Selection of Metrics for the Defect Prediction

Ilona Bluemke , Anna Stepień


The ability to estimate if a module or a class or a method is faulty, or not, is called the defect prediction. Prediction can be used to target the improvement efforts to those modules or classes that need it the most. We investigated the classification process (deciding if an element is faulty or not) in which the set of software metrics and several data mining algorithms were used. We conducted an experiment on ten open source projects. The data concerning defects were extracted from the repository of the control version system. In this study the process of choosing appropriate metrics for the defect prediction is described. In the selection process we use unique approach by random forest.
Author Ilona Bluemke (FEIT / IN)
Ilona Bluemke,,
- The Institute of Computer Science
, Anna Stepień (FEIT / IN)
Anna Stepień,,
- The Institute of Computer Science
Publication size in sheets0.55
Book Zamojski Wojciech, Mazurkiewicz Jacek, Sugier Jarosław, Walkowiak Tomasz, Kacprzyk Janusz (eds.): Dependability Engineering and Complex Systems. Proceedings of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, Advances in Intelligent Systems and Computing, vol. 470, 2016, Heidelberg New York Dordrecht London, Springer International Publishing, ISBN 978-3-319-39638-5, [978-3-319-39639-2], 606 p., DOI:10.1007/978-3-319-39639-2
Front_Matter.pdf / 144.8 KB / No licence information
Keywords in EnglishDefect prediction, Object metrics
ProjectDevelopment of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Rybiński Henryk, , Phone: +48 22 234 7731, start date 18-05-2015, end date 30-11-2016, II/2015/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
Depcos-6-IBlu-2016.pdf 177.51 KB
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 15.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators Scopus Citations = 3; WoS Citations = 3
Citation count*6 (2020-08-25)
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