@inproceedings{Beschastnikh13icse,
  author = {Ivan Beschastnikh and Yuriy Brun and Jenny Abrahamson and Michael D.
  Ernst and Arvind Krishnamurthy},
  title =
  {\href{http://people.cs.umass.edu/brun/pubs/pubs/Beschastnikh13icse.pdf}{Unifying
  FSM-inference algorithms through declarative specification}},
  booktitle = {Proceedings of the 35th International Conference on Software
  Engineering (ICSE)},
  venue = {ICSE},
  address = {San Francisco, CA, USA},
  month = {May},
  date = {22--24},
  year = {2013},
  pages = {252--261},
  doi = {10.1109/ICSE.2013.6606571},  
  note = {A previous version appeared as University of Washington technical
  report UW-CSE-13-03-01. \href{https://doi.org/10.1109/ICSE.2013.6606571}{DOI: 10.1109/ICSE.2013.6606571}},
  previous = {A previous version appeared as University of Washington technical
  report UW-CSE-13-03-01.},
  accept = {$\frac{85}{461} \approx 18\%$},

  abstract = {Logging system behavior is a staple development
  practice. Numerous powerful model inference algorithms have been
  proposed to aid developers in log analysis and system understanding.
  Unfortunately, existing algorithms are difficult to understand,
  extend, and compare. This paper presents InvariMint, an approach to
  specify model inference algorithms declaratively. We apply
  InvariMint to two model inference algorithms and present evaluation
  results to illustrate that InvariMint (1) leads to new fundamental
  insights and better understanding of existing algorithms, (2)
  simplifies creation of new algorithms, including hybrids that extend
  existing algorithms, and (3) makes it easy to compare and contrast
  previously published algorithms. Finally, InvariMint's declarative
  approach can outperform equivalent procedural algorithms.},

  fundedBy = {Google Inc. via the Faculty Research Award, 
  DARPA FA8750-12-2-0107, NSF CNS-0963754, NSF CCF-1016701},
}