Divide-by-zero Exceptions Raising via Branch Coverage. Bhattacharya, N., Sakti, A., Antoniol, G., Gu�h�neuc, Y., & Pesant, G. In Cohen, M. & Cinn�ide, M. �, 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.
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Paper abstract bibtex
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�l Gu�h�neuc and Gilles Pesant},
BOOKTITLE = {Proceedings of the 3<sup>rd</sup> International Symposium on Search-based Software Engineering (SSBSE)},
TITLE = {Divide-by-zero Exceptions Raising via Branch Coverage},
YEAR = {2011},
OPTADDRESS = {},
OPTCROSSREF = {},
EDITOR = {Myra Cohen and Mel � Cinn�ide},
MONTH = {September},
NOTE = {10 pages.},
OPTNUMBER = {},
OPTORGANIZATION = {},
PAGES = {204--218},
PUBLISHER = {IEEE CS Press},
OPTSERIES = {},
OPTVOLUME = {},
KEYWORDS = {Topic: <b>Test case generation</b>, Venue: <c>SSBSE</c>},
URL = {http://www.ptidej.net/publications/documents/SSBSE11a.doc.pdf},
PDF = {http://www.ptidej.net/publications/documents/SSBSE11a.ppt.pdf},
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).}
}Downloads: 0
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