Bugram: bug detection with n-gram language models

Song Wang, Devin Chollak, Dana Movshovitz-Attias, Lin Tan

Abstract

To improve software reliability, many rule-based techniques have been proposed to infer programming rules and detect violations of these rules as bugs. These rule-based approaches often rely on the highly frequent appearances of certain patterns in a project to infer rules. It is known that if a pattern does not appear frequently enough, rules are not learned, thus missing many bugs.

BibTeX

@inproceedings{Wang-al:ASE16,
  author    = {Song Wang and
               Devin Chollak and
               Dana Movshovitz{-}Attias and
               Lin Tan},
  title     = {Bugram: bug detection with n-gram language models},
  booktitle = {ASE},
  pages     = {708--719},
  publisher = {{ACM}},
  year      = {2016},
}

Bugram: bug detection with n-gram language models

Abstract

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