Just-In-Time Software Defect Prediction using a deep learning-based model

The increase in software complexity, driven by technological developments and user demands, has created major challenges for companies in Software Quality Assurance. Companies seek efficient ways to identify and mitigate defects, recognizing that they cause high financial costs and other problems with negative impacts on business. Among defect prediction approaches, Just-In-Time Software Defect Prediction has received increased attention from software industry professionals in recent years. This technique aims to identify and treat defects early, to improve the quality of the software development cycle. This study proposes a Deep Learning-based approach for Just-In-Time Software Defect Prediction using a large dataset of historical data from several popular software projects. The Deep Learning model was trained to identify defects by analyzing the software metrics provided by the dataset. The model achieved an accuracy of 82.08% in its predictions, and it was possible to determine the most relevant metrics for its conclusions through interpretability techniques. The results obtained demonstrate the potential of Just-In-Time Software Defect Prediction as a tool for improving software quality and encouraging the development of new studies and improvements in this area of research.