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Automated software test optimisation framework – an artificial bee colony optimisation-based approach

Automated software test optimisation framework – an artificial bee colony optimisation-based approach

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Software test suite optimisation is one of the most important problems in software engineering research. To achieve this optimisation, a novel approach based on artificial bee colony (ABC) optimisation is proposed here. The work applied in this approach is motivated by the intelligent behaviour of honey bees. Since the ABC system combines local search methods carried out by employed and onlooker bees with global search methods managed by scouts, the approach attains global or near-global optima. Here, the parallel behaviour of the three bees is used to reach the solution generation faster. The performance of the proposed approach is investigated based on coverage-based test adequacy criteria by comparing it with sequential ABC, random testing and genetic algorithm-based approaches. Based on the experimental results, it has been proved that the proposed parallel ABC approach outperforms the other approaches in test suite optimisation.

References

    1. 1)
      • D. Jeya Mala , M. Kamalapriya , R. Shobana , V. Mohan . A non-pheromone based intelligent swarm optimization technique in software test suite optimization.
    2. 2)
      • Basturk, B., Karaboga, D.: `An artificial bee colony (ABC) algorithm for numeric function optimization', IEEE Swarm Intelligence Symp., 12–14 May 2006, Indianapolis, Indiana, USA.
    3. 3)
      • McMinn, P., Holcombe, M.: `The state problem for evolutionary testing', Proc. GECCO 2003, 2003 (LNCS, 2724), p. 2488–2500.
    4. 4)
      • H. Li , C.P. Lam . Optimization of state-based test suites for software systems: an evolutionary approach. Int. J. Comput. Inf. Sci. , 3 , 212 - 223
    5. 5)
      • Pan, L., Zou, B., Li, J., Chen, H.: `Bi-objective model for test-suite reduction based on modified condition/decision coverage', Proc. PRDC, 2005, p. 12–14.
    6. 6)
      • D. Jeya Mala , V. Mohan . On the use of intelligent agents in test sequence selection and optimization. Int. J. Comput. Intell. Appl. , 2 , 155 - 179
    7. 7)
      • Anderson, C., Mayrhauser, A., Mraz, R.: `On the use of neural networks to guide software testing activities', Proc. Int. Test Conf., 1995.
    8. 8)
      • N. Karaboga . A new design method based on artificial bee colony algorithm for digital IIR filters. J. Franklin Inst. , 4 , 328 - 348
    9. 9)
      • Srinivasa Rao, R., Narasimham, S.V.L., Ramalingaraju, M.: `Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm', Proc. World Academy of Science, Engineering and Technology, 2008, 35, p. 709–715.
    10. 10)
      • D. Teodorović , M. Dell . Bee colony optimization – a cooperative learning approach to complex transportation problems. Adv. OR and AI Meth. Transport. , 51 - 60
    11. 11)
      • Díaz, E., Tuya, J., Blanco, R.: `Automated software testing using a meta-heuristic technique based on tabu search', ASE-2003.
    12. 12)
      • S. Russell , P. Norvig . (2003) Artificial intelligence: a modern approach.
    13. 13)
      • T.Y. Chen , M.F. Lau . Dividing strategies for the optimization of a test suite. Inf. Process. Lett. , 3 , 135 - 141
    14. 14)
      • T.E. Lindquist , K.M. Gutzmann , D.L. Remkes , G. McKee . Optimization of validation test suite coverage. ACM SIGSOFT Softw. Engng. Notes , 3 , 87 - 92
    15. 15)
      • Baudry, B., Fleurey, F., Tron, Y.L.: `Improving test suites for efficient fault localization', ICSE, 2006, p. 82–91.
    16. 16)
      • W. Pedrycz , J.F. Peters . (1998) Computational intelligence in software engineering.
    17. 17)
      • D. Jeya Mala , V. Mohan . IntelligenTester – test sequence optimization using multi-agents. J. Comput. , 6 , 39 - 46
    18. 18)
      • A. Baykasolu , L. Özbakır , P. Tapkan . (2007) Artificial bee colony algorithm and its application to generalized assignment problem.
    19. 19)
      • Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: `Technical note: bees algorithm', Manufacturing Engineering Centre, Cardiff University, Cardiff, 2005.
    20. 20)
      • ABC Algorithm Home page: http://mf.erciyes.edu.tr/abc/.
    21. 21)
      • Michael, C., McGraw, G.: `Automated software test data generation for complex programs', A Technical, 1999.
    22. 22)
      • Karaboga, D., Basturk, B.: `Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems', LNCS: Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, 2007, 4529/2007, p. 789–798, IFSA 2007, doi: 10.1007/978-3-540-72950-1_77.
    23. 23)
      • Zhang , Zili , Zhou . Fuzzy logic based approach for software testing. Int. J. Pattern Recog. Artif. Intell. , 4 , 709 - 722
    24. 24)
      • D. Karaboga , B. Basturk . (2008) On the performance of artificial bee colony (ABC) algorithm, applied soft computing.
    25. 25)
      • N. Tracey , N. Clark , K. Mander , N. McDermid , P. Henderson . (2002) A search based automated test data generation framework for safety critical systems,, Systems engineering for business process change (new directions).
    26. 26)
      • L.-P. Wong , C.Y. Puan , M.Y. Hean Low , C.S. Chong . Bee colony optimization algorithm with big valley landscape exploitation for job shop scheduling problems. Appl. Soft Comput. , 1 , 687 - 697
    27. 27)
      • M. Dorigo , V. Maniezzo , A. Colorni . The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B , 1 , 29 - 41
    28. 28)
      • M. Harman , B.F. Jones . Search based software engineering. Inf. Softw. Technol. , 14 , 833 - 839
    29. 29)
      • P. McMinn . Search-based software test data generation: a survey. Softw. Test. Verif. Reliab. , 2 , 105 - 156
    30. 30)
      • G.J. Myers . (1979) The art of software testing.
    31. 31)
      • B. Beizer . (1990) Software testing techniques.
    32. 32)
      • K. Deb . (2001) Multi-objective optimization using evolutionary algorithms.
    33. 33)
      • D. Jeya Mala , V. Mohan . Hybrid tester – an automated hybrid genetic algorithm based test case optimization framework for effective software testing. Int. J. Comput. Intell. Theory Pract. , 2 , 81 - 94
    34. 34)
      • Tonella, P.: `Evolutionary testing of classes', ISSTA, 2004, p. 11–14.
    35. 35)
      • K. Edward . (1995) Software testing in the real world – improving the process.
    36. 36)
      • T. Csöndes , S. Dibuz , B. Kotnyek . Test suite reduction in conformance testing. Acta Cybern. , 229 - 238
    37. 37)
      • P. Saraph , M. Last , A. Kandel . Test case generation and reduction by automated inout-output analysis. IEEE Trans. Softw. Engng. , 768 - 773
    38. 38)
      • J. Horgan , S. London , M. Lyu . Achieving software quality with testing coverage measures. IEEE Comput. , 9 , 60 - 69
    39. 39)
      • Briand, L.C.: `On the many ways software engineering can benefit from knowledge engineering', Proc. 14th SEKE, 2002, Italy, p. 3–6.
    40. 40)
      • H. Li , C.P. Lam . Software test data generation using ant colony optimization. Trans. Engng. Comput. Technol. , 1 - 4
    41. 41)
      • M. Fathian , B. Amiri , A. Maroosi . Application of honey-bee mating optimization algorithm on clustering. Appl. Math. Comput. , 2 , 1502 - 1513
    42. 42)
      • G. Rothermel , S. Elbaum , A. Malishevsky , P. Kallakuri , X. Qiu . On test suite composition and cost-effective regression testing. ACM Trans. Softw. Engng. Methodol. , 3 , 277 - 331
    43. 43)
      • A.E. Howe , A.V. Mayrhauser , R.T. Mraz . Test case generation as an AI planning problem. Autom. Softw. Engng. , 77 - 106
    44. 44)
      • http://en.wikipedia.org/wiki/Artificial_Bee_Colony_Algorithm.
    45. 45)
      • Zhang, X., Xu, B., Chen, Z., Nie, C., Li, L.: `An empirical evaluation of test suite reduction for boolean specification-based testing', Proc. QSIC-2008, IEEExplore 2008, doi.ieeecomputersociety.org /10.1109/QSIC. 2008.25.
    46. 46)
      • A. Singh . An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Appl. Soft Comput. , 2 , 625 - 631
    47. 47)
      • Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: `The bees algorithm, a novel tool for complex optimization problems', Proc Second Int. Virtual Conf. Intelligent Production Machines and Systems (IPROMS 2006), 2006, p. 454–459.
    48. 48)
      • Seesing A.: ‘EvoTest: test case generation using genetic programming and software analysis’. Thesis submitted to Delft University of Technology.
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