@INPROCEEDINGS{Sakti13-CPAIOR-ConstraintFitness,
   AUTHOR       = {Abdelilah Sakti and Yann-Ga�l Gu�h�neuc and 
      Gilles Pesant},
   BOOKTITLE    = {Proceedings of the 10<sup>th</sup> International Conference on Integration of Artificial Intelligence and Operations Research in Constraint Programming (CPAIOR)},
   TITLE        = {Constraint-based Fitness Function for Search-Based 
      Software Testing},
   YEAR         = {2013},
   OPTADDRESS   = {},
   OPTCROSSREF  = {},
   EDITOR       = {Carla Gomes and Meinolf Sellmann},
   MONTH        = {May},
   NOTE         = {Short paper. 4 pages.},
   OPTNUMBER    = {},
   OPTORGANIZATION = {},
   PAGES        = {378--385},
   PUBLISHER    = {Springer},
   OPTSERIES    = {},
   OPTVOLUME    = {},
   KEYWORDS     = {Topic: <b>Test case generation</b>, 
      Venue: <c>CPAIOR</c>},
   URL          = {http://www.ptidej.net/publications/documents/CPAIOR13.doc.pdf},
   PDF          = {http://www.ptidej.net/publications/documents/CPAIOR13.ppt.pdf},
   ABSTRACT     = {Evolutionary testing approach is a powerful automated 
      technique for generating test inputs. Its goal is to reach a branch 
      or a statement in a program under test. One major limit of this 
      approach is its fitness function that does not offer enough 
      information to orient the search to reach a test target with the 
      existence of nested predicates. To address the problem, we propose a 
      new fitness function based on constraint programming. The level of 
      difficulty to satisfy a constraint is the main factor for ranking 
      test candidates: We modulate predicates as a constraint satisfaction 
      problem, then the difficulty-level of a constraint is determined 
      according to the its impact on the search space. Difficulty level is 
      a novel fitness function have been designed to deal with nested 
      predicates and its usefulness have been improved based on benchmarks 
      from the literature.}
}

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Its goal is to reach a branch or a statement in a program under test. One major limit of this approach is its fitness function that does not offer enough information to orient the search to reach a test target with the existence of nested predicates. To address the problem, we propose a new fitness function based on constraint programming. The level of difficulty to satisfy a constraint is the main factor for ranking test candidates: We modulate predicates as a constraint satisfaction problem, then the difficulty-level of a constraint is determined according to the its impact on the search space. 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