@inproceedings{Ohmann14ase,
  author = {Tony Ohmann and Michael Herzberg and Sebastian Fiss and
  Armand Halbert and Marc Palyart and Ivan Beschastnikh and 
  Yuriy Brun},
  title =
  {Behavioral
  Resource-Aware Model Inference},
  booktitle = {Proceedings of the 29th IEEE/ACM International
  Conference on Automated Software Engineering (ASE)},
  venue = {ASE},
  pages = {19--30},
  address = {V{\"{a}}ster{\aa}s, Sweden},
  month = {September},
  date = {15--19},
  year = {2014},
  accept = {$\frac{50}{258} \approx 19\%$},

  doi = {10.1145/2642937.2642988},  
  note = {DOI: 10.1145/2642937.2642988},

  abstract = {Software bugs often arise because of differences between what
  developers think their system does and what the system actually does.
  These differences frustrate debugging and comprehension efforts. We
  describe Perfume, an automated approach for inferring behavioral,
  resource-aware models of software systems from logs of their
  executions. These finite state machine models ease understanding of
  system behavior and resource use.

  Perfume improves on the state of the art in model inference by
  differentiating behaviorally similar executions that differ in
  resource consumption. For example, Perfume separates otherwise
  identical requests that hit a cache from those that miss it, which can
  aid understanding how the cache affects system behavior and removing
  cache-related bugs. A small user study demonstrates that using Perfume
  is more effective than using logs and another model inference tool for
  system comprehension. A case study on the TCP protocol demonstrates
  that Perfume models can help understand non-trivial protocol behavior.
  Perfume models capture key system properties and improve system
  comprehension, while being reasonably robust to noise likely to occur
  in real-world executions.},

  fundedBy = {NSERC, Microsoft Research via a SEIF award},
}