary action by predicting test reliability automatically before developers conduct manual investigation. The reliability prediction module pre-assesses test results using historical data and test characteristics, identifying likely false positives in advance so developers can prioritize their manual investigation efforts more effectively.
Solution Approach 2:
The system implements self-service by automatically analyzing test failures and predicting reliability without requiring developer intervention. The reliability prediction module autonomously processes test data, identifies patterns, and provides reliability assessments, reducing the burden of manual validation while maintaining measurement precision.
3Reliability
If additional testing is conducted to validate unreliable tests, then test accuracy is improved, but testing time and resource consumption increase
Solution Approach 1:
The system applies partial action by selectively validating only those tests with low predicted reliability scores. Rather than re-running all tests, the reliability prediction module identifies tests that最需要 validation based on their failure patterns and historical data, conducting additional testing only where necessary to maintain accuracy while minimizing time consumption.
Data Source
AI summary
Described are techniques for software test reliability characterization. The techniques include receiving a test failure for a software under development in an Integrated Development Environment (IDE). The techniques further include identifying, from a software testing repository communicatively coupled to the IDE, a similar failed software test. The techniques further include applying a reliability index of the similar failed software test to the test failure. The techniques further include displaying, on a graphical user interface communicatively coupled to the IDE, a graphical illustration indicating the reliability index associated with the test failure.