Robust prediction of fault-proneness by random forests

  title={Robust prediction of fault-proneness by random forests},
  author={Lan Guo and Yan Ma and Bojan Cukic and Harshinder Singh},
  journal={15th International Symposium on Software Reliability Engineering},
Accurate prediction of fault prone modules (a module is equivalent to a C function or a C+ + method) in software development process enables effective detection and identification of defects. Such prediction models are especially beneficial for large-scale systems, where verification experts need to focus their attention and resources to problem areas in the system under development. This paper presents a novel methodology for predicting fault prone modules, based on random forests. Random… CONTINUE READING
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