An Unsupervised Software Fault Prediction Approach Using Threshold Derivation

Software fault prediction models help the software quality assurance team to manage the resources optimally during software maintenance. Most of the recently proposed fault prediction approaches are helpful on labeled datasets only. Recently, several threshold-based software fault prediction approac...

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Vydané v:	IEEE transactions on reliability Ročník 71; číslo 2; s. 911 - 932
Hlavní autori:	Kumar, Rakesh, Chaturvedi, Amrita, Kailasam, Lakshmanan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Clustering Codes Correlation coefficients Datasets Derivation Fault diagnosis Labeling Measurement Metric threshold post-hoc Nemenyi test Prediction models Predictive models Quality assurance random forest (RF) Software software fault prediction Software metrics Training unsupervised learning
ISSN:	0018-9529, 1558-1721
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