RT Journal Article
T1 Observations in using parallel and sequential evolutionary algorithms for automatic software testing
A1 Alba-Torres, Enrique
A1 Chicano-García, José-Francisco
K1 Computación evolutiva
AB In this paper we analyze the application of parallel and sequential evolutionary algorithms (EAs) to the automatic test data generation problem. The problem consists of automatically creating a set of input data to test a program. This is a fundamental step in software development and a time consuming task in existing software companies. Canonical sequential EAs have been used in the past for this task. We explore here the use of parallel EAs. Evidence of greater efficiency, larger diversity maintenance, additional availability of memory/CPU, and multi-solution capabilities of the parallel approach, reinforce the importance of the advances in research with these algorithms. We describe in this work how canonical genetic algorithms (GAs) and evolutionary strategies (ESs) can help in software testing, and what the advantages are (if any) of using decentralized populations in these techniques. In addition, we study the influence of some parameters of the proposed test data generator in the results. For the experiments we use a large benchmark composed of twelve programs that includes fundamental algorithms in computer science.
YR 2014
FD 2014-10-01
LK http://hdl.handle.net/10630/8146
UL http://hdl.handle.net/10630/8146
LA eng
NO Computers & Operations Research, 35 (10),2007, pp.3161-3183
NO Ministry of Education and Science and FEDER under Contract TIN2005-08818-C04-01 (the OPLINK Project). Francisco Chicano was supported by a Grant (BOJA 68/2003) from the Junta de Andalucía (Spain).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026