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Predicting Bugs in Large Industrial Software Systems
AT and T Labs, USA.
AT and T Labs, USA.ORCID iD: 0000-0002-1660-199X
2013 (English)In: Software Engineering: International Summer Schools, ISSSE 2009-2011, Salerno, Italy. Revised Tutorial Lectures, Germany: Springer Berlin/Heidelberg, 2013, 71-93 p.Chapter in book (Refereed)
Abstract [en]

This chapter is a survey of close to ten years of software fault prediction research performed by our group. We describe our initial motivation, the variables used to make predictions, provide a description of our standard model based on Negative Binomial Regression, and summarize the results of using this model to make predictions for nine large industrial software systems. The systems range in size from hundreds of thousands to millions of lines of code. All have been in the field for multiple years and many releases, and continue to be maintained and enhanced, usually at 3 month intervals. Effectiveness of the fault predictions is assessed using two different metrics. We compare the effectiveness of the standard model to augmented models that include variables related to developer counts, to inter-file calling structure, and to information about specific developers who modified the code. We also evaluate alternate prediction models based on different training algorithms, including Recursive Partitioning, Bayesian Additive Regression Trees, and Random Forests.

Place, publisher, year, edition, pages
Germany: Springer Berlin/Heidelberg, 2013. 71-93 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7171
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mdh:diva-23705DOI: 10.1007/978-3-642-36054-1_3Scopus ID: 2-s2.0-84893794271ISBN: 978-3-642-36053-4 (print)OAI: oai:DiVA.org:mdh-23705DiVA: diva2:680475
Available from: 2013-12-18 Created: 2013-12-18 Last updated: 2014-04-24Bibliographically approved

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Cite
Citation style
  • apa
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Language
  • de-DE
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Output format
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