Reinforcement Learning-Driven Adaptive Test Case Generation in Agile Development
Keywords:
reinforcement learning, adaptive test case generation, agile development, ScrumAbstract
Reinforcement learning (RL) is turned out to be a powerful environment for optimising complex decision-making processes End the application of reinforcement learning in software testing presents a very promising a method for enhancing test case generation. The objective of this study is to introduce a framework that can integrate RL-driven adaptive test case generation into Agile and Scrum-based development environments.
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References
D. A. Carlson, A. R. Kiessling, and R. S. Sutton, "Reinforcement Learning for Test Case Prioritization," in Proceedings of the 26th IEEE International Conference on Software Testing, Verification and Validation (ICST), 2023, pp. 45-55.
P. S. Nouwou Mindom, A. Nikanjam, and F. Khomh, "A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks," arXiv preprint arXiv:2208.12136, 2022.
C.-Y. Tsai and G. W. Taylor, "DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software," arXiv preprint arXiv:2201.12602, 2022.
B. Steenhoek, M. Tufano, N. Sundaresan, and A. Svyatkovskiy, "Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation," arXiv preprint arXiv:2310.02368, 2023.
M. Esnaashari and S. Damia, "Reinforcement Learning for Automated Test Case Generation: A Survey," Journal of Systems and Software, vol. 183, p. 111074, 2022.
H. Abo-eleneen, M. A. Ahmed, and M. A. Ismail, "Adaptive Test Case Prioritization Using Reinforcement Learning," Information and Software Technology, vol. 142, p. 106743, 2022.
T. Vuong and H. Takada, "Applying Reinforcement Learning to Automated GUI Testing," in Proceedings of the 29th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2018, pp. 838-843.
A. Zolfagharian, S. Mirarab, and M. R. Mousavi, "An Evolutionary Reinforcement Learning Approach for Test Case Generation in Cyber-Physical Systems," IEEE Transactions on Cybernetics, vol. 53, no. 2, pp. 987-1000, 2023.
M. Le, T. Hoang, and T. Nguyen, "Deep Reinforcement Learning for Automated GUI Testing," in Proceedings of the 44th International Conference on Software Engineering (ICSE), 2022, pp. 1234-1245.
S. Wan, X. Zhang, and Y. Liu, "Reinforcement Learning-Based Test Case Prioritization for Continuous Integration," in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2020, pp. 1030-1041.
A. Ahmadi, M. Shokouhi, and A. Hamzeh, "Reinforcement Learning for Test Case Selection in Regression Testing," Software Quality Journal, vol. 30, no. 1, pp. 45-67, 2022.
F. Haouari, M. Kessentini, and W. K. Chan, "Automated Test Case Generation Using Reinforcement Learning and Search-Based Software Engineering," Empirical Software Engineering, vol. 27, no. 3, p. 67, 2022.
A. Ahmed, M. U. Khan, and S. Lee, "Reinforcement Learning for Test Case Prioritization in Continuous Integration Environments," Information and Software Technology, vol. 139, p. 106714, 2021.
S. Sukur, M. B. Cohen, and S. Poulding, "Reinforcement Learning for Combinatorial Interaction Testing," in Proceedings of the 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE), 2018, pp. 208-219.
X. Hu, Y. Jia, and M. Harman, "Adaptive Test Case Prioritization Using Reinforcement Learning," IEEE Transactions on Software Engineering, vol. 47, no. 4, pp. 789-811, 2021.
G. Tesauro, "Temporal Difference Learning and TD-Gammon," Communications of the ACM, vol. 38, no. 3, pp. 58-68, 1995.
D. Silver et al., "Mastering the Game of Go Without Human Knowledge," Nature, vol. 550, pp. 354-359, 2017.
D. Silver et al., "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm," arXiv preprint arXiv:1712.01815, 2017.
R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA: MIT Press, 2018.
D. P. Bertsekas, Reinforcement Learning and Optimal Control. Belmont, MA: Athena Scientific, 2019.
