Defect detection in software development represents a vital activity that relies on efficient and precise techniques to assure the quality of the software. This manuscript tries to explore the possibilities of the Ada...
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ISBN:
(纸本)9783031821523;9783031821530
Defect detection in software development represents a vital activity that relies on efficient and precise techniques to assure the quality of the software. This manuscript tries to explore the possibilities of the Adaboost model optimized by a modified sinhcosh metaheuristics algorithm for accurate and efficient detection of defects. As modern development projects are dynamic and with tight deadlines, the capability of Adaboost classifier to adopt intricate patterns and metrics in the software code may help in identifying problematic modules, thus allowing focused testing in limited available time. As software development industry recently recognized the significance of the proactive testing, this manuscript suggests an approach where machine learning model was effectively integrated in a framework that helps in identifying error-prone modules. The suggested optimized Adaboost classifier has shown very promising performance with respect to the precision and accuracy of identifying faulty modules based on the software metrics, making it a potentially crucial tool that could be applied in the modern software development practice.
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