NAR-miner: discovering negative association rules from code for bug detection

Pan Bian, Bin Liang, Wenchang Shi, Jianjun Huang, Yan Cai

Abstract

Inferring programming rules from source code based on data mining techniques has been proven to be effective to detect software bugs. Existing studies focus on discovering positive rules in the form of A ⇒ B, indicating that when operation A appears, operation B should also be here. Unfortunately, the negative rules (A ⇒ ¬ B), indicating the mutual suppression or conflict relationships among program elements, have not gotten the attention they deserve. In fact, violating such negative rules can also result in serious bugs.

BibTeX

@inproceedings{Bian-al:FSE18,
  author    = {Pan Bian and
               Bin Liang and
               Wenchang Shi and
               Jianjun Huang and
               Yan Cai},
  title     = {{NAR-miner:} discovering negative association rules from code for bug detection},
  booktitle = {{ESEC/SIGSOFT} {FSE}},
  pages     = {411--422},
  publisher = {{ACM}},
  year      = {2018},
}

NAR-miner: discovering negative association rules from code for bug detection

Abstract

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