A Review of Test Case Prioritization and Optimization Techniques

  • Pavi Saraswat
  • Abhishek Singhal
  • Abhay Bansal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 731)


Software testing is a very important and crucial phase of software development life cycle. In order to develop good quality software, the effectiveness of the software has been tested. Test cases and test suites are prepared for testing, and it should be done in minimum time for which test case prioritization and optimization techniques are required. The main aim of test case prioritization is to test software in minimum time and with maximum efficiency, so for this there are many techniques, and to develop a new or better technique, existing techniques should be known. This paper presents a review on the techniques of test case prioritization and optimization. This paper also provides analysis of the literature available for the same.


Software testing Regression testing Test case prioritization Test case optimization 


  1. 1.
    Qu, B., Nie, C., Xu, B.: Test case prioritization for multiple processing queues. In: ISISE’08 International Symposium on Information Science and Engineering, vol. 2, pp. 646–649. IEEE (2008)Google Scholar
  2. 2.
    Hla, K.H.S., Choi, Y. Park, J.S. Applying particle swarm optimization to prioritizing test cases for embedded real time software retesting. In: 8th International Conference on Computer and Information Technology Workshops, pp. 527–532. IEEE (2008)Google Scholar
  3. 3.
    Tyagi, M., Malhotra, S.: Test case prioritization using multi objective particle swarm optimizer. In: International Conference on Signal Propagation and Computer Technology (ICSPCT), pp. 390–395. IEEE (2014)Google Scholar
  4. 4.
    Simons, C., Paraiso, E.C.: Regression test cases prioritization using failure pursuit sampling. In: 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 923–928. IEEE (2010)Google Scholar
  5. 5.
    Nagar, R., Kumar, A., Kumar, S., Baghel, A.S.: Implementing test case selection and reduction techniques using meta-heuristics. In: Confluence The Next Generation Information Technology Summit (Confluence), 2014 5th International Conference, pp. 837–842. IEEE (2014)Google Scholar
  6. 6.
    Ansari, A.S., Devadkar, K.K., Gharpure, P.: Optimization of test suite-test case in regression test. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–4. IEEE (2013)Google Scholar
  7. 7.
    Elanthiraiyan, N., Arumugam, C.: Parallelized ACO algorithm for regression testing prioritization in hadoop framework. In: International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 1568–1571. IEEE (2014)Google Scholar
  8. 8.
    Sharma, N., Purohit, G.N.: Test case prioritization techniques-an empirical study. In: International Conference on High Performance Computing and Applications (ICHPCA), vol. 28(2), pp. 159–182. IEEE (2014)Google Scholar
  9. 9.
    Kruse, P.M., Schieferdecker, I. Comparison of approaches to prioritized test generation for combinatorial interaction testing. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1357–1364. IEEE (2012)Google Scholar
  10. 10.
    Stochel, M.G., Sztando, R.: Testing optimization for mission-critical, complex, distributed systems. In: 32nd Annual IEEE International Conference on Computer Software and Applications, 2008. COMPSAC’08, pp. 847–852. IEEE (2008)Google Scholar
  11. 11.
    Islam, M.M., Scanniello, G.: MOTCP: a tool for the prioritization of test cases based on a sorting genetic algorithm and latent semantic indexing. In: 28th IEEE International Conference on Software Maintenance (ICSM), pp. 654–657. IEEE (2012)Google Scholar
  12. 12.
    Sabharwal, S., Sibal, R., Sharma, C.: A genetic algorithm based approach for prioritization of test case scenarios in static testing. In: 2nd International Conference on Computer and Communication Technology (ICCCT), pp. 304–309. IEEE (2011)Google Scholar
  13. 13.
    Khan, S.U.R., Parizi, R.M., Elahi, M.: A code coverage-based test suite reduction and prioritization framework.In: Fourth World Congress on Information and Communication Technologies (WICT), pp. 229–234. IEEE (2014)Google Scholar
  14. 14.
    Harman, M.: Making the case for MORTO: multi objective regression test optimization. In: Fourth International Conference on Software Testing, Verification and Validation Workshops, pp. 111–114. IEEE (2011)Google Scholar
  15. 15.
    Noguchi, T., Sato, A.: History-based test case prioritization for black box testing using ant colony optimization. In: IEEE 8th International Conference on Software Testing, Verification and Validation (ICST), pp. 1–2. IEEE (2015)Google Scholar
  16. 16.
    Ma, Z., Zhao, J.: Test case prioritization based on analysis of program structure. In: Software Engineering Conference, 2008. APSEC’08. 15th Asia-Pacific, pp. 471–478. IEEE (2008)Google Scholar
  17. 17.
    Wu, K., Fang, C., Chen, Z., Zhao, Z.: Test case prioritization incorporating ordered sequence of program elements. In: Proceedings of the 7th International Workshop on Automation of Software Test, pp. 124–130. IEEE Press (2012)Google Scholar
  18. 18.
    Prakash, N., Rangaswamy, T.R.: Modular based multiple test case prioritization. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1–7. IEEE (2012)Google Scholar
  19. 19.
    Rugthaicharoencheep, N., Thongkeaw, S., Auchariyamet, S.: Economic load dispatch with daily load patterns using particle swarm optimization. In: Proceedings of 46th International Universities Power Engineering Conference (UPEC), pp. 1–5. VDE (2011)Google Scholar
  20. 20.
    Chauhan, N., Kumar, H.: A hierarchical test case prioritization technique for object oriented software. In: International Conference on Contemporary Computing and Informatics (IC3I), pp. 249–254. IEEE (2014)Google Scholar
  21. 21.
    Baudry, B., Fleurey, F., Jezequel, J.M., Le Traon, Y.: Automatic test case optimization using a bacteriological adaptation model: application to. net components. In: Proceedings of the ASE 2002. 17th IEEE International Conference on Automated Software Engineering, pp. 253–256. IEEE (2002)Google Scholar
  22. 22.
    Malhotra, R., Tiwari, D.: Development of a framework for test case prioritization using genetic algorithm. ACM SIGSOFT Softw. Eng. Notes 38(3), 1–6 (2013)CrossRefGoogle Scholar
  23. 23.
    Mayan, J.A., Ravi, T.: Test case optimization using hybrid search technique. In: Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing. ACM (2014)Google Scholar
  24. 24.
    Arcuri, A., Briand, L.: Adaptive random testing: an illusion of effectiveness? In: Proceedings of the 2011 International Symposium on Software Testing and Analysis, pp. 265–275. ACM (2011)Google Scholar
  25. 25.
    Pastore, F., Mariani, L., Hyvärinen, A.E.J., Fedyukovich, G., Sharygina, N., Sehestedt, S., Muhammad, A.: Verification-aided regression testing. In: Proceedings of the 2014 International Symposium on Software Testing and Analysis, pp. 37–48. ACM (2014)Google Scholar
  26. 26.
    Gligoric, M., Negara, S. Legunsen, O., Marinov, D.: An empirical evaluation and comparison of manual and automated test selection. In: Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering, pp. 361–372. ACM (2014)Google Scholar
  27. 27.
    Baudry, B., Fleurey, F., Jézéquel, J.M., Le Traon, Y.: Automatic test case optimization: a bacteriologic algorithm. IEEE Softw. 22(2), 76–82 (2005)CrossRefGoogle Scholar
  28. 28.
    Baudry, B., Fleurey, F., Jézéquel, J.M., Le Traon, Y.: Genes and bacteria for automatic test cases optimization in the. net environment. In: Proceedings of the 13th International Symposium on Software Reliability Engineering, ISSR, pp. 195–206. IEEE (2002)Google Scholar
  29. 29.
    Liu, W., Dasiewicz, P.: The event-flow technique for selecting test cases for object-oriented programs. In: Canadian Conference on Engineering Innovation: Voyage of Discovery, vol. 1, pp. 257–260. IEEE (1997)Google Scholar
  30. 30.
    Hoseini, B., Jalili, S.: Automatic test path generation from sequence diagram using genetic algorithm. In: 7th International Symposium on Telecommunications (IST), pp. 106–111. IEEE (2014)Google Scholar
  31. 31.
    Mahajan, S., Joshi, S.D., Khanaa, V.: Component-based software system test case prioritization with genetic algorithm decoding technique using java platform. In: International Conference on Computing Communication Control and Automation, pp. 847–851. IEEE (2015)Google Scholar
  32. 32.
    Panichella, A., Oliveto, R., Di Penta, M., De Lucia, A.: Improving multi-objective test case selection by injecting diversity in genetic algorithms. IEEE Trans. Softw. Eng. 41(4), 358–383 (2015)CrossRefGoogle Scholar
  33. 33.
    Valdez, F., Melin, P., Mendoza, O.: A new evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms: the case of neural networks optimization. In: International Joint Conference on Neural Networks, IJCNN, (IEEE World Congress on Computational Intelligence), pp. 1536–1543. IEEE (2008)Google Scholar
  34. 34.
    Karnavel, K., Santhosh Kumar, J.: Automated software testing for application maintenance by using bee colony optimization algorithms (BCO). In: International Conference on Information Communication and Embedded Systems (ICICES), pp. 327–330. IEEE (2013)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSE, ASETAmity UniversityNoidaIndia

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