Ensemble Random Forests Classifier for Detecting Coincidentally Correct Test Cases
| Autor: | Akbar Siami Namin, Xiaozhen Xue, Shuvalaxmi Dass |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science media_common.quotation_subject 020207 software engineering Pattern recognition 02 engineering and technology Ensemble learning Random forest Support vector machine Test case Debugging 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Classifier (UML) media_common |
| Zdroj: | COMPSAC |
| DOI: | 10.1109/compsac48688.2020.00-72 |
| Popis: | The performance of coverage-based fault localization greatly depends on the quality of test cases being executed. These test cases execute some lines of the given program and determine whether the underlying tests are passed or failed. In particular, some test cases may be well-behaved (i.e., passed) while executing faulty statements. These test cases, also known as coincidentally correct test cases, may negatively influence the performance of the spectra-based fault localization and thus be less helpful as a tool for the purpose of automated debugging. In other words, the involvement of these coincidentally correct test cases may introduce noises to the fault localization computation and thus cause in divergence of effectively localizing the location of possible bugs in the given code. In this paper, we propose a hybrid approach of ensemble learning combined with a supervised learning algorithm namely, Random Forests (RF) for the purpose of correctly identifying test cases that are mislabeled to be the passing test cases. A cost-effective analysis of flipping the test status or trimming (i.e., eliminating from the computation) the coincidental correct test cases is also reported. |
| Databáze: | OpenAIRE |
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