On modeling software defect repair time

  • Hewett R
  • Kijsanayothin P
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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. © 2008 Springer Science+Business Media, LLC.

Author-supplied keywords

  • Analytical modeling
  • Computer software maintenance
  • Computer software selection and evaluation
  • Constructing models
  • Data mining
  • Defect repairs
  • Defect report analysis
  • Defects
  • Empirical approaches
  • Flow patterns
  • Information management
  • Learning algorithms
  • Learning systems
  • Machine learning algorithms
  • Medical softwares
  • Modeling softwares
  • Photomasks
  • Predictive models
  • Quality assurance
  • Quality control
  • Repair
  • Software defects
  • Software quality managements
  • Software reliability
  • Software testing
  • Testing management
  • Time to markets
  • Total quality management

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  • R Hewett

  • P Kijsanayothin

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