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Solving the Test Case Prioritization Problem with Secure Features Using Ant Colony System

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International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019) (CISIS 2019, ICEUTE 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 951))

Abstract

Nowadays, the correctness of a program is a must and this comes with a plus when is about security. It is a major benefit to have confident results when the software is tested. In particular, when a regression test is made, it ensures that when a program system is modified, the existing and good functionality is not affected. NP-hard problems, including complex optimization problems necessitate high quality and intensively tested software. It is described an optimized test case prioritization method inspired by ant colony optimization, called Test Case Prioritization ANT and denoted by TCP-ANT. The current approach uses the Average Percentage of Fault Detected (APFD) metric as selection criterion, and tries to uncover maximum fault and to reduce the regression testing time. Furthermore, there are considered some metrics to better encapsulate the TCP-ANT execution cost and a criterion for a proper number of test cases, hopefully covering all possible faults. The main aim of the paper is to illustrate the Test Case Prioritization ANT from security perspective. This approach includes a severity factor of an identified fault, when using APFD metric and compares the TCP-ANT beneficent results with random, reverse and no prioritization techniques.

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Notes

  1. 1.

    http://www.cs.ubbcluj.ro/~avescan/?q=node/220.

References

  1. Pezzand, M., Young, M.: Software Testing and Analysis: Process, Principles and Techniques. Wiley, New York (2008)

    Google Scholar 

  2. Battiti, R.: Reactive search: toward self-tuning heuristics. In: Rayward-Smith, V.J., et al. (eds.) Modern Heuristic Search Methods, Chap. 4, pp. 61–83. Wiley, New York (1996)

    Google Scholar 

  3. Cook, W., Cunningham, W., Pulleyblank, W., Schrijver, A.: Combinatorial Optimization. Wiley, New York (1998)

    MATH  Google Scholar 

  4. Pop, P.: Generalized Network Design Problems. Modeling and Optimization. De Gruyter, Berlin (2012)

    Book  Google Scholar 

  5. Yoo, S., Harman, M.: Regression testing minimization, selection and prioritization: a survey. Softw. Test. Verif. Reliab. 22(2), 67–120 (2012)

    Article  Google Scholar 

  6. Panigrahi, C., Mall, R.: An approach to prioritize the regression test cases of object-oriented programs. CSI Trans. ICT 1(2), 159–173 (2013)

    Article  Google Scholar 

  7. Rothermel, G., et al.: Test case prioritization: an empirical study. In: Conference on Software Maintenance, ICSM, Oxford, UK, pp. 179–188 (1999)

    Google Scholar 

  8. Khalilian, A., Azgomi, M., Fazlalizadeh, Y.: An improved method for test case prioritization by incorporating historical test case data. Sci. Comput. Program. 78(1), 93–116 (2012)

    Article  Google Scholar 

  9. Dorigo, M., Gambardella, L.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  10. Graves, T., et al.: An empirical study of regression test selection techniques. In: Proceedings of the International Conference on Software Engineering, ICSE, Kyoto, Japan, pp. 188–197 (1998)

    Google Scholar 

  11. Elbaum, S., Malishevsky, A., Rothermel, G.: Prioritizing test cases for regression testing. In: Proceedings of the International Symposium on Software Testing and Analysis, ISSTA Portland, USA, pp. 102–112 (2000)

    Google Scholar 

  12. Malishevsky, A., et al.: Cost-cognizant test case prioritization. Technical report TR-UNL-CSE-2006-004, University of Nebraska (2006)

    Google Scholar 

  13. Hwang, J., et al.: Selection of regression system tests for security policy evolution. In: IEEE/ACM International Conference on Automated Software Engineering, ASE, Essen, Germany, pp. 266–269 (2012)

    Google Scholar 

  14. OASIS: extensible access control markup language (XACML) (2005)

    Google Scholar 

  15. McGraw, G., Potter, B.: Software security testing. IEEE Secur. Priv. 2(5), 81–85 (2004)

    Article  Google Scholar 

  16. Zhang, X., et al.: Test case prioritization based on varying testing requirement priorities and test case costs. In: Conference on Quality Software (QSIC 2007), Portland, Oregon, USA, pp. 15–24 (2007)

    Google Scholar 

  17. Kayes, M.: Test case prioritization for regression testing based on fault dependency. In: 2011 3rd International Conference on Electronics Computer Technology, vol. 5, pp. 48–52 (2011)

    Google Scholar 

  18. Li, Z., Harman, M., Hierons, R.: Search algorithms for regression test case prioritization. IEEE Trans. Softw. Eng. 33(4), 225–237 (2007)

    Article  Google Scholar 

  19. Agrawal, A., Kaur, A.: A comprehensive comparison of ant colony and hybrid particle swarm optimization algorithms through test case selection. In: Data Engineering and Intelligent Computing, pp. 397–405. Springer, Singapore (2018)

    Google Scholar 

  20. Ahmad, S., Singh, D., Suman, P.: Prioritization for regression testing using ant colony optimization based on test factors. In: Intelligent Communication, Control and Devices, pp. 1353–1360. Springer, Singapore (2018)

    Google Scholar 

  21. Singh, Y., Kaur, A., Suri, B.: Test case prioritization using ant colony optimization. ACM SIGSOFT Softw. Eng. Notes 35(4), 1–7 (2010)

    Article  Google Scholar 

  22. Saraswat, P., Singhal, A., Bansal, A.: A review of test case prioritization and optimization techniques. Advances in Intelligent Systems and Computing, pp. 507–516. Springer, Singapore (2018)

    Google Scholar 

  23. Kavitha, N.: Test case prioritization for regression testing based on severity of fault. Int. J. Comput. Sci. Eng. 2(5), 1462–1466 (2010)

    Google Scholar 

  24. Perez-Uribe, A.: Ant colony system algorithm in C/C++ (2002)

    Google Scholar 

  25. Huang, Y.C., Peng, K.L., Huang, C.Y.: A history-based cost-cognizant test case prioritization technique in regression testing. J. Syst. Softw. 85(3), 626–637 (2012)

    Article  MathSciNet  Google Scholar 

  26. Pintea, C., Dumitrescu, D., Pop, P.: Combining heuristics and modifying local information to guide ant-based search. Carpath. J. Math. 24(1), 94–103 (2008)

    MathSciNet  MATH  Google Scholar 

  27. Pintea, C.M., Pop, P.: Sensor networks security based on sensitive robots agents: a conceptual model. Advances in Intelligent Systems and Computing, vol. 189, pp. 47–56. Springer, Heidelberg (2013)

    Google Scholar 

  28. Pintea, C.M., Crişan, G., Pop, P.: Towards secure transportation based on intelligent transport systems. Novel approach and concepts. Advances in Intelligent Systems and Computing, pp. 469–477. Springer, Cham (2018)

    Google Scholar 

  29. Pintea, C.M., Calinescu, A., Pop Sitar, C., Pop, C.: Towards secure & green two-stage supply chain networks. Logic J. IGPL (jzy028) (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, Babes-Bolyai University, Cluj-Napoca, Romania

    Andreea Vescan

  2. Department of Mathematics and Computer Science, Technical University Cluj-Napoca, North University Center at Baia Mare, Baia-Mare, Romania

    Camelia-M. Pintea & Petrica C. Pop

Authors
  1. Andreea Vescan
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  2. Camelia-M. Pintea
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  3. Petrica C. Pop
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Corresponding author

Correspondence to Camelia-M. Pintea .

Editor information

Editors and Affiliations

  1. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  2. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  3. University of Salamanca, Salamanca, Spain

    José António Sáez Muñoz

  4. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Vescan, A., Pintea, CM., Pop, P.C. (2020). Solving the Test Case Prioritization Problem with Secure Features Using Ant Colony System. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_7

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