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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on reliability Jg. 71; H. 2; S. 911 - 932
Hauptverfasser:	Kumar, Rakesh, Chaturvedi, Amrita, Kailasam, Lakshmanan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!