Comparison of Machine Learning Models for Effective Software Fault Detection
Keywords:
Machine learning, Software Defect Prediction, Debugging, PROMISE datasetAbstract
Software defect prediction is a field in software engineering that aims to identify and anticipate defects or bugs in software systems before they occur. The goal is to develop techniques and models that can help software development teams prioritize their testing efforts and allocate resources more effectively. For this purpose, various Machine learning techniques used and these algorithms can utilize various features, such as code metrics, historical defect data, and developer information, to build predictive models. This paper aims to develop a model for software defect prediction using various ML algorithms. Experiments were conducted using the proposed model on KC2 dataset from the NASA PROMISE repository. The Decision tree algorithm achieved 73.28%, Naïve Bayes 83.97%, KNN 80.15%, Support vector Machine 82.44% and Random Forest 80.92% for KC2 dataset. The results demonstrated that the different model succeeded in effectively predicting the defects in PROMISE datasets KC2.
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