An Empirical Comparison of Model Validation Techniques for Defect Prediction Models

Defect prediction models help software quality assurance teams to allocate their limited resources to the most defect-prone modules. Model validation techniques, such as $k$ -fold cross-validation, use historical data to estimate how well a model will perform in the future. However, little is known about how accurate the estimates of model validation techniques tend to be. In this paper, we investigate the bias and variance of model validation techniques in the domain of defect prediction. Analysis of 101 public defect datasets suggests that 77 percent of them are highly susceptible to producing unstable results– - selecting an appropriate model validation technique is a critical experimental design choice.…

• CC
  Compute Canada
• JS
  Japan Society for the Promotion of Science London

  Award: 16J03360
• NS
  Natural Sciences and Engineering Research Council of Canada
• JS
  Japan Society for the Promotion of Science