A learning-to-rank based fault localization approach using likely invariants

@inproceedings{Le2016ALB,
  title={A learning-to-rank based fault localization approach using likely invariants},
  author={Tien-Duy B. Le and David Lo and Claire Le Goues and Lars Grunske},
  booktitle={ISSTA},
  year={2016}
}
Debugging is a costly process that consumes much of developer time and energy. To help reduce debugging effort, many studies have proposed various fault localization approaches. These approaches take as input a set of test cases (some failing, some passing) and produce a ranked list of program elements that are likely to be the root cause of the failures (i.e., failing test cases). In this work, we propose Savant, a new fault localization approach that employs a learning-to-rank strategy, using… CONTINUE READING

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