On modeling software defect repair time

Rattikorn Hewett, Phongphun Kijsanayothin

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

The ability to predict the time required to repair software defects is important for both software quality management and maintenance. Estimated repair times can be used to improve the reliability and time-to-market of software under development. This paper presents an empirical approach to predicting defect repair times by constructing models that use well-established machine learning algorithms and defect data from past software defect reports. We describe, as a case study, the analysis of defect reports collected during the development of a large medical software system. Our predictive models give accuracies as high as 93.44%, despite the limitations of the available data. We present the proposed methodology along with detailed experimental results, which include comparisons with other analytical modeling approaches.

Original languageEnglish
Pages (from-to)165-186
Number of pages22
JournalEmpirical Software Engineering
Volume14
Issue number2
DOIs
StatePublished - Apr 2009

Keywords

  • Data mining
  • Defect report analysis
  • Quality assurance
  • Software testing
  • Testing management

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