Software defect prediction using hybrid model (CBIL) of convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). [PDF]
PeerJ Comput Sci, 2021 In recent years, the software industry has invested substantial effort to improve software quality in organizations. Applying proactive software defect prediction will help developers and white box testers to find the defects earlier, and this will ...
Farid AB +3 more
europepmc +2 more sources
Researcher bias: The use of machine learning in software defect prediction [PDF]
, 2014 This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2014 IEEE. Personal use of this material is permitted.
Bowes, David +5 more
core +1 more source
Automatic Feature Exploration and an Application in Defect Prediction
IEEE Access, 2019 Many software engineering tasks heavily rely on hand-crafted software features, e.g., defect prediction, vulnerability discovery, software requirements, code review, and malware detection.
Yu Qiu +4 more
doaj +1 more source
Prediction of Cross Project Defects using Ensemble based Multinomial Classifier [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2020 BACKGROUND: The availability of defect related data of different projects leads to cross project defect prediction an open issue. Many studies have focused on analyzing and improving the performance of Cross project defect prediction.OBJECTIVE: The ...
Lipika Goel +3 more
doaj +1 more source
Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review
IEEE Access, 2021 Recent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach.
F. Matloob +7 more
semanticscholar +1 more source
Practitioners’ Perceptions of the Goals and Visual Explanations of Defect Prediction Models [PDF]
IEEE Working Conference on Mining Software Repositories, 2021 Software defect prediction models are classifiers that are constructed from historical software data. Such software defect prediction models have been proposed to help developers optimize the limited Software Quality Assurance (SQA) resources and help ...
Jirayus Jiarpakdee +2 more
semanticscholar +1 more source
Extending Developer Experience Metrics for Better Effort-Aware Just-In-Time Defect Prediction
IEEE Access, 2022 Developers use defect prediction models to efficiently allocate limited resources for quality assurance and appropriately make a plan for software quality improvement activities.
Yeongjun Cho +3 more
doaj +1 more source
Data quality: Some comments on the NASA software defect datasets [PDF]
, 2013 Background-Self-evidently empirical analyses rely upon the quality of their data. Likewise, replications rely upon accurate reporting and using the same rather than similar versions of datasets.
Mair, C +7 more
core +1 more source
Causally Remove Negative Confound Effects of Size Metric for Software Defect Prediction
Applied Sciences, 2022 Software defect prediction technology can effectively detect potential defects in the software system. The most common method is to establish machine learning models based on software metrics for prediction.
Chenlong Li, Yuyu Yuan, Jincui Yang
doaj +1 more source
Time to failure prediction in rubber components subjected to thermal ageing: A combined approach based upon the intrinsic defect concept and the fracture mechanics [PDF]
, 2014 In this contribution, we attempt to derive a tool allowing the prediction of the stretch ratioat failure in rubber components subjected to thermal ageing.
NAÏT-ABDELAZIZ, M +10 more
core +1 more source