Hybrid Approach for Automated Test Data Generation
DOI:
https://doi.org/10.13052/jicts2245-800X.1043Keywords:
Test data generation, metaheuristic search, artificial immune search, ant colony optimization, negative selection algorithm, path coverageAbstract
Software testing has long been thought to be a good technique to improve the software quality and reliability. Path testing is the most reliable software testing technique and the key method for improving software quality among all testing approaches. On the other hand, test data quality has a big impact on the software testing activity’s ability to detect errors or defects. To solving testing problem, one must locate the entire search space for the relevant input data to encompass the different paths in the testable program. To satisfy path coverage, it is vital test to look at the accumulated test data across the thorough search area. A new approach based on ant colony optimization and negative selection algorithm (HACO-NSA) is presented in this research which overcome the flaws associated with search-based test data by generated automated test data. The optimum path testing objective is to generate appropriate test data to maximise coverage and to enhance the test data’s efficacy, as a result, the test data’s adequacy is validated using a path-based fitness function. In the NSA generation stage, the suggested method alters the new detectors creation using ACO. The proposed approach is evaluated for metrics such as average coverage, average generation, average time, and success rate and comparison has been done with random testing, ant colony optimization and negative selection algorithm Different benchmark programs have been used for object-oriented system. The findings show that the hybrid methodology escalates the coverage percentage and curtail test data size, reduces the redundancy in data and enhances the efficiency. The proposed approach is follows IEEE 829-2008 test documentation in entire testing process.
Downloads
References
S. C. Ntafos, “A Comparison of Some Structural Testing Strategies,” IEEE Trans. Softw. Eng., vol. 14, no. 6, pp. 868–874, 1988, doi: 10.1109/32.6165.
G. D. Everett and R. McLeod, Software Testing: Testing Across the Entire Software Development Life Cycle. 2006.
K. Sneha and G. M. Malle, “Assistant Professor in Computer Science Department,” 2017 Int. Conf. Energy, Commun. Data Anal. Soft Comput., pp. 77–81, 2017.
M. A. Jamil, M. Arif, N. Sham, A. Abubakar, and A. Ahmad, “Software Testing Techniques: A Literature Review,” 2016, doi: 10.1109/ICT4M.2016.40.
N. Anwar and S. Kar, “Review Paper on Various Software Testing Techniques & Strategies,” vol. 19, no. 2, 2019.
O. Sahin and B. Akay, “Comparisons of metaheuristic algorithms and fitness functions on software test data generation,” Appl. Soft Comput., vol. 49, pp. 1202–1214, 2016, doi: 10.1016/j.asoc.2016.09.045.
V. Garousi and M. V. Mäntylä, “A systematic literature review of literature reviews in software testing,” Inf. Softw. Technol., vol. 80, pp. 1339–1351, 2016, doi: 10.1016/j.infsof.2016.09.002.
S. Parnami, “Testing Target Path by Automatic Generation of,” vol. 3, no. 8, pp. 825–832, 2013.
K. Lakhotia and P. Mcminn, “Automated Test Data Generation for Coverage: Haven’t We Solved This Problem Yet?”
M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization artificial ants as a computational intelligence technique,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, 2006, doi: 10.1109/CI-M.2006.248054.
S. Anand et al., “The Journal of Systems and Software An orchestrated survey of methodologies for automated software test case generation Orchestrators and Editors,” vol. 86, no. 2013, pp. 1978–2001, 2015, doi: 10.1016/j.jss.2013.02.061.
M. Harman, S. A. Mansouri, and Y. Zhang, “Search Based Software Engineering: A Comprehensive Analysis and Review of Trends Techniques and Applications,” pp. 1–78, 2009.
M. Harman and P. Mcminn, “A Multi-Objective Approach To Search-Based Test Data Generation.”
W. Rhmann, “Dynamic Test Data Generation using Negative Selection Algorithm and Equivalence Class Partitioning,” vol. 8, no. 3, pp. 189–192, 2017.
J. Al-Enezi, M. Abbod, and S. Alsharhan, “Artificial Immune Systems-models, algorithms and applications,” Int. J. Res. Rev. Appl. Sci., vol. 3, no. May, pp. 118–131, 2010, [Online]. Available: http://bura.brunel.ac.uk/handle/2438/4643.
R. Rahnamoun, “Distributed Black-Box Software Testing Using Negative Selection,” vol. 2, no. 3, pp. 151–157, 2013.
S. Mustafa, U. Teknologi, R. Mohamad, and U. Teknologi, “Automated path testing using the negative selection algorithm,” no. April, 2017, doi: 10.1504/IJCVR.2017.10001815.
A. Pachauri, “Use of Clonal Selection Algorithm as Software Test Data Generation Technique,” vol. 2, no. 2, pp. 1–5, 2012.
S. M. M. Id, R. Mohamad, and S. Deris, “Optimal path test data generation based on hybrid negative selection algorithm and genetic algorithm,” pp. 1–21, 2020, doi: 10.1371/journal.pone.0242812.
S. M. Mohi-Aldeen, S. Deris, and R. Mohamad, “Systematic mapping study in automatic test case generation,” Front. Artif. Intell. Appl., vol. 265, pp. 703–720, 2014, doi: 10.3233/978-1-61499-434-3-703.
M. Harman and B. F. Jones, “Search-based software engineering,” vol. 43, pp. 833–839, 2001.
G. I. Latiu, O. A. Cret, and L. Vacariu, “Automatic Test Data Generation for Software Path Testing Using Evolutionary Algorithms,” 2012 Third Int. Conf. Emerg. Intell. Data Web Technol., pp. 1–8, 2012, doi: 10.1109/EIDWT.2012.25.
M. Harman, P. Mcminn, and R. Court, “A Theoretical & Empirical Analysis of Evolutionary Testing and Hill Climbing for Structural Test Data Generation,” 2007.
Y. Chen, Y. Zhong, T. Shi, and J. Liu, “Comparison of Two Fitness Functions for GA-based Path-Oriented Test Data Generation,” 2009, doi: 10.1109/ICNC.2009.235.
H. Tahbildar and B. Kalita, “Automated Software Test Data Generation: Direction of Research,” vol. 2, no. 1, 2011.
X. Zhu, “Software Test Data Generation Automatically Based on Improved Adaptive Particle Swarm Optimizer,” pp. 1300–1303, 2010, doi: 10.1109/ICCIS.2010.321.
S. Singla, D. Kumar, H. M. Rai, and P. Singla, “A Hybrid PSO Approach to Automate Test Data Generation for Data Flow Coverage with Dominance Concepts,” vol. 37, pp. 15–26, 2011.
D. A. N. Liu, X. Wang, and J. Wang, “AUTOMATIC TEST CASE GENERATION BASED ON GENETIC ALGORITHM,” vol. 48, no. 1, pp. 411–416, 2013.
M. A. Ahmed and I. Hermadi, “GA-based multiple paths test data generator,” vol. 35, pp. 3107–3124, 2008, doi: 10.1016/j.cor.2007.01.012.
S. Sekhara, B. Lam, M. L. H. Prasad, U. K. M, and S. Ch, “Procedia Engineering Automated Generation of Independent Paths and Test Suite Optimization Using Artificial Bee Colony,” vol. 00, no. 2011, 2012, doi: 10.1016/j.proeng.2012.01.851.
S. S. Dahiya, J. K. Chhabra, and S. Kumar, “Application of Artificial Bee Colony Algorithm to Software Testing,” Softw. Eng. Conf. (ASWEC), 2010 21st Aust., pp. 149–154, 2010, doi: 10.1109/ASWEC.2010.30.
B. Suri, P. Kaur, D. B. Suri, and P. Kaur, “Path Based Test Suite Augmentation using Artificial Bee Colony Algorithm,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 2, no. Ix, pp. 156–164, 2014.
S. Yang, T. Man, and J. Xu, “Improved ant algorithms for software testing cases generation,” Sci. World J., vol. 2014, 2014, doi: 10.1155/2014/392309.
C. Mao, L. Xiao, X. Yu, and J. Chen, “Adapting ant colony optimization to generate test data for software structural testing,” Swarm Evol. Comput., vol. 20, pp. 23–36, 2015, doi: 10.1016/j.swevo.2014.10.003.
P. Sharma, “Automated Software Testing Using Metahurestic Technique Based on Improved Ant Algorithms for Software Testing,” pp. 3505–3510.
P. R. Srivastava, “Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization,” 2010.
F. Sayyari and S. Emadi, “Automated generation of software testing path based on ant colony,” 2015 International Congress on Technology, Communication and Knowledge (ICTCK), 2015, pp. 435–440, doi: 10.1109/ICTCK.2015.7582709.
S. M. Mohi-Aldeen, R. Mohamad, and S. Deris, “Application of Negative Selection Algorithm (NSA) for test data generation of path testing,” Appl. Soft Comput. J., vol. 49, pp. 1118–1128, 2016, doi: 10.1016/j.asoc.2016.09.044.
P. Saini and S. Tyagi, “Test Data Generation for Basis Path Testing Using Genetic Algorithm and Clonal Selection Algorithm,” vol. 3, no. 6, pp. 2012–2015, 2014.
C. Mao, X. Yu, J. Chen, and J. Chen, “Generating Test Data for Structural Testing Based on Ant Colony Optimization,” 2012 12th Int. Conf. Qual. Softw., no. May, pp. 98–101, 2012, doi: 10.1109/QSIC.2012.12.
S. M. Mohi-aldeen, R. Mohamad, and S. Deris, “Automatic Test Case Generation for Structural Testing Using Negative Selection Algorithm.”
a. E. Rizzoli, “Ant colony optimization for real-world vehicle routing problems,” Swarm Intell., vol. 133, no. 1, pp. 87–151, 2007, doi: 10.1007/s11721-007-0005-x.
M. Dorigo, V. Maniezzo, and A. Colorni, “Dorigo-Maniezzo-Colomi_ the-Ant-System-Optimization-By-a-Colony-of-Cooperating-Agents,” vol. 26, no. 1, pp. 1–26, 1999, [Online]. Available: papers: //82ac23f7-2eaf-4339-a5e1-4600c19d7f01/Paper/p2331.
K. Socha and M. Dorigo, “Ant colony optimization for continuous domains,” Eur. J. Oper. Res., vol. 185, no. 3, pp. 1155–1173, 2008, doi: 10.1016/j.ejor.2006.06.046.
S. Nallaperuma, M. Wagner, and F. Neumann, “Ant Colony Optimisation and the Traveling Salesperson Problem – Hardness, Features and Parameter Settings Categories and Subject Descriptors,” no. I, 2013.
M. Dorigo and T. Stützle, Optimization.
J. Timmis, A. Hone, T. Stibor, and E. Clark, “Theoretical advances in artificial immune systems,” Theor. Comput. Sci., vol. 403, no. 1, pp. 11–32, 2008, doi: 10.1016/j.tcs.2008.02.011.
Neal, Mark, Stepney, Susan, Smith, Robert, Timmis, Jon. (2005). Conceptual frameworks for artificial immune systems. International Journal of Unconventional Computing, pp. 315–338, 2005.
D. Dasgupta, “Advances in artificial immune systems,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 40–43, 2006, doi: 10.1109/CI-M.2006.248056.
E. Bendiab, E. Bendiab, and M. K. Kholladi, “unsupervised classification based algorithm,” no. May 2017, 2012.
Z. Liu, T. A. O. Li, J. I. N. Yang, and T. A. O. Yang, “An Improved Negative Selection Algorithm Based on Subspace Density Seeking,” IEEE Access, vol. 5, pp. 12189–12198, 2017, doi: 10.1109/ACCESS.2017.2723621.
H. Hou and G. Dozier, “an evaluation of negative selection algorithm with constraint-based detectors,” 2006.
P. Agarwal, “Nature-Inspired Algorithms: State-of-Art, Problems and Prospects,” vol. 100, no. 14, pp. 14–21, 2014.
E. Alba and J. F. Chicano, “Software testing with evolutionary strategies,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3943 LNCS, pp. 50–65, 2006, doi: 10.1007/11751113_5.
I. Hermadi, C. Lokan, and R. Sarker, “Dynamic stopping criteria for search-based test data generation for path testing,” Inf. Softw. Technol., vol. 56, no. 4, pp. 395–407, 2014, doi: 10.1016/j.infsof.2014.01.001.
S. Kumar, D. K. Yadav, and D. A. Khan, “Artificial Bee Colony based Test Data Generation for Data-Flow Testing,” Indian J. Sci. Technol., vol. 9, no. 39, 2016, doi: 10.17485/ijst/2016/v9i39/100733.
C. C. Michael, G. McGraw, and M. A. Schatz, “Generating software test data by evolution,” IEEE Trans. Softw. Eng., vol. 27, no. 12, pp. 1085–1110, 2001, doi: 10.1109/32.988709.
A. S. Ghiduk, “Automatic generation of basis test paths using variable length genetic algorithm,” Inf. Process. Lett., vol. 114, no. 6, pp. 304–316, 2014, doi: 10.1016/j.ipl.2014.01.009.
R. Malhotra, “Comparison of Search based Techniques for Automated Test Data Generation,” vol. 95, no. 23, pp. 4–8, 2014.
