Total views : 434

A Comparative Evaluation of “m-ACO” Technique for Test Suite Prioritization

Affiliations

  • M. D. University, Rohtak - 124001, Haryana, India

Abstract


Objectives: The novel test case prioritization technique “m-ACO” (“Modified Ant Colony Optimization”) for regression testing has been comparatively evaluated. Methods: “m-ACO” prioritize the test cases by altering the food source selection criteria of natural ants to enhance fault diversity. The code for the proposed technique for prioritizing test case “m-ACO” has been implemented in Perl language. This paper makes a comparative evaluation of proposed “m-ACO” technique for prioritization of test cases with GA (“Genetic Algorithm”), BCO (“Bee Colony Optimization”) Algorithms and ACO (“Ant Colony Optimization”) Algorithms using three case studies. Two metrics namely APFD (“Average Percentage of Faults Detected”) and PTR (“Percentage of Test Suite Required for Complete Fault Coverage”) have been used to measure the effectiveness of the proposed “m-ACO” technique. Findings: The proposed technique “m-ACO” produced optimal or near optimal solutions. The proposed “m-ACO” technique proves its efficiency in comparison to GA, BCO and ACO methods individually. Improvements: The proposed technique improves the ACO method by altering food source selection criteria of natural ants. The future work in this direction will comparatively evaluate the proposed “m-ACO” technique using some well known software testing problems and open source software. An automated tool for the proposed technique is being developed.

Keywords

Fault Coverage, Genetic Algorithm, Regression Testing, Software Testing, Test Suite Prioritization.

Full Text:

 |  (PDF views: 291)

References


  • Onoma K, Tsai WT, Poonawala M, Suganuma H. Regression testing in an industrial environment. Communications of the ACM. 1998 May; 41(5):81–6.
  • Beizer B. Software testing techniques. 2nd ed. India: Dreamtech Press; 2003.
  • Leung H, White L. Insights into regression testing. Proceedings of the IEEE International Conference on Software Maintenance; 1989 Oct. p. 60–9.
  • Solanki K, Singh Y, Dalal S. Test case prioritization: An approach based on modified ant colony optimization. Proceedings of IEEE International Conference on Computer, Communication and Control; Indore, India. 2015 Sep. Available at IEEE-xplore Digital Library.
  • Rothermel GU, Chu C, Harrold MJ. Test case prioritization: An empirical study. Proceedings of the International Conference on Software Maintenance; Oxford, UK. 1999. p. 179–88.
  • Li Z, Harman M, Hierons RM. Search algorithms for regression test case prioritization. IEEE Transactions on Software Engineering. 2007; 33(4):225–37.
  • Salami AL. Evolutionary algorithm definition. American Journal of Engineering and Applied Science. 2009; 2(4):789–95.
  • Byson N. A goal programming method for generating priorities vectors. Journal of Operational Research, England. 1995; 46(5):641–8.
  • Crawford G, Williams C. A note on the analysis of subjective judgment matrices. Journal of Mathematical Psychology. Elsevier Publications. 1985; 29(4):387–405.
  • Singh Y, Kaur A, Suri B. Regression test selection and prioritization using variables: Analysis and experimentation. Software Quality Professional Magazine. 2009 Mar; 11(2):1–15.
  • Dorigo M, Maniezzo V, Colorni A. The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics. 1996; 26(1):29–41.
  • Suri B, Singhal S. Implementing ant colony optimization for test case selection and prioritization. International Journal of Computer Science and Engineering. 2011 May; 3(5):1924–32.
  • Srivastava PR, Baby K. Automated software testing using meta-heuristic technique based on an ant colony optimization. International Symposium on Electronic System Design (ISED); Bhubaneshwar, India. 2010 Dec. p. 235–40.
  • Singh Y, Kaur A, Suri B, Singhal S. Test case prioritization using ant colony optimization. ACM SIGSOFT Software Engineering Notes. 2012; 35(4):1–7.
  • Chandu PMSS, Sasikala T. Implementation of regression testing of test case prioritization. Indian Journal of Science and Technology. 2015 Apr; 8(S8):290–3. DOI: 10.17485/ijst/2015/v8iS8/61922.
  • Elbaum S, Malishevsky A, Rothermel G. Test case prioritization: A family of empirical studies. IEEE Transactions on Software Engineering. 2002; 28(2):159–82.
  • Elbaum S, Rothermel G, Kanduri S, Malishevsky AG. Selecting a cost-effective test case prioritization technique. Software Quality Journal. 2004; 12(3):185–210.
  • Malishevsky AG, Ruthruff JR, Rothermel G, Elbaum S. Cost Cognizant Test Case Prioritization, Technical Report. University of Nabraska Lincoln, 2006.
  • Srivastava PR. Test case prioritization. Journal of Theoretical and Applied Information Technology. 2008; 4(3):178–81.
  • Raju S, Uma GV. Factors oriented test case prioritization technique in regression testing using genetic algorithm. European Journal of Scientific Research. 2012; 74(3):389–402.
  • Berndt DJ, Watkins A. Investigating the performance of genetic algorithm based software test case generation. Proceedings of IEEE International Symposium on High Assurance Systems Engineering; 2004. p. 261–2.
  • Xanthakis S, Ellis C, Gall AL, Karapoulios K. Application of genetic algorithms to software testing. Proceedings of International Conference on Software Engineering and its Applications; 1992. p. 625–36.
  • Maheswari RU, Mala DJ. Combined genetic and simulated annealing approach for test case prioritization. Indian Journal of Science and Technology. 2015 Dec; 8(35):1–5. DOI: 10.17485/ijst/2015/v8i35/81102.
  • Kaur A, Goyal S. A bee colony optimization algorithm for code coverage test suite prioritization. International Journal of Engineering Science and Technology. 2011; 3(4):2786–795.
  • Jeyamala D, Mohan V. ABC-artificial bee colony optimization based test suite optimization technique. International Journal of Software Engineering. 2009; 2(2):1–33.
  • McCaffrey JD. Generation of pair-wise test sets using a simulated bee colony algorithm. Proceedings of IEEE International Conference on Information Reuse and Integration; 2009 Aug. p. 115–9.
  • Maheshwari V, Prasanna M. Generation of test case using automation in software systems: A review. Indian Journal of Science and Technology. 2015 Dec; 8(35):1–9. DOI: 10.17485/ijst/2015/v8i35/72881.
  • Jacob TP, Ravi. An optimal technique for reducing the effort of regression test. Indian Journal of Science and Technology. 2013 Aug; 6(8):5065–9. DOI:10.17485/ijst/2013/v6i8/36345.

Refbacks

  • There are currently no refbacks.


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