Predicting Software Defects Using Bayesian Approaches
Keywords:
defect anticipation in software, Bayesian Networks, categorization, machine learningAbstract
In the realm of software engineering, the anticipation of software flaws holds significant importance as it enables developers to pinpoint and rectify issues before they escalate into expensive and challenging bugs. Timely identification of software defects not only economizes time and resources in the software development lifecycle but also assures the ultimate quality of the end product. This study seeks to assess three algorithms for constructing Bayesian Networks, aiming to classify projects as susceptible to defects. While Naive Bayes is the prevailing method in literature, this research introduces K2, Hill Climbing, and TAN as alternatives for constructing Bayesian Networks. Meanwhile, three publicly available PROMISE datasets are employed, incorporating McCabe and Halstead complexity metrics. The obtained results are benchmarked against widely used approaches like Decision Tree and Random Forest. Performance metrics applied in a cross-validation process reveal that the classification outcomes are on par with Decision Tree and Random Forest. Notably, Bayesian algorithms exhibit lower variability, enhancing the robustness of software engineering predictions. This advantage is evident in the consistent results of training and test data selection, distinguishing them from the variable outcomes observed in Decision Tree and Random Forest approaches.
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