Machine learning for software engineering: A tertiary study
Machine learning (ML) techniques increase the effectiveness of software engineering (SE)
lifecycle activities. We systematically collected, quality-assessed, summarized, and …
lifecycle activities. We systematically collected, quality-assessed, summarized, and …
Data quality matters: A case study on data label correctness for security bug report prediction
In the research of mining software repositories, we need to label a large amount of data to
construct a predictive model. The correctness of the labels will affect the performance of a …
construct a predictive model. The correctness of the labels will affect the performance of a …
Improving high-impact bug report prediction with combination of interactive machine learning and active learning
Context: Bug reports record issues found during software development and maintenance. A
high-impact bug report (HBR) describes an issue that can cause severe damage once …
high-impact bug report (HBR) describes an issue that can cause severe damage once …
Predicting the first response latency of maintainers and contributors in pull requests
SH Khatoonabadi, A Abdellatif… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The success of a Pull Request (PR) depends on the responsiveness of the maintainers and
the contributor during the review process. Being aware of the expected waiting times can …
the contributor during the review process. Being aware of the expected waiting times can …
On the way to sboms: Investigating design issues and solutions in practice
The increase of software supply chain threats has underscored the necessity for robust
security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a …
security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a …
An empirical study of rule-based and learning-based approaches for static application security testing
Background: Static Application Security Testing (SAST) tools purport to assist developers in
detecting security issues in source code. These tools typically use rule-based approaches to …
detecting security issues in source code. These tools typically use rule-based approaches to …
Developers' perception of GitHub Actions: A survey analysis
GitHub Actions is a powerful tool for automating workflows on GitHub repositories, with
thousands of Actions currently available on the GitHub Marketplace. So far, the research …
thousands of Actions currently available on the GitHub Marketplace. So far, the research …
Identifying and resolving conflict in mobile application features through contradictory feedback analysis
As mobile applications proliferate and user feedback becomes abundant, the task of
identifying and resolving conflicts among application features is crucial for delivering …
identifying and resolving conflicts among application features is crucial for delivering …
[HTML][HTML] VALIDATE: A deep dive into vulnerability prediction datasets
M Esposito, D Falessi - Information and Software Technology, 2024 - Elsevier
Context: Vulnerabilities are an essential issue today, as they cause economic damage to the
industry and endanger our daily life by threatening critical national security infrastructures …
industry and endanger our daily life by threatening critical national security infrastructures …
Robust learning of deep predictive models from noisy and imbalanced software engineering datasets
With the rapid development of Deep Learning, deep predictive models have been widely
applied to improve Software Engineering tasks, such as defect prediction and issue …
applied to improve Software Engineering tasks, such as defect prediction and issue …