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Adopting Social Group Optimization Algorithm Using Mutation Testing for Test Suite Generation: SGO-MT

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Book cover Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11622))

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Abstract

Test case generation is popular among the researchers and doing this manually is an exhaustive and time taking process. Automation can cut its cost and create an effective test suite that is evaluated for its adequacy over a set of faults. These faults can be created by applying mutagenic rules that have been used appropriately for searching the improved test inputs in search-based approaches. Researchers have advised these approaches combining mutation testing are more effective at test generation. This paper proposes a novel test generation algorithm SGO-MT by adopting social group optimization algorithm (SGO) with the goal to reveal maximum faults in the software. SGO follows the concept of learning the traits of humans in a group. It works in two phases: acquiring phase (learning from society) and improving phase (learning from the teacher) that try to enhance the fitness of each individual. In learning from society, each individual test case is influenced by another while in the latter case, test data are evolved with respect to the fittest test case. SGO-MT stops functioning when it achieves its desired objective ie. detection of maximum possible artificial faults.

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References

  1. Myers, G.J.: The Art of Software Testing. Wiley, New York (1989)

    MATH  Google Scholar 

  2. Agarwal, K.K., Singh, Y.: Software Engineering. New Age International Publishers (2007)

    Google Scholar 

  3. Jia, Y., Harman, M.: An analysis and survey of the development of mutation testing. IEEE Trans. Softw. Eng. 37(5), 649–678 (2011)

    Article  Google Scholar 

  4. Zhu, Q., Panichella, A., Zaidman, A.: A systematic literature review on how mutation testing supports quality assurance processes. Softw. Test. Verif. Reliab. 28(6), 1675 (2018)

    Article  Google Scholar 

  5. Andrews, J.H., Briand, L.C., Labiche, Y.: Is mutation an appropriate tool for testing experiments? In: Proceedings of the 27th International Conference on Software Engineering, ICSE 2005, pp. 402–411. ACM (2005)

    Google Scholar 

  6. Dave, M., Agrawal, R.: Search based techniques and mutation analysis in automatic test case generation: a survey. In: 2015 IEEE International Advance Computing Conference (IACC), pp. 795–799 (2015)

    Google Scholar 

  7. McMinn, P.: Search-based software test data generation: a survey: research articles. Softw. Test. Verif. Reliab. 14(2), 105–156 (2004)

    Article  Google Scholar 

  8. McMinn, P.: Search-based software testing: past, present and future. In: Proceedings of the 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2011, pp. 153–163. IEEE Computer Society (2011)

    Google Scholar 

  9. Sahin, O., Akay, B.: Comparisons of metaheuristic algorithms and fitness functions on software test data generation. Appl. Soft Comput. 49, 1202–1214 (2016)

    Article  Google Scholar 

  10. Ali, S., Briand, L.C., Hemmati, H., Panesar-Walawege, R.K.: A systematic review of the application and empirical investigation of search-based test case generation. IEEE Trans. Softw. Eng. 36(6), 742–762 (2010)

    Article  Google Scholar 

  11. Miller, W., Spooner, D.L.: Automatic generation of floating-point test data. IEEE Trans. Softw. Eng. SE–2(3), 223–226 (1976)

    Article  MathSciNet  Google Scholar 

  12. Fraser, G., Zeller, A.: Mutation-driven generation of unit tests and oracles. IEEE Trans. Softw. Eng. 38(2), 278–292 (2012)

    Article  Google Scholar 

  13. Fraser, G., Arcuri, A.: Achieving scalable mutation-based generation of whole test suites. Empir. Softw. Eng. 20(3), 783–812 (2015)

    Article  Google Scholar 

  14. Mirjalili, S.: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 89, 228–249 (2015)

    Article  Google Scholar 

  15. Yazdani, M., Jolai, F.: Lion Optimization Algorithm (LOA): a nature-inspired metaheuristic algorithm. J. Comput. Des. Eng. 3, 24–36 (2016)

    Google Scholar 

  16. Mirjalili, S., Lewis, A.: The Whale Optimization Algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  17. Bansal, J.C., Sharma, H., Jadon, S.S., Clerc, M.: Spider Monkey Optimization algorithm for numerical optimization. Memetic Comput. 6(1), 31–47 (2013)

    Article  Google Scholar 

  18. Shah-Hosseini, H.: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int. J. Bio-Inspired Comput. 1(1), 71–79 (2009)

    Article  Google Scholar 

  19. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  20. Mirjalili, S.: The Ant Lion Optimizer. Adv. Eng. Softw. 83, 80–98 (2015)

    Article  Google Scholar 

  21. Satapathy, S., Naik, A.: Social group optimization (SGO): a new population evolutionary optimization technique. Complex Intell. Syst. 2, 173–203 (2016)

    Article  Google Scholar 

  22. Silva, R.A., de Souza, S.D.R.S., de Souza, P.S.L.: A systematic review on search based mutation testing. Inf. Softw. Technol. 81, 19–35 (2017)

    Article  Google Scholar 

  23. Jatana, N., Suri, B., Rani, S.: Systematic literature review on search based mutation testing. e-Inform. Softw. Eng. J. 11(1), 59–76 (2017)

    Google Scholar 

  24. Rodrigues, D.S., Delamaro, M.E., Correa, C.G., Nunes, F.L.S.: Using genetic algorithms in test data generation: a critical systematic mapping. ACM Comput. Surv. 51(2), (41)1–(41)23 (2018)

    Article  Google Scholar 

  25. Souza, F.C., Papadakis, M., Durelli, V.H.S., Delamaro, M.E.: Test data generation techniques for mutation testing: a systematic mapping. In: Proceedings of 11th Workshop on Experimental Software Engineering Latin Americal Wrokshop (ESELAW) (2014)

    Google Scholar 

  26. Fang, J., Zhang, H., Liu, J., Zhao, J., Zhang, Y., Wang, K.: A transformer fault diagnosis model using an optimal hybrid dissolved gas analysis features subset with improved social group optimization-support vector machine classifier. Energies MDPI Open Access J. 11(8), 1–18 (2018)

    Google Scholar 

  27. Naik, A., Satapathy, S.C., Ashour, A.S., Dey, N.: Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Comput. Appl. 30(1), 271–287 (2016)

    Article  Google Scholar 

  28. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology. Control and Artificial Intelligence. MIT Press, Cambridge (1992)

    Book  Google Scholar 

  29. Yang, X.S.: Nature Inspired Metaheuristic Algorithms, vol. 504. Luniver Press (2010)

    Google Scholar 

  30. Kennedy, J., Eberhard, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  31. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. 26(1), 29–41 (1996)

    Article  Google Scholar 

  32. Rajakumar, B.: The Lion’s Algorithm: a new nature-inspired search algorithm. Procedia Technol. 6, 126–135 (2012)

    Article  Google Scholar 

  33. McComb, K., Pusey, A., Packer, C., Grinnell, J.: Female lions can identify potentially infanticidal males from their roars. Biol. Sci. R. Soc. 252(1333), 59–64 (1993)

    Article  Google Scholar 

  34. Watkins, W.A., Schevill, W.E.: Aerial observation of feeding behavior in four baleen whales: eubalaena glacialis, balaenoptera borealis, megaptera novaeangliae, and balaenoptera physalus. J. Mammal. 60(1), 155–163 (1979)

    Article  Google Scholar 

  35. Hof, P.R., Van Der Gucht, E.: Structure of the cerebral cortex of the humpback whale, Megaptera novaeangliae (Cetacea, Mysticeti, Balaenopteridae). Adv. Integr. Anat. Evol. Biol. Anat. Rec. 290, 1–31 (2007)

    Article  Google Scholar 

  36. Ma, Y.-S., Offutt, J., Kwon, Y.R.: MuJava: an automated class mutation system. Softw. Test. Verif. Reliab. 15(2), 97–133 (2005)

    Article  Google Scholar 

  37. Ma, Y.-S., Offutt, J.: Description of method-level mutation operators for Java. Technical report, Electronics and Telecommunications Research Institute, Korea (2005)

    Google Scholar 

  38. Grun, B.J.M., Schuler, D., Zeller, A.: The impact of equivalent mutants. In: 2009 International Conference on Software Testing, Verification, and Validation Workshops, pp. 192–199. IEEE (2009)

    Google Scholar 

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Acknowledgment

The authors would like to acknowledge Ministry of Electronics and Information Technology, Govt. of India for supporting this research under Visvesvaraya Ph.D. Scheme for Electronics and IT.

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Authors and Affiliations

  1. USICT, GGS Indraprastha University, Delhi, India

    Shweta Rani & Bharti Suri

Authors
  1. Shweta Rani
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  2. Bharti Suri
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Corresponding author

Correspondence to Shweta Rani .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg , Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Rani, S., Suri, B. (2019). Adopting Social Group Optimization Algorithm Using Mutation Testing for Test Suite Generation: SGO-MT. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_39

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  • DOI: https://doi.org/10.1007/978-3-030-24305-0_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24304-3

  • Online ISBN: 978-3-030-24305-0

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