Software Testing Based on Research: A Road Map
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Abstract
This paper serves as a guide for both researchers and students who are new to the research area of Search-Based Package Testing. The application of metaheuristic explore methods in this context specifically pertains to utilizing these algorithms for generating test data. In the realm of software engineering research, software testing emerges as a robust and fertile ground for exploration. The integration of AI methods into software program testing is an evolving research direction. Often, newcomers to this field face challenges due to limited knowledge about the interaction between software testing and artificial intelligence. This paper aims to provide a roadmap for these new researchers or students in the field.
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Aguilar-Ruiz, J.S., et al., An evolutionary approach to estimating software development projects. Information and Software Technology, 2001. 43(14): p. 875-882. DOI: https://doi.org/10.1016/S0950-5849(01)00193-8
Harman, M. and B.F. Jones, Search-based software engineering. Information and software Technology, 2001. 43(14): p. 833-839. DOI: https://doi.org/10.1016/S0950-5849(01)00189-6
Harman, M., S.A. Mansouri, and Y. Zhang, Search-based software engineering: Trends, techniques and applications. ACM Computing Surveys (CSUR), 2012. 45(1): p. 1-61. DOI: https://doi.org/10.1145/2379776.2379787
Akhtar, M.F., K. Ali, and S. Sadaqat, Factors influencing the profitability of Islamic banks of Pakistan. International research journal of finance and economics, 2011. 66(66): p. 1-8.
Bagnall, A.J., V.J. Rayward-Smith, and I.M. Whittley, The next release problem. Information and software technology, 2001. 43(14): p. 883-890. DOI: https://doi.org/10.1016/S0950-5849(01)00194-X
Kirsopp, C., M.J. Shepperd, and J. Hart, Search heuristics, case-based reasoning and software project effort prediction. 2002.
Mitchell, B.S. and S. Mancoridis, On the evaluation of the bunch search-based software modularization algorithm. Soft Computing, 2008. 12: p. 77-93. DOI: https://doi.org/10.1007/s00500-007-0218-3
Canfora, G., et al. An approach for QoS-aware service composition based on genetic algorithms. in Proceedings of the 7th annual conference on Genetic and evolutionary computation. 2005. DOI: https://doi.org/10.1145/1068009.1068189
Cohen, J.A., A.P. Mannarino, and V.R. Staron, A pilot study of modified cognitive-behavioral therapy for childhood traumatic grief (CBT-CTG). Journal of the American Academy of Child & Adolescent Psychiatry, 2006. 45(12): p. 1465-1473. DOI: https://doi.org/10.1097/01.chi.0000237705.43260.2c
Mitchison, H.M., et al., Targeted disruption of the Cln3 gene provides a mouse model for Batten disease. Neurobiology of disease, 1999. 6(5): p. 321-334. DOI: https://doi.org/10.1006/nbdi.1999.0267
Harman, G., Prince of networks: Bruno Latour and metaphysics. 2009: re. press.
Vogel, T., C. Tran, and L. Grunske, A comprehensive empirical evaluation of generating test suites for mobile applications with diversity. Information and Software Technology, 2021. 130: p. 106436. DOI: https://doi.org/10.1016/j.infsof.2020.106436
Anand, A., et al., Knowledge sharing, knowledge transfer and SMEs: evolution, antecedents, outcomes and directions. Personnel review, 2021. 50(9): p. 1873-1893. DOI: https://doi.org/10.1108/PR-05-2020-0372
Shioda, S., Coupon subset collection problem with quotas. Methodology and Computing in Applied Probability, 2021. 23(4): p. 1203-1235. DOI: https://doi.org/10.1007/s11009-020-09811-z
Feldt, R. and S. Yoo. Flexible probabilistic modeling for search based test data generation. in Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops. 2020. DOI: https://doi.org/10.1145/3387940.3392215
Sarro, K.J., et al., Seasonal variation of strength and power magnitude and asymmetry, and injury profile of Brazilian jiu-jitsu athletes. Journal of Physical Education and Sport, 2022. 22(6): p. 1346-1355.
Parry, O., et al. Flake it'till you make it: Using automated repair to induce and fix latent test flakiness. in Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops. 2020. DOI: https://doi.org/10.1145/3387940.3392177
Binkley, D., et al. An Investigation into the Effect of Control and Data Dependence Paths on Predicate Testability. in 2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM). 2020. IEEE. DOI: https://doi.org/10.1109/SCAM51674.2020.00023
Dąbrowski, P., et al., Photosynthetic efficiency of Microcystis ssp. under salt stress. Environmental and Experimental Botany, 2021. 186: p. 104459. DOI: https://doi.org/10.1016/j.envexpbot.2021.104459
Anand, S., et al., An orchestrated survey of methodologies for automated software test case generation. Journal of systems and software, 2013. 86(8): p. 1978-2001. DOI: https://doi.org/10.1016/j.jss.2013.02.061
McMinn, P., Search‐based software test data generation: a survey. Software testing, Verification and reliability, 2004. 14(2): p. 105-156. DOI: https://doi.org/10.1002/stvr.294
Roper, S., Product innovation and small business growth: a comparison of the strategies of German, UK and Irish companies. Small Business Economics, 1997. 9: p. 523-537. DOI: https://doi.org/10.1023/A:1007963604397
Miller, W. and D.L. Spooner, Automatic generation of floating-point test data. IEEE Transactions on Software Engineering, 1976(3): p. 223-226. DOI: https://doi.org/10.1109/TSE.1976.233818
McMinn, P. Search-based software testing: Past, present and future. in 2011 IEEE Fourth International Conference on Software Testing, Verification and Validation Workshops. 2011. IEEE. DOI: https://doi.org/10.1109/ICSTW.2011.100
Pargas, R.P., M.J. Harrold, and R.R. Peck, Test‐data generation using genetic algorithms. Software testing, verification and reliability, 1999. 9(4): p. 263-282. DOI: https://doi.org/10.1002/(SICI)1099-1689(199912)9:4<263::AID-STVR190>3.0.CO;2-Y
Tracey, I., et al., Imaging attentional modulation of pain in the periaqueductal gray in humans. Journal of Neuroscience, 2002. 22(7): p. 2748-2752. DOI: https://doi.org/10.1523/JNEUROSCI.22-07-02748.2002