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Comparing the Effectiveness of Several Modeling Methods for Fault Prediction
AT and T Labs, United States .ORCID iD: 0000-0002-1660-199X
AT and T Labs, United States .
AT and T Labs, United States .
2010 (English)In: Journal of Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 15, no 3, p. 277-295Article in journal (Refereed) Published
Abstract [en]

We compare the effectiveness of four modeling methods-negative binomial regression, recursive partitioning, random forests and Bayesian additive regression trees-for predicting the files likely to contain the most faults for 28 to 35 releases of three large industrial software systems. Predictor variables included lines of code, file age, faults in the previous release, changes in the previous two releases, and programming language. To compare the effectiveness of the different models, we use two metrics-the percent of faults contained in the top 20% of files identified by the model, and a new, more general metric, the fault-percentile-average. The negative binomial regression and random forests models performed significantly better than recursive partitioning and Bayesian additive regression trees, as assessed by either of the metrics. For each of the three systems, the negative binomial and random forests models identified 20% of the files in each release that contained an average of 76% to 94% of the faults. 

Place, publisher, year, edition, pages
2010. Vol. 15, no 3, p. 277-295
National Category
Engineering and Technology
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URN: urn:nbn:se:mdh:diva-23690DOI: 10.1007/s10664-009-9111-2ISI: 000276480300003Scopus ID: 2-s2.0-77954760375OAI: oai:DiVA.org:mdh-23690DiVA, id: diva2:680316
Available from: 2013-12-17 Created: 2013-12-17 Last updated: 2017-12-06Bibliographically approved

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Weyuker, Elaine

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