Constraint-based Fitness Function for Search-Based Software Testing. Sakti, A., Guéhéneuc, Y., & Pesant, G. In Gomes, C. & Sellmann, M., editors, Proceedings of the 10<sup>th</sup> International Conference on Integration of Artificial Intelligence and Operations Research in Constraint Programming (CPAIOR), pages 378–385, May, 2013. Springer. Short paper. 4 pages.Paper abstract bibtex 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.
@INPROCEEDINGS{Sakti13-CPAIOR-ConstraintFitness,
author = {Abdelilah Sakti and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc and Gilles Pesant},
title = {Constraint-based Fitness Function for Search-Based Software Testing},
booktitle = {Proceedings of the 10<sup>{th}</sup> International Conference on Integration of Artificial Intelligence and Operations Research in Constraint Programming ({CPAIOR})},
year = {2013},
month = {May},
editor = {Carla Gomes and Meinolf Sellmann},
publisher = {Springer},
note = {Short paper. 4 pages.},
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.},
grant = {NSERC DG and FQRNT team grant},
keywords = {Test case generation ; CPAIOR},
kind = {MISA},
language = {english},
url = {http://www.ptidej.net/publications/documents/CPAIOR13.doc.pdf},
pdf = {http://www.ptidej.net/publications/documents/CPAIOR13.ppt.pdf},
pages = {378--385},
comment = {Short paper.}
}
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