Publication

Abstract : During software development and maintenance, predicting software bugs becomes essential. An essential activity of the quality assurance process, defect prediction at the beginning of the software development life cycle has received extensive research over the past two decades. Early detection of defective modules in software development can assist the development team in making efficient and effective use of the resources at hand to produce high-quality software in a short amount of time. Using a machine learning approach, which finds hidden patterns in software attributes, it is possible to recognize the problematic modules. In the NASA data set JM1, the suggested work is contrasted with various machine learning classification procedures. The limit of random forest speculation is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since ensemble learning of random forest requires a ton of decision trees to acquire elite execution, the situation is not appropriate for carrying out the design on the limited-scale equipment like an embedded system. In this paper, we propose a boosted random forest, experimental outcomes show that the proposed technique, which comprises of less decision trees, has higher speculation capacity contrasting with the traditional technique. The experimental findings demonstrated that our proposed boosted random forest model results in greater levels of defect prediction accuracy, improving software quality.