Enhancing Software Quality Assessment: Classifier-Based Reduction of Mutation Test Generation
محورهای موضوعی : journal of Artificial Intelligence in Electrical Engineeringbahman arasteh 1 , Zeinab Asghari 2 , abbas koochari 3
1 - Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
2 - گروه کامپیوتر، دانشگاه آزاد اسلامی، واحد ارس، هادیشهر، آذربایجان شرقی
3 - Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
کلید واژه: software mutation testing, instruction classification, error propagation, machine learning,
چکیده مقاله :
Mutation testing could be a capable procedure to assess the quality of test suites. The method of creating mutation testing includes creating a huge number of test cases, which can be computationally costly and time-consuming. This consider proposes a classifier-based approach to diminish the number of created mutation tests that includes preparing a classifier on a set of instruction highlights to decide which ones are error-prone. The classifier is prepared on a dataset of instruction characteristics for identifying the foremost compelling informational for infusing mutants Mutation score is calculated to decide the foremost viable enlightening. The ponder assesses the adequacy of the approach through tests on a few open-source ventures. The comes about appear that the approach is able to decrease the number of produced mutants whereas keeping up high mutation score. This approach has the potential to essentially diminish the computational burden of change testing and make strides the effectiveness of program testing.
Mutation testing could be a capable procedure to assess the quality of test suites. The method of creating mutation testing includes creating a huge number of test cases, which can be computationally costly and time-consuming. This consider proposes a classifier-based approach to diminish the number of created mutation tests that includes preparing a classifier on a set of instruction highlights to decide which ones are error-prone. The classifier is prepared on a dataset of instruction characteristics for identifying the foremost compelling informational for infusing mutants Mutation score is calculated to decide the foremost viable enlightening. The ponder assesses the adequacy of the approach through tests on a few open-source ventures. The comes about appear that the approach is able to decrease the number of produced mutants whereas keeping up high mutation score. This approach has the potential to essentially diminish the computational burden of change testing and make strides the effectiveness of program testing.
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