Article

Applying particle swarm optimization to software testing

Authors:

Andreas Windisch,

Stefan Wappler,

Joachim WegenerAuthors Info & Claims

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

Pages 1121 - 1128

https://doi.org/10.1145/1276958.1277178

Published: 07 July 2007 Publication History

Abstract

Evolutionary structural testing is an approach to automatically generating test cases that achieve high structural code coverage. It typically uses genetic algorithms (GAs) to search for relevant test cases. In recent investigations particle swarm optimization (PSO), an alternative search technique, often outperformed GAs when applied to various problems. This raises the question of how PSO competes with GAs in the context of evolutionary structural testing.In order to contribute to an answer to this question, we performed experiments with 25 small artificial test objects and 13 more complex industrial test objects taken from various development projects. The results show that PSO outperforms GAs for most code elements to be covered in terms of effectiveness and efficiency.

References

[1]

B. Clow and T. White. An evolutionary race: A comparison of genetic algorithms and particle swarm optimization for training neural networks. In Proceedings of the International Conference on Artificial Intelligence, IC-AI '04, Volume 2, pages 582--588. CSREA Press, 2004.

[2]

R. C. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. In Proceedings of the 6th International Symposium on Micromachine Human Science, pages 39--43, 1995.

[3]

Genetic and Evolutionary Algorithm Toolbox for use with Matlab. http://www.geatbx.com.

[4]

R. Hassan, B. Cohanim, and O. de Weck. A comparison of particle swarm optimization and the genetic algorithm. In Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, 2005.

[5]

R. J. W. Hodgson. Partical swarm optimization applied to the atomic cluster optimization problem. In GECCO, pages 68--73, 2002.

Digital Library

[6]

T. Huang and A. S. Mohan. A hybrid boundary condition for robust particle swarm optimization. IEEE Antennas and Wireless Propagation Letters, 4:112--117, 2005.

[7]

L. Oliva J. Horák, P. Chmela and Z. Raida. Global optimization of the dual-band planar antenna: Pso versus ga. In Radioelektronika, 2006.

[8]

B. F. Jones, H. Sthamer, and D. E. Eyres. Automatic test data generation using genetic algorithms. Software Engineering Journal, 11(5):299--306, September 1996.

[9]

K. O. Jones. Comparison of genetic algorithm and particle swarm optimization. In Proceedings of the International Conference on Computer Systems and Technologies, 2005.

[10]

J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, volume 4, pages 1942--1948 vol.4. IEEE Press, 1995.

[11]

J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation, 10:281--295, 2006.

Digital Library

[12]

R. P. Pargas, M. J. Harrold, and R. R. Peck. Test-data generation using genetic algorithms. Journal of Software Testing, Verification and Reliability, 9(4):263--282, 1999.

[13]

J. Robinson and Y. Rahmat-Samii. Particle swarm optimization in electromagnetics. IEEE Transactions on Antennas and Propagation, 52:397--407, Feb. 2004.

[14]

J. Wegener, A. Baresel, and H. Sthamer. Evolutionary test environment for automatic structural testing. Information and Software Technology, 43(1):841--854, 2001.

[15]

S. E. Xanthakis, C. C. Skourlas, and A.K. LeGall. Application of genetic algorithms to software testing. In Proceedings of the 5th International Conference on Software Engineering and its Applications, pages 625--636, 1992.

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Index Terms

Applying particle swarm optimization to software testing
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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cover image ACM Conferences

GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation

July 2007

2313 pages

ISBN:9781595936974

DOI:10.1145/1276958

General Chair:
Hod Lipson
Cornell University, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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GECCO07: Genetic and Evolutionary Computation Conference

July 7 - 11, 2007

London, England

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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https://doi.org/10.1109/TSE.2024.3354971
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