An approach for test data generation using program slicing and particle swarm optimization

Heuristic search-based test data generation has a potential higher efficiency in software testing with path covering. However, these approaches are suffered in covering the long and complex path. In this paper, we propose a method for generating test data based on program slicing and particle swarm...

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Bibliographic Details
Published in:Neural computing & applications 2014-12, Vol.25 (7-8), p.2047-2055
Main Authors: Jiang, Shujuan, Yi, Dandan, Ju, Xiaolin, Wang, Lingsai, Liu, Yingqi
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Computer systems performance. Reliability
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Original Article
Probability and Statistics in Computer Science
Programming theory
Software
Theoretical computing
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Summary:Heuristic search-based test data generation has a potential higher efficiency in software testing with path covering. However, these approaches are suffered in covering the long and complex path. In this paper, we propose a method for generating test data based on program slicing and particle swarm optimization. With the interest points selected from a target path, we perform a program slicing to remove the statements which are irrelevant to the interest points. Our method simplifies the target path and the actual path to get a better fitness value. After program slices obtained, the population is evolved using particle swarm optimization to improve the efficiency of test data generation.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-014-1692-z