Trimming test suites with coincidentally correct test cases for enhancing fault localizations

Although empirical studies have demonstrated the usefulness of statistical fault localizations based on code coverage, the effectiveness of these techniques may be deteriorated due to the presence of some undesired circumstances such as the existence of coincidental correctness where one or more passing test cases exercise a faulty statement and thus causing some confusion to decide whether the underlying exercised statement is faulty or not. Fault localizations based on coverage can be improved if all possible instances of coincidental correctness are identified and proper strategies are employed to deal with these troublesome test cases. We introduce a technique to effectively identify coincidentally correct test cases. The proposed technique combines support vector machines and ensemble learning to detect mislabeled test cases, i.e. Coincidentally correct test cases. The ensemble-based support vector machine then can be used to trim a test suite or flip the test status of the coincidental correctness test cases and thus improving the effectiveness of fault localizations.