Total views : 3656

An Efficient Heuristic Based Test Suite Minimization Approach


  • Department of Computer Science and Applications, Barkatullah University, Bhopal – 462026, Madhya Pradesh, India


Objectives: Development of an efficient test suite minimization approach in order to reduce the size of a previously acquired test suite and produce a new representative suite which will guarantee the same requirement coverage that was achieved before minimization for an effective and efficient regression testing. Method: Test suite minimizations techniques try to reduce the size and redundancy of test suite by removing certain test cases since requirement covered by them are already covered by other test cases. But, it has been found that the acquired test cases after minimization severely lacks ability to achieve the desirable code coverage because the minimization was done based on a single test adequacy criteria. In this paper, we propose an efficient heuristic based test suite minimization algorithm which will reduce the size of the test suites with respective to multiple test adequacy criterions in order to preserve the fault detection effectiveness and code coverage characteristics of the final test suite. Findings: Our experimental results indicate that a significant percentage of reduction in the test suite size is achieved when the minimization is performed with respect to multiple test adequacy criterions. Our approach is unique compared to the existing approaches in the sense that, we carried out minimization based on multiple test adequacy criterions while most of the existing approaches usually take one or two criterions into consideration. The proposed approach is evaluated based on two well known software testing metrics; one indicate the percentage of reduction in test suite size and the second one indicate the percentage of code coverage achieved by the minimized test suite. Our experimental results indicate that a significant percentage of reduction in the size as well as significant code coverage characteristics is achieved when the minimization is done according to the proposed approach. Improvements: The important contribution of this study is that, it presents a novel and efficient test suite minimization technique that optimizes the test suite size based on multiple adequacy criterions.


Regression Testing, Software Testing, Test Data Generation, Test Suite Minimization, Test Suite Selection and Data Clustering.

Full Text:

 |  (PDF views: 1102)


  • Black J, Melachrinoudis E, Kaeli D. Bi-criteria models for all uses test suite reduction. Proceedings of the 26th International Conference on Software Engineering, 2004. p. 106–15. Crossref
  • Chen TY, Lau MF. A new heuristic for test suite reduction.
  • Information and Software Technology. 1998 Jul; 40(56):347–54. Crossref
  • Harrold MJ, Gupta R, Soffa ML. A methodology for controlling the size of a test suite. ACM Transactions on Software Engineering and Methodology. 1993 Jul; 2(3):270–85. Crossref
  • Hartmann J, Robson D J. Revalidation during the software maintenance phase. Proceedings Conference on Software Maintenance, 1989. p. 70–80. Crossref
  • Horgan JR, London S. A data flow coverage testing tool for C. Proceedings of Second Symposium on Assessment of Quality Software Development Tools, 1992. p. 2–10. Crossref
  • Mansour N, El-Fakih K. Simulated annealing and genetic algorithms for optimal regression testing. Journal of Software: Evolution and Process. 1999 Janl; 11(1):19–34.
  • Offutt AJ, Pan J, Voas JM. Procedures for reducing the size of coverage based test sets. Proceedings of 12th International Conference on Testing Computer Software, 1995 Jun. p. 111–23.
  • Garey MR, Johnson DS. Computers and Intractability. A Guide to the Theory of NP-Completeness. New York: W. H. Freeman & Company; 1979. PMCid:PMC1619045
  • Chvatal V. A Greedy Heuristic for the Set-Covering Problem. Mathematics of Operations Research. 1979 Aug; 4(3):233–5. Crossref
  • Tallam S, Gupta N. A concept analysis inspired greedy algorithm for test suite minimization. ACM SIGSOFT Software Engineering Notes. 2006 Jan; 31(1):35–42. Crossref
  • Hsu HY, Orso A. MINTS: A general framework and tool for supporting test-suite minimization. IEEE 31st International conference on Software Engineering, 2009. p. 419–29. Crossref
  • Yoo S, Harman M. Regression testing minimization, selection and prioritization: A survey. Software Testing, Verification and Reliability. 2012 Mar; 22(2):67–120. Crossref
  • Jeffrey D, Gupta N. Test suite reduction with selective redundancy. Proceedings of the 21st IEEE International Conference on Software Maintenance, 2005. p. 549–58. Crossref
  • Heimdahl MPE, George D. Test-Suite Reduction for ModelBased Tests: Effects on Test Quality and Implications for Testing. Proceedings of the 19th IEEE International Conference on Automated Software Engineering, 2004. p. 176–85. Crossref
  • Rothermel G, Harrold MJ, Ostrin J, Hong C. An Empirical Study of the Effects of Minimization on the Fault Detection Capabilities of Test Suites. International Conference of Software Maintenance, 1998 Nov. p. 34–43. Crossref
  • Gupta AK, Khan FA. An Efficient Test Data Generation Approach for Unit Testing. IOSR Journal of Computer Engineering (IOSR-JCE ). 2016; 18(4):97–107. Crossref
  • Zhu H, Hall PAV, May JHR. Software unit test coverage and adequacy. ACM Computing Surveys. 1997 Dec; 29(4):366– 427. Crossref
  • The random test data generation tool. Available from Crossref Accessed on 25/11/2016.
  • Wong WE, Horgan JR, London S, Mathur AP. Effect of test set Minimzation on fault detection effectiveness. Proceedings of the 17th international conference on Software engineering, 1995. p. 41–50.
  • Khan FA, Gupta AK, Bora DJ. An Efficient Technique to Test Suite Minimization using Hierarchical Clustering Approach. International Journal of Emerging Science and Engineering (IJESE). 2015 Sep; 3(11):1–9.
  • Khan FA, Gupta AK, Bora DJ. Profiling of Test Cases with Clustering Methodology. International Journal of Computer Applications. 2014 Nov; 106(14):32–7.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.