Deep Learning for Software Defect Prediction: A Comprehensive Review

Deep learning has become an increasingly popular method for software defect prediction due to its ability to learn complex patterns from large amounts of data. The purpose of this review is to provide an overview of the current state of research in deep learning-based software defect prediction, including its significance and potential benefits.

Software defect prediction is a critical task in software engineering that aims to identify potential defects in software systems before they occur. This can help improve software quality, reduce development costs, and enhance user satisfaction. Deep learning has emerged as a promising approach for software defect prediction, as it can automatically learn complex patterns and relationships in software data, including code, test cases, and bug reports.

Recent studies have shown that deep learning-based software defect prediction can achieve high accuracy and outperform traditional machine learning methods. Various deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs), have been applied to software defect prediction and achieved promising results. Moreover, researchers have explored different types of software data, such as source code features, execution traces, and software metrics, to train deep learning models for defect prediction.

However, there are still many challenges and limitations in deep learning-based software defect prediction, such as data quality, feature selection, and model interpretability. Therefore, further research is needed to address these issues and improve the effectiveness and practicality of deep learning-based software defect prediction.

Overall, deep learning-based software defect prediction has significant potential for improving software quality and reducing development costs. This review provides a comprehensive overview of the current state of research in this field and highlights the challenges and opportunities for future research.