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 …
Predicting the precise number of software defects: Are we there yet?
Abstract Context: Defect Number Prediction (DNP) models can offer more benefits than
classification-based defect prediction. Recently, many researchers proposed to employ …
classification-based defect prediction. Recently, many researchers proposed to employ …
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 …
Software defect prediction based on gated hierarchical LSTMs
H Wang, W Zhuang, X Zhang - IEEE Transactions on Reliability, 2021 - ieeexplore.ieee.org
Software defect prediction, aimed at assisting software practitioners in allocating test
resources more efficiently, predicts the potential defective modules in software products …
resources more efficiently, predicts the potential defective modules in software products …
Software defect prediction via attention‐based recurrent neural network
G Fan, X Diao, H Yu, K Yang, L Chen - Scientific Programming, 2019 - Wiley Online Library
In order to improve software reliability, software defect prediction is applied to the process of
software maintenance to identify potential bugs. Traditional methods of software defect …
software maintenance to identify potential bugs. Traditional methods of software defect …
The best of both worlds: integrating semantic features with expert features for defect prediction and localization
To improve software quality, just-in-time defect prediction (JIT-DP)(identifying defect-
inducing commits) and just-in-time defect localization (JIT-DL)(identifying defect-inducing …
inducing commits) and just-in-time defect localization (JIT-DL)(identifying defect-inducing …
Defect prediction with semantics and context features of codes based on graph representation learning
J Xu, F Wang, J Ai - IEEE Transactions on Reliability, 2020 - ieeexplore.ieee.org
To optimize the process of software testing and to improve software quality and reliability,
many attempts have been made to develop more effective methods for predicting software …
many attempts have been made to develop more effective methods for predicting software …
Revisiting supervised and unsupervised models for effort-aware just-in-time defect prediction
Effort-aware just-in-time (JIT) defect prediction aims at finding more defective software
changes with limited code inspection cost. Traditionally, supervised models have been …
changes with limited code inspection cost. Traditionally, supervised models have been …
Leopard: Identifying vulnerable code for vulnerability assessment through program metrics
Identifying potentially vulnerable locations in a code base is critical as a pre-step for effective
vulnerability assessment; ie, it can greatly help security experts put their time and effort to …
vulnerability assessment; ie, it can greatly help security experts put their time and effort to …
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 …