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Gravitational Bee Search Algorithm with Fuzzy Logic for Effective Test Suite Minimization and Prioritization

Affiliations

  • Department of Computer Science, Karpagam University, Coimbatore - 641021, Tamil Nadu, India

Abstract


Objectives: Software testing is the significant part of software development and is essential to confirm the quality of the software. The test suites developed for this purpose can be used again and updated repeatedly as the software advances. Subsequently, novel test cases will be added to the test suite and because of that, the size of the test suite will become bigger. Moreover, the test suite becomes redundant. Thus executing/re-executing the large test suite consumes more time and also increases the cost of testing. Therefore, in order to minimize the cost and the time of testing, it is essential to minimize the test suite. Methods/Statistical Analysis: Thus, the focus of this paper is to minimize the test suite by discovering a group of test cases that gives the same or better coverage as the original test suite based on some condition. Finding: In this study, the minimization is achieved by using a Gravitational Bee Search (GBS) algorithm, this algorithm is derived by combining artificial bee colony and gravitational search algorithms. Then, the Fuzzy operation is applied for prioritization to achieve efficient test suite. The algorithm searches for the optimum solution by calculating fitness values using coverage information. The search process is repetitive until a reduced test suite is identified. Application/ Improvement: The proposed algorithm is applied on an online ticket booking system and the results shows that the proposed system is approximately 15% to 20% more efficient than the existing system respective to the number of test cases and execution time.

Keywords

Gravitational Bee Search, Software Testing, Test Suite Minimization, Test Coverage.

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References


  • Blue D, Segall I, Tzoref-Brill R, Zlotnick A. Interaction-based test-suite minimization. IEEE ICSE; USA. 2013. p. 182–91.
  • Shahabuddin SM, Prasanth Y. Integration testing prior to unit testing: a paradigm shift in object oriented software testing of agile software engineering. Indian Journal of Science and Technology. 2016 May; 9(20). DOI: 10.17485/ijst/2016/v9i20/91223.
  • Parsa S, Khalilian A. On the optimization approach towards test suite minimization. International Journal of Software Engineering and its Applications. 2010; 4(1):15–28.
  • Mudgal AP. A proposed model for minimization of test suite. Journal of Nature Inspired Computing (JNIC). 2013; 1(2):34–7.
  • Prasad S, Jain M, Singh S, Patvardhan C. Regression optimizer a multi coverage criteria test suite minimization technique. IJAIS. 2002; 1(8):5–11.
  • Selvakumar S, Ramaraj N. Multi-objective minimization of test suite and its cost associates using swarm intelligence. International Review on Computers and Software. 2011; 6(2):275.
  • Singh RR. Test suite minimization using evolutionary optimization algorithms: Review. IJERT. 2014; 3(6):2086–91.
  • Joseph AK, Radhamani G. Fuzzy C Means (FCM) clustering based hybrid swarm intelligence algorithm for test case optimization. Research Journal of Applied Sciences, Engineering and Technology. 2014; 87(1):76–82.
  • Agrawal S, Raw RS, Tyagi N, Misra AK. Fuzzy Logic based Greedy Routing (FLGR) in multi-hop vehicular ad hoc networks. Indian Journal of Science and Technology. 2015 Nov; 8(30). DOI: 10.17485/ijst/2015/v8i1/70085.
  • Raman B, Subramani S. An efficient specific update search domain based glowworm swarm optimization for test case prioritization. The International Arab Journal of Information Technology. 2015; 12(6).
  • De Souza LS, Prudêncio RBC, Flávia A, Barros D. A hybrid particle swarm optimization and harmony search algorithm approach for multi-objective test case selection. Journal of the Brazilian Computer Society. 2015; 21(19):1–5.
  • Sampath S, Bryce R, Atif M, Memon M. A uniform representation of hybrid criteria for regression testing. IEEE Transactions on Software Engineering. 2013; 39(10):1326–44.
  • Jianga S, Wanga Y, Ji Z. Convergence analysis and performance of an improved gravitational search algorithm. Applied Soft Computing (ASC). Elsevier. 2014; 24:363–84.
  • Mala DJ, Mohan V. ABC tester-artificial bee colony based software test suite optimization approach. IJSE. 2009; 2(2):1–33.
  • Malhotra R, Anand C, Jain N, Mittal A. Comparison of search based techniques for automated test data generation. International Journal of Computer Applications. 2014; 95(23):1–5.
  • Khajehzadeh M, Eslami M. Gravitational search algorithm for optimization of retaining structures. Indian Journal of Science and Technology. 2012 Jan; 5(1). DOI: 10.17485/ijst/2012/v5i1/30937.
  • Tavakkolai H, Yadollahi N, Yadollahi M, Hosseinabadi AAR, Kardgar M. Sensor selection wireless multimedia sensor network using gravitational search algorithm. Indian Journal of Science and Technology. 2015 Jul; 8(14). DOI: 10.17485/ijst/2015/v8i14/68808.
  • Suri B, Mangal I, Srivastava V. Regression test suite reduction using an hybrid technique based on BCO and genetic algorithm. IJCSI. 2009; 2(1-2):165–72.

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