Progress on approaches to software defect prediction
Software defect prediction is one of the most popular research topics in software
engineering. It aims to predict defect‐prone software modules before defects are discovered …
engineering. It aims to predict defect‐prone software modules before defects are discovered …
Machine/deep learning for software engineering: A systematic literature review
Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …
Survey on software defect prediction techniques
Recent advancements in technology have emerged the requirements of hardware and
software applications. Along with this technical growth, software industries also have faced …
software applications. Along with this technical growth, software industries also have faced …
Bgnn4vd: Constructing bidirectional graph neural-network for vulnerability detection
Context: Previous studies have shown that existing deep learning-based approaches can
significantly improve the performance of vulnerability detection. They represent code in …
significantly improve the performance of vulnerability detection. They represent code in …
Deep semantic feature learning for software defect prediction
Software defect prediction, which predicts defective code regions, can assist developers in
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
finding bugs and prioritizing their testing efforts. Traditional defect prediction features often …
Heterogeneous defect prediction
Software defect prediction is one of the most active research areas in software engineering.
We can build a prediction model with defect data collected from a software project and …
We can build a prediction model with defect data collected from a software project and …
Deep learning based software defect prediction
Software systems have become larger and more complex than ever. Such characteristics
make it very challengeable to prevent software defects. Therefore, automatically predicting …
make it very challengeable to prevent software defects. Therefore, automatically predicting …
A comprehensive investigation of the role of imbalanced learning for software defect prediction
Q Song, Y Guo, M Shepperd - IEEE Transactions on Software …, 2018 - ieeexplore.ieee.org
Context: Software defect prediction (SDP) is an important challenge in the field of software
engineering, hence much research work has been conducted, most notably through the use …
engineering, hence much research work has been conducted, most notably through the use …
Deeplinedp: Towards a deep learning approach for line-level defect prediction
C Pornprasit… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …
Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning
H Tong, B Liu, S Wang - Information and Software Technology, 2018 - Elsevier
Context Software defect prediction (SDP) plays an important role in allocating testing
resources reasonably, reducing testing costs, and ensuring software quality. However …
resources reasonably, reducing testing costs, and ensuring software quality. However …