Data mining techniques for building fault-proneness models in telecom java software

Erik Arisholm, Lionel C. Briand, Magnus Fuglerud

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper describes a study performed in an industrial setting that attempts to build predictive models to identify parts of a Java system with a high fault probability. The system under consideration is constantly evolving as several releases a year are shipped to customers. Developers usually have limited resources for their testing and inspections and would like to be able to devote extra resources to faulty system parts. The main research focus of this paper is two-fold: (1) use and compare many data mining and machine learning techniques to build fault-proneness models based mostly on source code measures and change/fault history data, and (2) demonstrate that the usual classification evaluation criteria based on confusion matrices may not be fully appropriate to compare and evaluate models.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Symposium on Software Reliability Engineering, ISSRE 2007
Pages215-224
Number of pages10
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event18th IEEE International Symposium on Software Reliability Engineering, ISSRE 2007 - Trollhattan, Sweden
Duration: 5 Nov 20079 Nov 2007

Publication series

NameProceedings - International Symposium on Software Reliability Engineering, ISSRE
ISSN (Print)1071-9458

Conference

Conference18th IEEE International Symposium on Software Reliability Engineering, ISSRE 2007
Country/TerritorySweden
CityTrollhattan
Period5/11/079/11/07

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