Test Case Minimization in COTS Methodology Using Genetic Algorithm: A Modified Approach

  • ReenaEmail author
  • Pradeep Kumar Bhatia
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 605)


In software testing, many times redundant test cases are used for a small piece of code. Testing is a tedious task and it requires more effort and time. Mostly numbers of defects are not uniformly distributed in COTS and defect that does not occur frequently requires more effort to remove. So, test case minimization techniques with proper test plan are used. In this paper, we propose a model for test case minimization in Component–Based system. In the propose model, a soft computing technique, Genetic algorithm is added into class partitioning (Boundary Value Analysis and Partitioning Testing) to optimize fitness values in test suit generation. To improve the performance of genetic algorithm, we also added fitness scaling in proposed algorithm. We believe the model we have developed is an important step towards easing the process of testing COTS components.


COTS Component CBSD Fitness scaling Genetic algorithm Software testing Test case 


  1. 1.
    Allahaim, F.S., Liu, L.: Causes of cost overruns on infrastructure projects in Saudi Arabia. J. Coll. Ent. 5, 32–57 (2015)Google Scholar
  2. 2.
    Crnkovic, I.: Component-based software engineering: new challenges in software development. Softw. Focus 2, 127–133 (2001)CrossRefGoogle Scholar
  3. 3.
    Abts, C.: COTS-based systems (CBS) functional density-a heuristic for better CBS design. In: International Conference on COTS-Based Software Systems, pp. 1–9. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  4. 4.
    Wang, J.A.: Towards component-based software engineering. J. Com. Sci. 16, 177–189 (2000)Google Scholar
  5. 5.
    Haddox, J.M., Kapfhammer, G.M.: An approach for understanding and testing third party software component. In: Reliability and Maintainability Symposium, pp. 293–299 (2002)Google Scholar
  6. 6.
    Alsmadi, I.: Using genetic algorithms for test case generation and selection optimization. In: 23rd Canadian Conference Electrical and Computer Engineering (CCECE), pp. 1–4, IEEE (2010)Google Scholar
  7. 7.
    Ngamtawee, R., Wardkein, P.: Simplified genetic algorithm & 58; simplify and improve RGA for parameter optimizations. Adv. Elec. Comp. Eng. 14, 55–64 (2014)CrossRefGoogle Scholar
  8. 8.
    Mendes, W.R., Pereira, F.G., Cavalieri, D.C.: A hybrid model based on genetic algorithm and space-filling curve applied to optimization of vehicle routes. Adv. Elec. Comp. Eng. 18, 45–52 (2018)CrossRefGoogle Scholar
  9. 9.
    Popentiu, V., Florin, G.A.: Nature-inspired approaches in software faults identification and debugging. Proc. Comput. Sci. 92, 6–12 (2016)CrossRefGoogle Scholar
  10. 10.
    Srisura, B., Lawanna, A.: False test case selection: improvement of regression testing approach. In: 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 1–6. IEEE (2016)Google Scholar
  11. 11.
    Yang, W., Mukul, R.P., Tao X.: A grey-box approach for automated GUI-model generation of mobile applications. In: International Conference on Fundamental Approaches to Software Engineering, pp. 250–265. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Linzhang, W., Yuan, J., Yu, X., Hu, J., Li, X., Zheng, G.: Generating test cases from UML activity diagram based on gray-box method. In: 11th Asia-Pacific Software Engineering Conference, pp. 284–291. IEEE (2004)Google Scholar
  13. 13.
    Bartholomew, R., Rockwell, C.: Using combinatorial testing to reduce software rework. CrossTalk 23, 23–26 (2014)Google Scholar
  14. 14.
    Nidagundi, P., Leonids N.: Towards utilization of a lean canvas in the biometric software testing. IIOAB J. Inst. Integr. Omics Appl. Biotechnol. (2017)Google Scholar
  15. 15.
    Kabir, M.N., Ali, J., Alsewari, A.A., Zamli, K.Z.: An adaptive flower pollination algorithm for software test suite minimization. In: 3rd International Conference on Electrical Information and Communication Technology (EICT), IEEE, pp. 1–5 (2017)Google Scholar
  16. 16.
    Bright, K., Vikash, Y.: Automatic test case generation for performance enhancement of software through genetic algorithm and random testing. J. Eng. Sci. Res. Sci. 7, 186–191 (2018)Google Scholar
  17. 17.
    Mohapatra, S.K., Prasad, S.: Using chemical reaction optimisation for test case minimisation problem. J. Soft. Eng. Tech. App. 2(1), 22–40 (2017)Google Scholar
  18. 18.
    Ali, S., Li, Y., Yue, T., Zhang, M.: An empirical evaluation of mutation and crossover operators for multi-objective uncertainty-wise test minimization. In: 10th International Workshop, IEEE, pp. 21–27 (2017)Google Scholar
  19. 19.
    Mohapatra, S.K., Prasad, S.: Using chemical reaction optimisation for test case minimisation problem. J. Soft. Eng. Tech. App. 2, 22–40 (2017)Google Scholar
  20. 20.
    Subashini, B., Jeyamala, D.: Test suite reduction based on traceability matrix with association rule mining technique. J. Inf. Syst. Change Manag. 9, 205–237 (2017)Google Scholar
  21. 21.
    Ahmed, B.S.: Test case minimization approach using fault detection and combinatorial optimization techniques for configuration-aware structural testing. J. Eng. Sci. Tech. 19, 737–753 (2016)Google Scholar
  22. 22.
    Srividhya, J., Gunasundari, R.: Test suite minimization and empirical analysis of optimization algorithms. J. Theor. Appl. Inf. Tech. 94, 159–166 (2016)Google Scholar
  23. 23.
    Pessemier, N., Seinturier, L., Duchien, L., Coupaye, T.: A component-based and aspect-oriented model for software evolution. J. Comput. Appl. Technol. 3, 94–105 (2008)CrossRefGoogle Scholar
  24. 24.
    Vijayalakshmi, K., Ramaraj, N., Amuthakkannan, R., Kannan, S.M.: A new algorithm in assembly for component-based software using dependency chart. J. Inf. Syst. Change Manag. 2, 261–278 (2007)Google Scholar
  25. 25.
    Burton, B.A., Aragon, R.W., Bailey, S.A., Koehler, K.D., Mayes, L.A.: The reusable software library. In: IEEE, pp. 25 (1987)Google Scholar
  26. 26.
    Omara, F.A., Arafa, M.M.: Genetic algorithms for task scheduling problem. In: Foundations of Computational Intelligence, vol. 3, pp. 479–507. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  27. 27.
    Srivastava, P.R., Kim, T.H.: Application of genetic algorithm in software testing. J. Soft. Eng. Appl. 3, 87–96 (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.GJU & STHisarIndia
  2. 2.HisarIndia

Personalised recommendations