Research of Automatic Test Case Generation Algorithm Based on Improved Particle Swarm Optimization

DOI

10.2991/icmmct-16.2016.310How to use a DOI?

Keywords

Test case; Particle swarm optimization; Neighbor; Software test; Morlet.

Abstract

The software testing is an important way to find bugs, and guarantee the quality and reliability of software. Automatic software case generation can effectively improve test efficiency, reduce test time and cost of development, so it has been widely concerned. Aiming at premature convergence and local optimum problems of automatic software case generation based on particle swarm optimization algorithm, an automatic test case generation algorithm based on improved position and particle swarm optimization is proposed. The proposed algorithm can effectively solve the premature convergence problem by dynamically adjusting the inertia factor and Morlet variation to change the position of particles. Meanwhile, neighbor position information is used to solve the locally optimum problem. Simulation demonstrates that compared with genetic algorithm, artificial immune algorithm and standard particle swarm optimization algorithm, the proposed algorithm is the best in the term of iterations and overhead time.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Weiwei Wu
PY  - 2016/03
DA  - 2016/03
TI  - Research of Automatic Test Case Generation Algorithm Based on Improved Particle Swarm Optimization
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1557
EP  - 1561
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.310
DO  - 10.2991/icmmct-16.2016.310
ID  - Wu2016/03
ER  -