Work

Automatic test data generation for path testing using a new stochastic algorithm

Year: 2005

Type: conference-paper

Abstract: Software testing is an important activity of the software development process, and automate test data generation contributes to reduce cost and time efforts. Path testing is a complex problem and metaheuristics have been proposed to deal with it. In this paper, an initial assessment of the efficacy of a recently proposed metaheuristic, the Generalized Extremal Optimization (GEO), in dealing with such kind of problem is made. Results show that it can be very competitive with the frequently used Genetic Algorithm (GA) metaheuristic, while requiring a much lesser effort in parameter setting, making it an alternative to such method in path testing.

Source: Simpósio Brasileiro de Engenharia de Software (SBES)

DOI: 10.5753/sbes.2005.23823

Authors: Bruno T. de Abreu, Eliane Martins, Fabiano L. de Sousa

Institutions: Universidade Estadual de Campinas (UNICAMP), Instituto Nacional de Pesquisas Espaciais

Language: English

Topic: Software Testing and Debugging Techniques

Subfield: Software

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Conselho Nacional de Desenvolvimento Científico e Tecnológico

OpenAlex ID: W4360930797

FWCI: 0.54

Cites: 38

Cited by: 14

Related to: 10