A Systematic Literature Review of Software Defect Prediction Using Deep Learning

Abstract

The approaches associated with software defect prediction are used to reduce the time and cost of discovering software defects in source code and to improve the software quality in the organizations. There are two approaches to reveal the software defects in the source code. The first approach is concentrated on the traditional features such as lines of code, code complexity, etc. However, these features fail to extract the semantics of the source code. The second one is concentrated on revealing these semantics. This paper presents a Systematic Literature Review (SLR) of software defect prediction using deep learning models. This SLR is focused on identifying the studies that use the semantics of the source code for improving defect prediction. This SLR aims to analyze the used datasets, models and frameworks. Also, identifying the evaluation metrics to ensure their applicability in software defect prediction. IEEE Xplore, Scopus and Web of Science digital libraries were used to select the suitable primary studies. Forty (40) primary studies were selected that published by 15 December 2020 for analysis based on the quality criteria. The project levels that applied in the studies were: Within-project 52.5%, cross-project 17.5% and both within-project and cross-project 30%. The datasets used were: Promise dataset 68.18% and other datasets 31.82%. The most used deep learning model in the primary studies was: Convolutional Neural Network (CNN) by 35%. The most used evaluation metrics were: F-measure and Area Under the Curve (AUC). Software defect prediction using deep learning models is still a valuable topic and requires much research studies to enhance the performance of the defect prediction.