Deep Learning and Explainable AI for Accurate and Interpretable Software Defect Prediction

Abstract

Software defect prediction is an essential aspect of software development process as defects significantly impact software reliability and usability. Several methods have been studied to predict the defects in software, but most of the time, the prediction result lack of transparancy about which features actually contributes to the prediction results. In this study, we adopt the Convolutional Neural Network (CNN) model for defect prediction, then apply the Local Interpretable Model-Agnostic Expl

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