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Littérature scientifique sur le sujet « Reinforcement Learning in Testing »

Auteur : Grafiati
Publié le 7 juin 2025
Mis à jour le 2 août 2025

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Articles de revues sur le sujet "Reinforcement Learning in Testing"

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Desharnais, Josée, François Laviolette, and Sami Zhioua. "Testing probabilistic equivalence through Reinforcement Learning." Information and Computation 227 (June 2013): 21–57. http://dx.doi.org/10.1016/j.ic.2013.02.002.

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Varshosaz, Mahsa, Mohsen Ghaffari, Einar Broch Johnsen, and Andrzej Wąsowski. "Formal Specification and Testing for Reinforcement Learning." Proceedings of the ACM on Programming Languages 7, ICFP (2023): 125–58. http://dx.doi.org/10.1145/3607835.

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The development process for reinforcement learning applications is still exploratory rather than systematic. This exploratory nature reduces reuse of specifications between applications and increases the chances of introducing programming errors. This paper takes a step towards systematizing the development of reinforcement learning applications. We introduce a formal specification of reinforcement learning problems and algorithms, with a particular focus on temporal difference methods and their definitions in backup diagrams. We further develop a test harness for a large class of reinforcemen
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Ghanem, Mohamed C., and Thomas M. Chen. "Reinforcement Learning for Efficient Network Penetration Testing." Information 11, no. 1 (2019): 6. http://dx.doi.org/10.3390/info11010006.

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Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The propo
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Deviatko, Anna. "Evolution of Automated Testing Methods Using Machine Learning." American Journal of Engineering and Technology 07, no. 05 (2025): 88–100. https://doi.org/10.37547/tajet/volume07issue05-07.

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program testing is crucial for guaranteeing program dependability, but it has historically included a lot of manual labor, which restricts coverage and raises expenses. By creating and selecting test cases, anticipating defect-prone locations, and evaluating test results, machine learning (ML)-driven testing approaches automate and improve traditional software testing. This study examines the development of these techniques. Significant enhancements are provided by ML-driven techniques, such as early fault detection, shorter testing times, and increased test coverage. The paper offers a thorou
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Abo-eleneen, Amr, Ahammed Palliyali, and Cagatay Catal. "The role of Reinforcement Learning in software testing." Information and Software Technology 164 (December 2023): 107325. http://dx.doi.org/10.1016/j.infsof.2023.107325.

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Sun, Chang-Ai, Ming-Jun Xiao, He-Peng Dai, and Huai Liu. "A Reinforcement Learning Based Approach to Partition Testing." Journal of Computer Science and Technology 40, no. 1 (2025): 99–118. https://doi.org/10.1007/s11390-024-2900-7.

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Tao, Jiaye, Chao Hong, Yun Fu, et al. "Coverage-guided fuzz testing method based on reinforcement learning seed scheduling." Journal of Physics: Conference Series 2816, no. 1 (2024): 012107. http://dx.doi.org/10.1088/1742-6596/2816/1/012107.

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Abstract The existing fuzz testing methods for industrial control protocols suffer from insufficient coverage, false positives, and an inability to handle protocol semantics. This paper proposes a reinforcement learning-based seed scheduling coverage-guided fuzz testing method. Building upon coverage-guided fuzz testing techniques, we integrate reinforcement learning with seed scheduling to optimize the seed selection strategy, thereby enhancing the efficiency of protocol vulnerability detection. Experimental results demonstrate the feasibility and effectiveness of this approach. Through reinf
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Wang, Cong, Qifeng Zhang, Qiyan Tian, et al. "Learning Mobile Manipulation through Deep Reinforcement Learning." Sensors 20, no. 3 (2020): 939. http://dx.doi.org/10.3390/s20030939.

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Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system
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Levytskyi, Volodymyr, and Oleksii Lopuha. "Test data generation using deep reinforcement learning." Management of Development of Complex Systems, no. 59 (September 27, 2024): 155–64. http://dx.doi.org/10.32347/2412-9933.2024.59.155-164.

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The process of creating test data for software is one of the most complex and labor-intensive stages in the software development cycle. It requires significant resources and effort, especially when it comes to achieving high test coverage. Search-Based Software Testing (SBST) is an approach that automates this process by using metaheuristic algorithms to generate test data. Metaheuristic algorithms, such as genetic algorithms or simulated annealing, operate on the principle of systematically exploring possible options and selecting the most effective solutions based on feedback from a fitness
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Pradhan, Shreeja. "Evaluating Deep Reinforcement Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 008 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem37434.

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Abstract—Recent advancements in machine learning, partic- ularly in reinforcement learning (RL), have enabled solutions to previously intractable problems. This research paper delves into the mathematical underpinnings of several prominent deep RL algorithms, including REINFORCE, A2C, DDPG, and SAC. By implementing and testing these algorithms in the MuJoCo simulator, I evaluate their performance in training agents to achieve complex tasks, such as walking in a 3D environment. Our findings demonstrate the efficacy of these algorithms in real-time learning and adaptation, underscored by the sup
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Thèses sur le sujet "Reinforcement Learning in Testing"

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ROMDHANA, ANDREA. "Deep Reinforcement Learning Driven Applications Testing." Doctoral thesis, Università degli studi di Genova, 2023. https://hdl.handle.net/11567/1105635.

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Applications have become indispensable in our lives, and ensuring their correctness is now a critical issue. Automatic system test case generation can significantly improve the testing process for these applications, which has recently motivated researchers to work on this problem, defining various approaches. However, most state-of-the-art approaches automatically generate test cases leveraging symbolic execution or random exploration techniques. This led to techniques that lose efficiency when dealing with an increasing number of program constraints and become inapplicable when conditions ar
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Silguero, Russell V. "Do contingency-conflicting elements drop out of equivalence classes? Re-testing Sidman's (2000) theory." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc848078/.

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Sidman's (2000) theory of stimulus equivalence states that all positive elements in a reinforcement contingency enter an equivalence class. The theory also states that if an element from an equivalence class conflicts with a programmed reinforcement contingency, the conflicting element will drop out of the equivalence class. Minster et al. (2006) found evidence suggesting that a conflicting element does not drop out of an equivalence class. In an effort to explain maintained accuracy on programmed reinforcement contingencies, the authors seem to suggest that participants will behave in accorda
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Fakih, Saif. "A learning approach to obtain efficient testing strategies in medical diagnosis." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000309.

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Koppula, Sreedevi. "Automated GUI Tests Generation for Android Apps Using Q-learning." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984181/.

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Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is
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Izquierdo, Ayala Pablo. "Learning comparison: Reinforcement Learning vs Inverse Reinforcement Learning : How well does inverse reinforcement learning perform in simple markov decision processes in comparison to reinforcement learning?" Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259371.

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This research project elaborates a qualitative comparison between two different learning approaches, Reinforcement Learning (RL) and Inverse Reinforcement Learning (IRL) over the Gridworld Markov Decision Process. The interest focus will be set on the second learning paradigm, IRL, as it is considered to be relatively new and little work has been developed in this field of study. As observed, RL outperforms IRL, obtaining a correct solution in all the different scenarios studied. However, the behaviour of the IRL algorithms can be improved and this will be shown and analyzed as part of the sco
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Seymour, B. J. "Aversive reinforcement learning." Thesis, University College London (University of London), 2010. http://discovery.ucl.ac.uk/800107/.

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We hypothesise that human aversive learning can be described algorithmically by Reinforcement Learning models. Our first experiment uses a second-order conditioning design to study sequential outcome prediction. We show that aversive prediction errors are expressed robustly in the ventral striatum, supporting the validity of temporal difference algorithms (as in reward learning), and suggesting a putative critical area for appetitive-aversive interactions. With this in mind, the second experiment explores the nature of pain relief, which as expounded in theories of motivational opponency, is r
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Player, John Roger. "Dynamic testing of rock reinforcement systems." Thesis, Curtin University, 2012. http://hdl.handle.net/20.500.11937/58684.

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The research consisted of a design, construction, commissioning and comprehensive use of a Dynamic Test Facility for the testing of full scale reinforcement systems. Laboratory testing was undertaken in double embedment configuration under axial load. The energy balance for 74 tests had an average error of ± 2.7kJ measured at impact velocities ranging from 3 to 8m/sec, with total input energies in the range 20 to 96kJ. The results from testing a wide range of reinforcement systems are presented and recommendations made for their application to dynamic rock failure.
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Akrour, Riad. "Robust Preference Learning-based Reinforcement Learning." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112236/document.

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Les contributions de la thèse sont centrées sur la prise de décisions séquentielles et plus spécialement sur l'Apprentissage par Renforcement (AR). Prenant sa source de l'apprentissage statistique au même titre que l'apprentissage supervisé et non-supervisé, l'AR a gagné en popularité ces deux dernières décennies en raisons de percées aussi bien applicatives que théoriques. L'AR suppose que l'agent (apprenant) ainsi que son environnement suivent un processus de décision stochastique Markovien sur un espace d'états et d'actions. Le processus est dit de décision parce que l'agent est appelé à ch
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Tabell, Johnsson Marco, and Ala Jafar. "Efficiency Comparison Between Curriculum Reinforcement Learning & Reinforcement Learning Using ML-Agents." Thesis, Blekinge Tekniska Högskola, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20218.

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Yang, Zhaoyuan Yang. "Adversarial Reinforcement Learning for Control System Design: A Deep Reinforcement Learning Approach." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu152411491981452.

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Livres sur le sujet "Reinforcement Learning in Testing"

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Sutton, Richard S. Reinforcement Learning. Springer US, 1992.

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Wiering, Marco, and Martijn van Otterlo, eds. Reinforcement Learning. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27645-3.

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Sutton, Richard S., ed. Reinforcement Learning. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3618-5.

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Lorenz, Uwe. Reinforcement Learning. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61651-2.

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Nandy, Abhishek, and Manisha Biswas. Reinforcement Learning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3285-9.

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S, Sutton Richard, ed. Reinforcement learning. Kluwer Academic Publishers, 1992.

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Lorenz, Uwe. Reinforcement Learning. Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68311-8.

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Li, Jinna, Frank L. Lewis, and Jialu Fan. Reinforcement Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28394-9.

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Xiao, Zhiqing. Reinforcement Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-4933-3.

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Bigby, D. N. Rock reinforcement and testing. HSE Books, 2004.

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Chapitres de livres sur le sujet "Reinforcement Learning in Testing"

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Veanes, Margus, Pritam Roy, and Colin Campbell. "Online Testing with Reinforcement Learning." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11940197_16.

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Reichstaller, André, Benedikt Eberhardinger, Alexander Knapp, Wolfgang Reif, and Marcel Gehlen. "Risk-Based Interoperability Testing Using Reinforcement Learning." In Testing Software and Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47443-4_4.

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Desharnais, Josée, François Laviolette, and Sami Zhioua. "Testing Probabilistic Equivalence Through Reinforcement Learning." In FSTTCS 2006: Foundations of Software Technology and Theoretical Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11944836_23.

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Lu, Yuteng, Weidi Sun, and Meng Sun. "Mutation Testing of Reinforcement Learning Systems." In Dependable Software Engineering. Theories, Tools, and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91265-9_8.

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Nurakhmetov, Darkhan. "Reinforcement Learning Applied to Adaptive Classification Testing." In Theoretical and Practical Advances in Computer-based Educational Measurement. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18480-3_17.

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Ma, Tao, Shaukat Ali, Tao Yue, and Maged Elaasar. "Fragility-Oriented Testing with Model Execution and Reinforcement Learning." In Testing Software and Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67549-7_1.

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Du, Mengjun, Peiyang Li, Lian Song, W. K. Chan, and Bo Jiang. "OAT: An Optimized Android Testing Framework Based on Reinforcement Learning." In Theoretical Aspects of Software Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35257-7_3.

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Gao, Yuemeng, Chuanqi Tao, Hongjing Guo, and Jerry Gao. "A Deep Reinforcement Learning-Based Approach for Android GUI Testing." In Web and Big Data. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25201-3_20.

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Camilli, Matteo, Raffaela Mirandola, Patrizia Scandurra, and Catia Trubiani. "Towards Online Testing Under Uncertainty Using Model-Based Reinforcement Learning." In Lecture Notes in Computer Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36889-9_17.

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Rodríguez-Valdés, Olivia, Tanja E. J. Vos, Beatriz Marín, and Pekka Aho. "Reinforcement Learning for Scriptless Testing: An Empirical Investigation of Reward Functions." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33080-3_9.

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Actes de conférences sur le sujet "Reinforcement Learning in Testing"

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Doreste, Andréa. "Adversarial Testing with Reinforcement Learning." In 2025 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 2025. https://doi.org/10.1109/icst62969.2025.10988958.

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Steenhoek, Benjamin, Michele Tufano, Neel Sundaresan, and Alexey Svyatkovskiy. "Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00011.

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Nayab, Shahzad, and Franz Wotawa. "Testing and Reinforcement Learning - A Structured Literature Review." In 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C). IEEE, 2024. http://dx.doi.org/10.1109/qrs-c63300.2024.00049.

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Toggi, Aaryen, Bhavna Bose, Dharini Naidu, and Raghav Srivastava. "Metasploit Based Automated Penetration Testing Using Reinforcement Learning." In 2024 First International Conference for Women in Computing (InCoWoCo). IEEE, 2024. https://doi.org/10.1109/incowoco64194.2024.10863399.

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Păduraru, Ciprian, Rareş Cristea, and Alin Stefanescu. "End-to-End RPA-Like Testing Using Reinforcement Learning." In 2024 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 2024. http://dx.doi.org/10.1109/icst60714.2024.00045.

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Nguyen, Tien-Quang, Nghia-Hieu Cong, Ngoc-Minh Quach, Hieu Dinh Vo, and Son Nguyen. "Reinforcement Learning-Based REST API Testing with Multi-Coverage." In 2024 16th International Conference on Knowledge and System Engineering (KSE). IEEE, 2024. https://doi.org/10.1109/kse63888.2024.11063636.

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Ahmad, Tanwir, Matko Butkovic, and Dragos Truscan. "Using Reinforcement Learning for Security Testing: A Systematic Mapping Study." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962455.

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Ferdous, Raihana, Fitsum Kifetew, Davide Prandi, and Angelo Susi. "Curiosity Driven Multi-agent Reinforcement Learning for 3D Game Testing." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962505.

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Mani, Nariman, and Salma Attaranasl. "Adaptive Test Healing using LLM/GPT and Reinforcement Learning." In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2025. https://doi.org/10.1109/icstw64639.2025.10962516.

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Laursen, Thomas Steenfeldt, and Ole Jakob Mengshoel. "Reinforcement Learning for Sustainable Maritime Transportation: Reshaping Adaptive Stress Testing." In 2025 IEEE Symposia on Computational Intelligence for Energy, Transport and Environmental Sustainability (CIETES). IEEE, 2025. https://doi.org/10.1109/cietes63869.2025.10995042.

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Rapports d'organisations sur le sujet "Reinforcement Learning in Testing"

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Singh, Satinder, Andrew G. Barto, and Nuttapong Chentanez. Intrinsically Motivated Reinforcement Learning. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440280.

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Ghavamzadeh, Mohammad, and Sridhar Mahadevan. Hierarchical Multiagent Reinforcement Learning. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada440418.

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Harmon, Mance E., and Stephanie S. Harmon. Reinforcement Learning: A Tutorial. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada323194.

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Tadepalli, Prasad, and Alan Fern. Partial Planning Reinforcement Learning. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada574717.

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Ghavamzadeh, Mohammad, and Sridhar Mahadevan. Hierarchical Average Reward Reinforcement Learning. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada445728.

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Johnson, Daniel W. Drive-Reinforcement Learning System Applications. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada264514.

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Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn, and Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48418.

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The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a mari
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Cleland, Andrew. Bounding Box Improvement With Reinforcement Learning. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6322.

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Li, Jiajie. Learning Financial Investment Strategies using Reinforcement Learning and 'Chan theory'. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-946.

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Baird, III, Klopf Leemon C., and A. H. Reinforcement Learning With High-Dimensional, Continuous Actions. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada280844.

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