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Using faults-slip-through metric as a predictor of fault-proneness
Blekinge Institute of Technology. (IS (Embedded Systems))ORCID iD: 0000-0003-0611-2655
2010 (English)In: Proceedings - Asia-Pacific Software Engineering Conference, APSEC, 2010, p. 412-422Conference paper, Published paper (Refereed)
Abstract [en]

Background: The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. Aims: This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete test phase, the prediction is done at the speci?c test levels of integration and system test. Method: We applied eight classi?cation techniques, to the task of identifying faultprone modules, representing a variety of approaches, including a standard statistical technique for classi?cation (logistic regression), tree-structured classi?ers (C4.5 and random forests), a Bayesian technique (Naïve Bayes), machine-learning techniques (support vector machines and back-propagation arti?cial neural networks) and search-based techniques (genetic programming and arti?cial immune recognition systems) on FST data collected from two large industrial projects from the telecommunication domain. Results: Using area under the receiver operating characteristic (ROC) curve and the location of (PF, PD) pairs in the ROC space, the faults-slip-through metric showed impressive results with the majority of the techniques for predicting fault-prone modules at both integration and system test levels. There were, however, no statistically signi?cant differences between the performance of different techniques based on AUC, even though certain techniques were more consistent in the classi?cation performance at the two test levels. Conclusions: We can conclude that the faults-slip-through metric is a potentially strong predictor of fault-proneness at integration and system test levels. The faults-slip-through measurements interact in ways that is conveniently accounted for by majority of the data mining techniques.

Place, publisher, year, edition, pages
2010. p. 412-422
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-22284DOI: 10.1109/APSEC.2010.54Scopus ID: 2-s2.0-79951765157ISBN: 9780769542669 (print)OAI: oai:DiVA.org:mdh-22284DiVA, id: diva2:661352
Conference
17th Asia Pacific Software Engineering Conference: Software for Improving Quality of Life, APSEC 2010; Sydney, NSW; Australia; 30 November 2010 through 3 December 2010
Available from: 2013-11-03 Created: 2013-10-31 Last updated: 2013-12-03Bibliographically approved

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Afzal, Wasif

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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More styles
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  • asciidoc
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