Software defect data and predictability for testing schedules

Rattikorn Hewett, Aniruddha Kulkarni, Catherine Stringfellow, Anneliese Andrews

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

4 Scopus citations

Abstract

Software defect data are typically used in reliability modeling to predict the remaining number of defects in order to assess software quality and release decisions However, in practice such decisions are often constrained by availability of resources As software gets more complex, testing and fixing defects become difficult to schedule This paper attempts to predict an estimated time for fixing software defects found during testing processes We present an empirical approach that employs well-established data mining algorithms to construct predictive models from historical defect data We evaluate the approach using a dataset of defect reports obtained from testing of a release of a large medical system The accuracy obtained from our predictive models is as high as 93% despite the fact that not all relevant information was collected The paper discusses detailed methods of experiments, results and their interpretations.

Original languageEnglish
Title of host publication18th International Conference on Software Engineering and Knowledge Engineering, SEKE 2006
Pages499-504
Number of pages6
StatePublished - 2006
Event18th International Conference on Software Engineering and Knowledge Engineering, SEKE 2006 - San Francisco Bay, CA, United States
Duration: Jul 5 2006Jul 7 2006

Publication series

Name18th International Conference on Software Engineering and Knowledge Engineering, SEKE 2006

Conference

Conference18th International Conference on Software Engineering and Knowledge Engineering, SEKE 2006
CountryUnited States
CitySan Francisco Bay, CA
Period07/5/0607/7/06

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