Results 261 to 270 of about 86,307 (298)

Multiscale Modeling of Quantum Dot Solar Cells: Integration of Density Functional Theory, SCAPS, Lambert W Analysis, and Machine Learning. [PDF]

ACS Omega
Yahyaoui N.
europepmc   +1 more source

Software defect association mining and defect correction effort prediction

IEEE Transactions on Software Engineering, 2006
Much current software defect prediction work concentrates on the number of defects remaining in software system. In this paper, we present association rule mining based methods to predict defect associations and defect-correction effort.
Qinbao Song, Martin Shepperd, M Cartwright   +2 more
exaly   +2 more sources

A General Software Defect-Proneness Prediction Framework [PDF]

IEEE Transactions on Software Engineering, 2011
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2011 IEEE. Personal use of this material is permitted.
Qinbao Song, Martin Shepperd, Shi Ying   +2 more
exaly   +2 more sources

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DeepLineDP: Towards a Deep Learning Approach for Line-Level Defect Prediction

IEEE Transactions on Software Engineering, 2023
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 software defects.
Chanathip Pornprasit, C. Tantithamthavorn   +1 more
semanticscholar   +1 more source

A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools

Engineering applications of artificial intelligence, 2022
Delivering high-quality software products is a challenging task. It needs proper coordination from various teams in planning, execution, and testing. Many software products have high numbers of defects revealed in a production environment.
Jalaj Pachouly, Swati Ahirrao, K. Kotecha, Ganeshsree Selvachandran, A. Abraham   +4 more
semanticscholar   +1 more source

The best of both worlds: integrating semantic features with expert features for defect prediction and localization

ESEC/SIGSOFT FSE, 2022
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 code lines in commits) have been widely studied by learning semantic ...
Chao Ni, Wei Wang, Kaiwen Yang, Xin Xia, Kui Liu, David Lo   +5 more
semanticscholar   +1 more source

A Systematic Survey of Just-in-Time Software Defect Prediction

ACM Computing Surveys, 2022
Recent years have experienced sustained focus in research on software defect prediction that aims to predict the likelihood of software defects. Moreover, with the increased interest in continuous deployment, a variant of software defect prediction ...
Yunhua Zhao, Kostadin Damevski, Hui Chen
semanticscholar   +1 more source

Deep Semantic Feature Learning for Software Defect Prediction

IEEE Transactions on Software Engineering, 2020
Software defect prediction, which predicts defective code regions, can assist developers in finding bugs and prioritizing their testing efforts. Traditional defect prediction features often fail to capture the semantic differences between different ...
Song Wang, Jaechang Nam
exaly   +2 more sources

Deep just-in-time defect prediction: how far are we?

International Symposium on Software Testing and Analysis, 2021
Defect prediction aims to automatically identify potential defective code with minimal human intervention and has been widely studied in the literature.
Zhen Zeng, Yuqun Zhang, Haotian Zhang, Lingming Zhang   +3 more
semanticscholar   +1 more source