Data Mining Static Code Attributes to Learn Defect Predictors

@inproceedings{Menzies2007DataMS,
  title={Data Mining Static Code Attributes to Learn Defect Predictors},
  author={Tim Menzies and Jeremy Greenwald and Art Frank},
  year={2007}
}
The value of using static code attributes to learn defect predictors has been widely debated. Prior work has explored issues like the merits of "McCabes versus Halstead versus lines of code counts" for generating defect predictors. We show here that such debates are irrelevant since how the attributes are used to build predictors is much more important than which particular attributes are used. Also, contrary to prior pessimism, we show that such defect predictors are demonstrably useful and… CONTINUE READING

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