Nature inspired algorithm using particle swarm approach with variations in inertia weights for automatic test data generation based on dominance concepts

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    In software testing, testing of all program statements is a very crucial issue as it consumes a lot of time, effort and cost. The time, effort and cost can be reduced by using an efficient technique to reduce the test case and a good optimization algorithm to generate efficient, reliable and unique test cases. In this paper, the concept of dominance tree is used which covers all edges/statement by using minimum test case. Nature inspired algorithm - PSO (Particle Swarm Optimization) by applying different inertia weights is used to generate unique, reliable and efficient test cases to cover the leaf nodes of dominance tree. Inertia weights like fixed inertia weight (FIW), global-local best (GLbestIW), Time-Dependent weight (TDW), and proposed GLbestRandIW weights are used with PSO to investigate the effect of inertia weights on the execution of PSO with respect to number of generation required, percentage coverage , total test cases generated to test the software under consideration.


  • Keywords


    Testing; Particle Swarm Optimization (PSO); Inertia Weight; Dominance Tree.

  • References


      [1] Girgis MR, “Automatic test data generation for data flow testing using genetic algorithm”, Journal of Universal Computer Science, Vol.11, No.6, (2005), pp.898–915.

      [2] Pargas RP, Harrold MJ & Peck RR, “Test Data Generation using Genetic Algorithms”, Software Testing Verification and Reliability, Vol.9, (1999), pp.263-282.https://doi.org/10.1002/(SICI)1099-1689(199912)9:4<263::AID-STVR190>3.0.CO;2-Y.

      [3] Alander JT, Mantere T &Turunen P, “Genetic Algorithm Based Software Testing”, Proceedings of International Conference, (1998), pp.325-328.https://doi.org/10.1007/978-3-7091-6492-1_71.

      [4] Abido MA, “Multiobjective particle swarm optimization technique for environmental/economic dispatch problem”, Electric Power System Research, Vol.79, No.7, (2009), pp.1105–1113.https://doi.org/10.1016/j.epsr.2009.02.005.

      [5] Boyer R, Elspas B & Levitt K, “Select-a formal system for testing and debugging programs by symbolic execution”, SIGPLAN Otices, Vol.10, No.6, (1975), pp.234-245.https://doi.org/10.1145/390016.808445.

      [6] Clarke L, “A system to generate test data and symbolically execute programs”, IEEE Transaction on Software Eng., Vol.SE-2, No.3, (1976), pp.215- 222.https://doi.org/10.1109/TSE.1976.233817.

      [7] Ramamoorthy C, Ho S & Chen W, “On the automated generation of program test data”, IEEE Trans. Software Eng., Vol.SE-2, No.4. (1976), pp.293-300.https://doi.org/10.1109/TSE.1976.233835.

      [8] Howden W, “Symbolic testing and the DISSECT symbolic evaluation system”, IEEE Trans. Software Eng., Vol.SE-4, No.4, (1977), pp.266- 278.https://doi.org/10.1109/TSE.1977.231144.

      [9] Arumugam MS & Rao MVC, “On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems”, Discrete Dynamics in Nature and Society, (2006).https://doi.org/10.1155/DDNS/2006/79295.

      [10] Ince D, “The automatic generation of test data”, Computer Journal, Vol.30, No.1, (1987), pp.63-69. https://doi.org/10.1093/comjnl/30.1.63.

      [11] Miller W & Spooner D, “Automatic generation of floating-point test data”, IEEE Trans. Software Eng., Vol.SE-2, No.3, (1976), pp.223-226.https://doi.org/10.1109/TSE.1976.233818.

      [12] Offutt J, Jin Z & Pan J, “The Dynamic domain reduction procedure for test data generation”, Software Practice and Experience, Vol.29, No.2, (1997), pp.167–193.https://doi.org/10.1002/(SICI)1097-024X(199902)29:2<167::AID-SPE225>3.0.CO;2-V.

      [13] Gupta N, Mathur AP &Soffa ML, “Automat geneticed test data generation using an iterative relaxation method”, ACM SIGSOFT Sixth International Symposium on Foundations of Software Engineering, (1998), pp.231–244.

      [14] Kennedy J & Eberhart R, “Particle swarm optimization”, IEEE International Conference on Neural Networks, (1995), pp.1942–1948.https://doi.org/10.1109/ICNN.1995.488968.

      [15] Narmada N & Mohapatra DP, “Automatic Test Data Generation for data flow testing using Particle Swarm Optimization”, Communications in Computer and Information Science, Vol.95, No.1, (2010), pp.1-12.

      [16] Michael CC, McGraw GE&Schatz MA, “Generating software test data by evolution”, IEEE Transactions on Software Engineering, Vol.27, No.12, (2001), pp.1085-1110. https://doi.org/10.1109/32.988709.

      [17] Ghiduk AS, Harrold MJ&Girgis MR, “Using Genetic Algorithms to Aid Test-Data Generation for Data-Flow Coverage”, 14th Asia-Pacific Software Engineering Conference, (2007).https://doi.org/10.1109/ASPEC.2007.73.

      [18] Ghiduk AS & Girgis MR, “Using Genetic Algorithms and dominance concepts for generating reduced test data”, Informatics, Vol.34, (2010), pp.377-385.

      [19] Chang KH, Cross JH, Carlisle WH & Brown DB, “A framework for intelligent test data generation”, Journal of Intelligent and Robotic Systems-Theory and Application, Vo.5, No.2, (1992), pp.147-165.https://doi.org/10.1007/BF00444293.

      [20] Biswas A, Mishra KK, Tiwari S &Misra AK, “Physics-inspired optimization algorithms: a survey”, Journal of Optimization, (2013).


 

View

Download

Article ID: 9949
 
DOI: 10.14419/ijet.v7i1.1.9949




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.