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Heterogeneous Defect Prediction [PDF]
IEEE Transactions on Software Engineering, 2018 Many recent studies have documented the success of cross-project defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. However, most studies share the same limitations: it requires homogeneous data; i.e., different projects must describe themselves using the same ...
Jaechang Nam +4 more
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Revisiting Unsupervised Learning for Defect Prediction [PDF]
Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, 2017 Collecting quality data from software projects can be time-consuming and expensive. Hence, some researchers explore "unsupervised" approaches to quality prediction that does not require labelled data.
Fu, Wei, Menzies, Tim
core +2 more sources
Software Defect Association Mining and Defect Correction Effort Prediction [PDF]
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.
Cartwright, MH +3 more
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Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk. [PDF]
PeerJ Comput Sci, 2019 Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone ...
Niedermayr R, Röhm T, Wagner S.
europepmc +4 more sources
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction. [PDF]
ScientificWorldJournal, 2016 Kumudha P, Venkatesan R.
europepmc +3 more sources
Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers
Mathematics, 2023 To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA.
Akinbowale Nathaniel Babatunde +3 more
doaj +1 more source
A Survey on Software Defect Prediction Using Deep Learning
Mathematics, 2021 Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability.
Elena N. Akimova +6 more
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An Improved Confounding Effect Model for Software Defect Prediction
Applied Sciences, 2023 Software defect prediction technology can effectively improve software quality. Depending on the code metrics, machine learning models are built to predict potential defects.
Yuyu Yuan, Chenlong Li, Jincui Yang
doaj +1 more source
Metals, 2021 In recent decades, the automotive industry has had a constant evolution with consequent enhancement of products quality. In industrial applications, quality may be defined as conformance to product specifications and repeatability of manufacturing ...
Maria Emanuela Palmieri +2 more
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Personalized defect prediction [PDF]
2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2013 Many defect prediction techniques have been proposed. While they often take the author of the code into consideration, none of these techniques build a separate prediction model for each developer. Different developers have different coding styles, commit frequencies, and experience levels, causing different defect patterns.
Tian Jiang, Lin Tan, Sunghun Kim
openaire +1 more source