چکیده مقاله
Software testing is a critical process aimed at identifying defects, ensuring compliance with existing requirements, and verifying compatibility with customer hardware environment To reduce costs and enhance accuracy, predicting software defects before the testing phase becomes essential Through predictive approaches, approximately 95% of defects can be anticipated before reaching the testing stage In this study, a hybrid approach combining Particle Swarm Optimization PSO and Genetic Algorithm GA is employed for feature selection in software defect prediction This is followed by a decision tree classifier designed to achieve comprehensive defect coverage The results demonstrate that the proposed method achieves an accuracy exceeding 90% and outperforms baseline methods and previous studies in terms of quality
کلیدواژهها
نویسندگان
Elaheh Nooraddini
Vocational Instructor, Department of Education, Chatrood, Kerman, Iran
شیوه ارجاع
Nooraddini, Elaheh,1403,Improving Software Testing Process Using Fault Prediction Based on the Combination of Genetic Algorithm and Particle Swarm Optimization,The 5th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
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مجموعه مقالات پنجمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات17 اسفند 1403 · مشهد