Skip to main content

Soft Computing Based Software Testing – A Concise Travelogue

  • Conference paper
  • First Online:
Proceedings of Sixth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 547))

Abstract

Soft computing is an accumulation of procedures, which intend to adventure resistance for the defect, deception, ambiguity and incomplete truth to accomplish tractability, strength, and low arrangement cost. In this paper, a comprehensive overview of software testing based on soft computing is presented. In this survey, we try to elaborate some problems of software engineering specifically software testing and their solutions, which are based on soft computing approaches. The paper presents an overview of the usage of soft computing techniques including Neural Networks, Fuzzy Logic, Ant Colony Optimization, and Particle Swarm Optimization and Genetic algorithm in software testing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Commun. ACM 37(3), 77–85 (1994)

    Article  Google Scholar 

  2. Yang, X.S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M. (eds.): Swarm Intelligence and Bio-Inspired Computation: Theory and Applications. Newnes, Oxford (2013)

    Google Scholar 

  3. Chaturvedi, D.K.: Soft Computing: Techniques and its Applications in Electrical Engineering. SCI, vol. 103. Springer, Heidelberg (2008)

    Google Scholar 

  4. Binder, R.V.: Testing Object-Oriented Systems: Objects, Patterns, and Tools (1999)

    Google Scholar 

  5. Beizer, B.: Software Testing Techniques (1990)

    Google Scholar 

  6. Clapp, J.A.: Software Quality Control, Error Analysis, and Testing. William Andrew (1995)

    Google Scholar 

  7. Harman, M., Mansouri, S.A., Zhang, Y.: Search-based software engineering: Trends, techniques and applications. ACM Comput. Surv. (CSUR) 45(1), 11 (2012)

    Article  Google Scholar 

  8. Ghiduk, A.S.: Automatic generation of basis test paths using variable length genetic algorithm. Inf. Process. Lett. 114(6), 304–316 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Ferrer, J., Kruse, P.M., Chicano, F., Alba, E.: Search based algorithms for test sequence generation in functional testing. Inf. Softw. Technol. 58, 419–432 (2015)

    Article  Google Scholar 

  10. Khurana, N., Chillar, R.S.: Test Case Generation and Optimization using UML Models and Genetic Algorithm. Procedia Comput. Sci. 57, 996–1004 (2015)

    Article  Google Scholar 

  11. Varshney, S., Mehrotra, M.: Search based software test data generation for structural testing: a perspective. ACM SIGSOFT Softw. Eng. Not. 38(4), 1–6 (2013)

    Article  Google Scholar 

  12. Fausett, L.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall Inc., Upper Saddle River (1994)

    MATH  Google Scholar 

  13. Haykin, S.: Neural Network, A Comprehensive Foundation. Prentice Hall India, New Delhi (2003)

    MATH  Google Scholar 

  14. Aggarwal, K.K., Singh, Y., Kaur, A., Malhotra, R.: Application of artificial neural network for predicting maintainability using object-oriented metrics’. Trans. Eng. Comput. Technol. 15, 285–289 (2006)

    Google Scholar 

  15. Aggarwal, K.K., Singh, Y., Kaur, A., Sangwan, O.P.: A neural net based approach to test oracle. ACM SIGSOFT Softw. Eng. Not. 29(3), 1–6 (2004)

    Article  Google Scholar 

  16. Singh, Y., Bhatia, P.K., Kaur, A., Sangwan, O.: Application of neural networks in software engineering: a review. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds.) ICISTM 2009. CCIS, vol. 31, pp. 128–137. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00405-6_17

    Chapter  Google Scholar 

  17. Singh, Y., Bhatia, P.K., Sangwan, O.: ANN model for predicting software function point metric. ACM SIGSOFT Softw. Eng. Not. 34(1), 1–4 (2009)

    Google Scholar 

  18. Pedemonte, M., Nesmachnow, S., Cancela, H.: A survey on parallel ant colony optimization. Appl. Soft Comput. 11(8), 5181–5197 (2011)

    Article  Google Scholar 

  19. Mao, C., Xiao, L., Yu, X., Chen, J.: Adapting ant colony optimization to generate test data for software structural testing. Swarm Evol. Comput. 20, 23–36 (2015)

    Article  Google Scholar 

  20. Schumann, J., Nelson, S.: Toward V&V of neural network based controllers. In: Proceedings of the First Workshop on Self-Healing Systems, pp. 67–72 ACM (2002)

    Google Scholar 

  21. Aggarwal, K.K., Singh, Y., Chandra, P., Puri, M.: Evaluation of various training algorithms in a neural network model for software engineering applications. ACM SIGSOFT Softw. Eng. Not. 30(4), 1–4 (2005)

    Google Scholar 

  22. Gökçe, N., Eminov, M., Belli, F.: Coverage-based, prioritized testing using neural network clustering. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 1060–1071. Springer, Heidelberg (2006). doi:10.1007/11902140_110

    Chapter  Google Scholar 

  23. Engel, A., Last, M.: Modeling software testing costs and risks using fuzzy logic paradigm. J. Syst. Softw. 80(6), 817–835 (2007)

    Article  Google Scholar 

  24. Lokasyuk, V.M., Pomorova, O.V., Govorushchenko, T.O.: Neural nets method for estimation of the software retesting necessity. In: Proceedings of the 2008 International Workshop on Software Engineering in East and South Europe, pp. 9–14. ACM (2008)

    Google Scholar 

  25. Tsai, K.H., Wang, T.I., Hsieh, T.C., Chiu, T.K., Lee, M.C.: Dynamic computerized testlet-based test generation system by discrete PSO with partial course ontology. Expert Syst. Appl. 37(1), 774–786 (2010)

    Article  Google Scholar 

  26. Kumar, P., Singh, Y.: Assessment of software testing time using soft computing techniques. ACM SIGSOFT Softw. Eng. Not. 37(1), 1–6 (2012)

    Article  Google Scholar 

  27. Pizzi, N.J.: A fuzzy classifier approach to estimating software quality. Inf. Sci. 241, 1–11 (2013)

    Article  Google Scholar 

  28. Tyagi, K., Sharma, A.: An adaptive neuro fuzzy model for estimating the reliability of component-based software systems. Appl. Comput. Inf. 10(1), 38–51 (2014)

    Google Scholar 

  29. Bhasin, H., Khanna, E.: Neural network based black box testing. ACM SIGSOFT Softw. Eng. Not. 39(2), 1–6 (2014)

    Article  Google Scholar 

  30. Wang, J., Lin, Y.I.: A fuzzy multicriteria group decision making approach to select configuration items for software development. Fuzzy Sets Syst. 134(3), 343–363 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  31. Fenton, N.E., Ohlsson, N.: Quantitative analysis of faults and failures in a complex software system. IEEE Trans. Softw. Eng. 26(8), 797–814 (2000)

    Article  Google Scholar 

  32. Ahmed, B.S., Sahib, M.A., Potrus, M.Y.: Generating combinatorial test cases using Simplified Swarm Optimization (SSO) algorithm for automated GUI functional testing. Eng. Sci. Technol. Int. J. 17(4), 218–226 (2014)

    Article  Google Scholar 

  33. Mahmoud, T., Ahmed, B.S.: An efficient strategy for covering array construction with fuzzy logic-based adaptive swarm optimization for software testing use. Expert Syst. Appl. 42(22), 8753–8765 (2015)

    Article  Google Scholar 

  34. Darwish, S. M.: Software test quality rating: A paradigm shift in swarm computing for software certification. Knowl.-Based Systems (2016)

    Google Scholar 

  35. Masri, W., Zaraket, F.A.: Coverage-Based Software Testing: Beyond Basic Test Requirements. Advances in Computers (2016)

    Google Scholar 

  36. Yang, S., Man, T., Xu, J., Zeng, F., Li, K.: RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation. Inf. Softw. Technol. 76, 19–30 (2016)

    Article  Google Scholar 

  37. Siddiqui, T., & Ahmad, R.: A review on software testing approaches for cloud applications. Perspect. Sci. 8, 689–691 (2016)

    Google Scholar 

  38. Singh, Y., Bhatia, P.K., Sangwan, O.: Software reusability assessment using soft computing techniques. ACM SIGSOFT Softw. Eng. Not. 36(1), 1–7 (2011)

    Article  Google Scholar 

  39. Srivastava, P.R., Kim, T.H.: Application of genetic algorithm in software testing. Int. J. Softw. Eng. Appl. 3(4), 87–96 (2009)

    Google Scholar 

  40. Saglietti, F., Oster, N., Pinte, F.: White and grey-box verification and validation approaches for safety-and security-critical software systems. Inf. Sec. Tech. Rep. 13(1), 10–16 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deepak Sharma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Sharma, D., Chandra, P. (2017). Soft Computing Based Software Testing – A Concise Travelogue. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-10-3325-4_22

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3325-4_22

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3324-7

  • Online ISBN: 978-981-10-3325-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics