Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques

The rising deployment of software in automation and the cognitive skills of machines indicate a machine revolution in modern human civilization. Thus, diagnosing and predicting software faults is crucial to software reliability. In this paper, we first preprocessed four real datasets offered by National Aeronautics and Space Administration with twenty-one features using the Synthetic Minority Oversampling Technique and Label Encoding techniques. Subsequently, we experimented with thirteen software fault diagnosis Machine Learning (ML) models, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., Random Forest Regression, Linear Regression, Naïve Bayes, Decision Tree Classifier, Logistic Regression, KNeighbors Classifier, AdaBoost, Gradient Boosting Classifier, Gradient Boosting Regression, XGBR Regressor, XGBoost Classifier, Extra Trees Classifier and Support Vectors Machine after that, we compared each ML Model to select the best…