Divide-by-zero Exceptions Raising via Branch Coverage. Bhattacharya, N., Sakti, A., Antoniol, G., Guéhéneuc, Y., & Pesant, G. In Cohen, M. & Cinnéide, M. Ó, editors, Proceedings of the 3<sup>rd</sup> International Symposium on Search-based Software Engineering (SSBSE), pages 204–218, September, 2011. IEEE CS Press. 10 pages.Paper abstract bibtex In this paper, we discuss how a search-based branch coverage approach can be used to design an effective test data generation approach, specifically targeting divide-by-zero exceptions. We first propose a novel additive fitness function combining \emphapproach level and \emphbranch distance. We then use different search strategies, ıe hill climbing, simulated annealing, and genetic algorithm, to evaluate the performance of the novel fitness function on a small synthetic example as well as on methods known to throw divide-by-zero exceptions, extracted from real world systems, namely Eclipse and Android. Finally, we also describe how the test data generation for divide-by-zero exceptions can be formulated as a constraint programming problem and compare the resolution of this problem with a genetic algorithm in terms of execution time. We thus report evidence that genetic algorithm using our novel fitness function out-performs hill climbing and simulated annealing and a previous approach (in terms of numbers of fitness evaluation) but is out-performed by constraint programming (in terms of execution time).
@INPROCEEDINGS{Bhattacharya11-SSBSE-DivideByZero,
author = {Neelesh Bhattacharya and Abdelilah Sakti and Giuliano Antoniol and Yann-Ga{\"e}l Gu{\'e}h{\'e}neuc and Gilles Pesant},
title = {Divide-by-zero Exceptions Raising via Branch Coverage},
booktitle = {Proceedings of the 3<sup>{rd}</sup> International Symposium on Search-based Software Engineering ({SSBSE})},
year = {2011},
month = {September},
editor = {Myra Cohen and Mel {\'O} Cinn{\'e}ide},
publisher = {IEEE CS Press},
note = {10 pages.},
abstract = {In this paper, we discuss how a search-based branch coverage approach can be used to design an effective test data generation approach, specifically targeting divide-by-zero exceptions. We first propose a novel additive fitness function combining \emph{approach level} and \emph{branch distance}. We then use different search strategies, \ie{} hill climbing, simulated annealing, and genetic algorithm, to evaluate the performance of the novel fitness function on a small synthetic example as well as on methods known to throw divide-by-zero exceptions, extracted from real world systems, namely Eclipse and Android. Finally, we also describe how the test data generation for divide-by-zero exceptions can be formulated as a constraint programming problem and compare the resolution of this problem with a genetic algorithm in terms of execution time. We thus report evidence that genetic algorithm using our novel fitness function out-performs hill climbing and simulated annealing and a previous approach (in terms of numbers of fitness evaluation) but is out-performed by constraint programming (in terms of execution time).},
grant = {NSERC DG and CRC on Software Patterns},
keywords = {Test case generation ; SSBSE},
kind = {MISA},
language = {english},
url = {http://www.ptidej.net/publications/documents/SSBSE11a.doc.pdf},
pdf = {http://www.ptidej.net/publications/documents/SSBSE11a.ppt.pdf},
pages = {204--218}
}
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