Deep Learning for Software Defect Prediction: A Comprehensive Review

Deep learning has emerged as a promising approach for software defect prediction in recent years. The purpose of this review is to summarize the current state of research on deep learning for software defect prediction, including its significance and research status in both domestic and international contexts.

The significance of software defect prediction lies in its potential to improve the reliability and quality of software systems, and thereby reduce the risk of failures and errors that can be costly and time-consuming to fix. Deep learning offers a number of advantages for this task, including the ability to learn complex patterns and features from large amounts of data, and to automatically adapt to new and changing data without the need for manual tuning or parameterization.

To date, a number of studies have been conducted to explore the use of deep learning for software defect prediction, both in domestic and international contexts. These studies have investigated a range of different deep learning techniques and architectures, including convolutional neural networks, recurrent neural networks, and deep belief networks, and have evaluated their performance using various metrics such as precision, recall, and F1-score.

Overall, the results of these studies suggest that deep learning has the potential to improve the accuracy and effectiveness of software defect prediction, compared to traditional machine learning approaches. However, there are also challenges and limitations that need to be addressed, such as the need for large and diverse datasets, the risk of overfitting or underfitting, and the need for interpretability and explainability of the models.

In summary, deep learning has emerged as a promising approach for software defect prediction, with significant potential to improve the reliability and quality of software systems. However, further research is needed to address the challenges and limitations, and to develop more effective and interpretable models that can be applied in real-world settings.