Multi-objective Optimization Based Software Testing Using Kansei Quality Approach

  • Shilpa
  • Kavita Choudhary
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)


Software testing suggests the quality of the software product. More the effective testing means high quality software product. In this paper we have identified and prioritized the parameters according to the perspective of different entities involved in software lifecycle. The prime objective of the paper is to perform software testing from the perspective of Kansei Engineering methodology with multi-objective optimization.


Kansei approach Multi-objective optimization Fault tolerance Accuracy Code coverage 


  1. 1.
    Guowei, H.: Software reliability-growth test and the software reliability-testing platform. In: Proceedings of Annual Reliability and Maintainability Symposium. ©IEEE (1997)Google Scholar
  2. 2.
    Sun, H.T.: Optimize defect detection techniques through empirical software engineering method. In: IEEE International Conference on Electro Information Technology. ©IEEE (2005)Google Scholar
  3. 3.
    Pasquini A.: Sensitivity of reliability-growth models to operational profile errors vs. testing accuracy. IEEE Trans. Reliab. ©IEEE (1996)Google Scholar
  4. 4.
    Lyu, M.R.: Optimal allocation of test resources for software reliability growth modeling in software development. IEEE Trans. Reliab. ©IEEE (2002)Google Scholar
  5. 5.
    Cai, X.: Software reliability modeling with test coverage: experimentation and measurement with a fault-tolerant software project. In: The 18th IEEE International Symposium on Software Reliability. ISSRE ‘07. ©IEEE (2007)Google Scholar
  6. 6.
    Sandhu, P.S.: A model for early prediction of faults in software systems. In: 2nd International Conference on Computer and Automation Engineering (ICCAE). ©IEEE (2010)Google Scholar
  7. 7.
    Turmon, M.: Tests and tolerances for high-performance software-implemented fault detection. IEEE Trans. Comput. ©IEEE (2003)Google Scholar
  8. 8.
    Park, H.: Historical value-based approach for cost-cognizant test case prioritization to improve the effectiveness of regression testing. In: Second International Conference on Secure System Integration and Reliability Improvement, SSIRI ‘08. ©IEEE (2008)Google Scholar
  9. 9.
    Zhou, Z.Q., Sinaga, A., Zhao, L., Susilo, W., Cai, K.Y.: Improving software testing cost-effectiveness through dynamic partitioning. In: 9th International Conference on Quality Software. ©IEEE (2009)Google Scholar
  10. 10.
    Nakagawa, E.Y., Maldonado, J.C.: Contributions and perspectives in architectures of software testing environments. In: 25th Brazilian Symposium on Software Engineering (SBES). © IEEE (2011)Google Scholar
  11. 11.
    Oyetoyan, T.D., Cruzes, D.S., Conradi, R.: Transition and defect patterns of components in dependency cycles during software evolution. In: IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE), Software Evolution Week. ©IEEE (2014)Google Scholar
  12. 12.
    Choudhary, K., Purohit, G.N.: A multi-objective optimization algorithm for uniformly distributed generation of test cases. In: International Conference on Computing for Sustainable Global Development (INDIACom). ©IEEE (2014)Google Scholar
  13. 13.
    Indumathi, C.P., Galeebathullah, B., Pandithurai, O.: Analysis of test case coverage using data mining technique. In: IEEE International Conference on Communication Control and Computing Technologies (ICCCCT). ©IEEE (2010)Google Scholar
  14. 14.
    Sarwar, T. Habib, W. Arif, F.,”Requirements based testing of software”, Second International Conference on Informatics and Applications (ICIA). ©IEEE (2013)Google Scholar
  15. 15.
    Birt, J.R., Sitte, R.: Optimizing testing efficiency with error-prone path identification and genetic algorithms. In: Proceedings on Software Engineering Conference. ©IEEE (2004)Google Scholar
  16. 16.
    Lokman, A.M.: Design & emotion: the kansei engineering methodology, vol. 1(1). ISSN 2231-7473 ©2010. Faculty of Computer and Mathematical Sciences, Universiti Teknolgi MARA, (UiTM), Malaysia (2010)Google Scholar
  17. 17.
    Bouchard, C., Lim, D., Aoussat, A.: Development of a Kansei engineering system for industrial. design.
  18. 18.
    Lokman, A.M., Haron, M.B.C., Abidin, S.Z.Z., Khalid, N.E.A., Ishihara, S.: Prelude to Natphoric Kansei engineering framework.
  19. 19.
    Lanzotti, A., Tarantino, P.: Kansei engineering approach for total quality design and continuous innovation.
  20. 20.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.ITM UniversityGurgoanIndia

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