Abstract
Software Defect Prediction (SDP), even in its early stages, is a crucial and significant activity. SDP has recently received a lot of attention as a quality assurance method. Massive amounts of reports and defect data may be generated by the component services. Although much emphasis has been placed on developing defect prediction models using machine learning (ML), some work has been done to determine how effective source code is. ML is a supervised algorithm that is used to produce better results. To appreciate defect prediction in SOS better, this paper suggests a fault diagnosis framework based on the web access to care.ML tools such as Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) are used to evaluate the model’s utility, and certain metrics are also constructed using feature extraction techniques. Finally, the performances of the ML algorithms are compared and the better one is analyzed.
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