Using Deep Learning and Class Imbalance Techniques to Predict Software Defects

A software application defect is a variance or diversion from the end user's needs or the original business requirements. A software defect is a coding error that results in inaccurate or unexpected outcomes from a software program. Prediction of defects/faults in software is that the process of identifying software modules is likely to have errors before they are tested. The phase of testing of any life cycle of a software is the most expensive and resource-intensive. Software defect prediction (SDP) can reduce testing expenses, which could ultimately result in the development of those software having the high quality at a more affordable price. This research study utilizes different class imbalance techniques like oversampling and undersampling and uses the three models Random Forest, CNN, and LSTM to determine which one will produce the best results. This study uses the dataset from the public PROMISE repository.