Deep learning based vulnerability detection: Are we there yet?
Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …
security. Existing program analysis techniques either suffer from high false positives or false …
Software visualization and deep transfer learning for effective software defect prediction
Software defect prediction aims to automatically locate defective code modules to better
focus testing resources and human effort. Typically, software defect prediction pipelines are …
focus testing resources and human effort. Typically, software defect prediction pipelines are …
Deep learning approach for software maintainability metrics prediction
Software maintainability predicts changes or failures that may occur in software after it has
been deployed. Since it deals with the degree to which an application may be understood …
been deployed. Since it deals with the degree to which an application may be understood …
Detecting privacy requirements from User Stories with NLP transfer learning models
Context: To provide privacy-aware software systems, it is crucial to consider privacy from the
very beginning of the development. However, developers do not have the expertise and the …
very beginning of the development. However, developers do not have the expertise and the …
Revisiting supervised and unsupervised methods for effort-aware cross-project defect prediction
Cross-project defect prediction (CPDP), aiming to apply defect prediction models built on
source projects to a target project, has been an active research topic. A variety of supervised …
source projects to a target project, has been an active research topic. A variety of supervised …
[HTML][HTML] A three-stage transfer learning framework for multi-source cross-project software defect prediction
J Bai, J Jia, LF Capretz - Information and Software Technology, 2022 - Elsevier
Context Transfer learning techniques have been proved to be effective in the field of Cross-
project defect prediction (CPDP). However, some questions still remain. First, the conditional …
project defect prediction (CPDP). However, some questions still remain. First, the conditional …
How far have we progressed in identifying self-admitted technical debts? A comprehensive empirical study
Background. Self-admitted technical debt (SATD) is a special kind of technical debt that is
intentionally introduced and remarked by code comments. Those technical debts reduce the …
intentionally introduced and remarked by code comments. Those technical debts reduce the …
DeepCPDP: Deep learning based cross-project defect prediction
Cross-project defect prediction (CPDP) is an active research topic in the domain of software
defect prediction, since CPDP can be applied to the following scenarios: the target project …
defect prediction, since CPDP can be applied to the following scenarios: the target project …
Predicting the emergence of community smells using socio-technical metrics: A machine-learning approach
Community smells represent sub-optimal conditions appearing within software development
communities (eg, non-communicating sub-teams, deviant contributors, etc.) that may lead to …
communities (eg, non-communicating sub-teams, deviant contributors, etc.) that may lead to …
Kernel spectral embedding transfer ensemble for heterogeneous defect prediction
H Tong, B Liu, S Wang - IEEE Transactions on Software …, 2019 - ieeexplore.ieee.org
Cross-project defect prediction (CPDP) refers to predicting defects in the target project
lacking of defect data by using prediction models trained on the historical defect data of …
lacking of defect data by using prediction models trained on the historical defect data of …