Výsledky vyhledávání - • Software and its engineering → Software testing and debugging Reinforcement learning

  1. 1

    Adaptive REST API Testing with Reinforcement Learning Autor Kim, Myeongsoo, Sinha, Saurabh, Orso, Alessandro

    ISSN: 2643-1572
    Vydáno: IEEE 11.09.2023
    “… To address these limitations, we present an adaptive REST API testing technique that incorporates reinforcement learning to prioritize…”
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  2. 2

    Automatic Web Testing Using Curiosity-Driven Reinforcement Learning Autor Zheng, Yan, Liu, Yi, Xie, Xiaofei, Liu, Yepang, Ma, Lei, Hao, Jianye, Liu, Yang

    ISBN: 1665402962, 9781665402965
    ISSN: 1558-1225
    Vydáno: IEEE 01.05.2021
    “… WebExplor adopts a curiosity-driven reinforcement learning to generate high-quality action sequences (test cases…”
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  3. 3

    DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning Autor Corradini, Davide, Montolli, Zeno, Pasqua, Michele, Ceccato, Mariano

    ISSN: 2643-1572
    Vydáno: ACM 27.10.2024
    “… However, current black-box testing approaches rely heavily on the information available in the API's formal documentation, i.e…”
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  4. 4

    Intelligent software debugging: A reinforcement learning approach for detecting the shortest crashing scenarios Autor Durmaz, Engin, Tümer, M. Borahan

    ISSN: 0957-4174, 1873-6793
    Vydáno: New York Elsevier Ltd 15.07.2022
    Vydáno v Expert systems with applications (15.07.2022)
    “… Thus, automatic software testing methods have become inevitable to catch more bugs. To locate and repair bugs with an emphasis on the crash scenarios, we present in this work a reinforcement learning (RL…”
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  5. 5

    DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems Autor Ma, Lei, Juefei-Xu, Felix, Zhang, Fuyuan, Sun, Jiyuan, Xue, Minhui, Li, Bo, Chen, Chunyang, Su, Ting, Li, Li, Liu, Yang, Zhao, Jianjun, Wang, Yadong

    ISSN: 2643-1572
    Vydáno: ACM 01.09.2018
    “…Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data…”
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  6. 6

    Accelerating Finite State Machine-Based Testing Using Reinforcement Learning Autor Turker, Uraz Cengiz, Hierons, Robert M., El-Fakih, Khaled, Mousavi, Mohammad Reza, Tyukin, Ivan Y.

    ISSN: 0098-5589, 1939-3520
    Vydáno: New York IEEE 01.03.2024
    “… This paper addresses this scalability problem by introducing a reinforcement learning framework…”
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    Journal Article
  7. 7

    Can Cooperative Multi-Agent Reinforcement Learning Boost Automatic Web Testing? An Exploratory Study Autor Fan, Yujia, Wang, Sinan, Fei, Zebang, Qin, Yao, Li, Huaxuan, Liu, Yepang

    ISSN: 2643-1572
    Vydáno: ACM 27.10.2024
    “…Reinforcement learning (RL)-based web GUI testing techniques have attracted significant attention in both academia and industry due to their ability to facilitate automatic and intelligent exploration of websites under test…”
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  8. 8

    On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementations Autor Hundal, Rajdeep Singh, Xiao, Yan, Cao, Xiaochun, Dong, Jin Song, Rigger, Manuel

    ISSN: 1558-1225
    Vydáno: IEEE 26.04.2025
    “…Deep Reinforcement Learning (DRL) is a paradigm of artificial intelligence where an agent uses a neural network to learn which actions to take in a given environment…”
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  9. 9

    A Test Oracle for Reinforcement Learning Software Based on Lyapunov Stability Control Theory Autor Zhang, Shiyu, Song, Haoyang, Wang, Qixin, Shen, Henghua, Pei, Yu

    ISSN: 1558-1225
    Vydáno: IEEE 26.04.2025
    “…Reinforcement Learning (RL) has gained significant attention in recent years. As RL software becomes more complex and infiltrates critical application domains, ensuring its quality and correctness becomes increasingly important…”
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  10. 10

    Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data Autor Tappler, Martin, Pferscher, Andrea, Aichernig, Bernhard K., Konighofer, Bettina

    ISSN: 1558-1225
    Vydáno: ACM 14.04.2024
    “…Reinforcement learning from demonstrations (RLfD) is a promising approach to improve the exploration efficiency of reinforcement learning (RL…”
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  11. 11

    Efficient state synchronisation in model-based testing through reinforcement learning Autor Turker, Uraz Cengiz, Hierons, Robert M., Mousavi, Mohammad Reza, Tyukin, Ivan Y.

    ISSN: 2643-1572
    Vydáno: IEEE 01.11.2021
    “…Model-based testing is a structured method to test complex systems. Scaling up model-based testing to large systems requires improving the efficiency of various steps involved in testcase generation and more importantly, in test-execution…”
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  12. 12

    Deeply Reinforcing Android GUI Testing with Deep Reinforcement Learning Autor Lan, Yuanhong, Lu, Yifei, Li, Zhong, Pan, Minxue, Yang, Wenhua, Zhang, Tian, Li, Xuandong

    ISSN: 1558-1225
    Vydáno: ACM 14.04.2024
    “… While previous studies have demonstrated the superiority of Reinforcement Learning (RL) in Android GUI testing, its effectiveness remains limited, particularly in large, complex apps…”
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  13. 13

    Learning-to-Rank vs Ranking-to-Learn: Strategies for Regression Testing in Continuous Integration Autor Bertolino, Antonia, Guerriero, Antonio, Miranda, Breno, Pietrantuono, Roberto, Russo, Stefano

    ISSN: 1558-1225
    Vydáno: ACM 01.10.2020
    “…, reinforcement learning). Various ML algorithms can be applied in each strategy. In this paper we introduce ten of such algorithms for adoption in CI practices, and perform…”
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  14. 14

    Reinforcement Learning-Based Fuzz Testing for the Gazebo Robotic Simulator Autor Ren, Zhilei, Li, Yitao, Li, Xiaochen, Qi, Guanxiao, Xuan, Jifeng, Jiang, He

    ISSN: 2994-970X, 2994-970X
    Vydáno: New York, NY, USA ACM 22.06.2025
    “… mechanism to handle strict input requirements, and a reinforcement learning-based command generator selection…”
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  15. 15

    AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL Autor Stennett, Tyler, Kim, Myeongsoo, Sinha, Saurabh, Orso, Alessandro

    ISSN: 2574-1934
    Vydáno: IEEE 27.04.2025
    “…) with Multi-Agent Reinforcement Learning (MARL) and large language models (LLMs) for effective REST API testing…”
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  16. 16

    Location is Key: Leveraging LLM for Functional Bug Localization in Verilog Design Autor Yao, Bingkun, Wang, Ning, Zhou, Jie, Wang, Xi, Gao, Hong, Jiang, Zhe, Guan, Nan

    Vydáno: IEEE 22.06.2025
    “…In Verilog code design, identifying and locating functional bugs is an important yet challenging task. Existing automatic bug localization methods have limited…”
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  17. 17

    Automatic HMI Structure Exploration Via Curiosity-Based Reinforcement Learning Autor Cao, Yushi, Zheng, Yan, Lin, Shang-Wei, Liu, Yang, Teo, Yon Shin, Toh, Yuxuan, Adiga, Vinay Vishnumurthy

    ISSN: 2643-1572
    Vydáno: IEEE 01.11.2021
    “…Discovering the underlying structure of HMI software efficiently and sufficiently for the purpose of testing without any prior knowledge on the software logic remains a difficult problem…”
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  18. 18

    Formal Specification and Testing for Reinforcement Learning Autor Varshosaz, Mahsa, Ghaffari, Mohsen, Johnsen, Einar Broch, Wąsowski, Andrzej

    ISSN: 2475-1421, 2475-1421
    Vydáno: New York, NY, USA ACM 30.08.2023
    “…The development process for reinforcement learning applications is still exploratory rather than systematic…”
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  19. 19

    Reward Augmentation in Reinforcement Learning for Testing Distributed Systems Autor Borgarelli, Andrea, Enea, Constantin, Majumdar, Rupak, Nagendra, Srinidhi

    ISSN: 2475-1421, 2475-1421
    Vydáno: New York, NY, USA ACM 08.10.2024
    “… We describe a randomized testing approach for distributed protocol implementations based on reinforcement learning…”
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  20. 20

    A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs Autor Kim, Myeongsoo, Stennett, Tyler, Sinha, Saurabh, Orso, Alessandro

    ISSN: 1558-1225
    Vydáno: IEEE 26.04.2025
    “… a dependency-embedded multi-agent approach for REST API testing that integrates multi-agent reinforcement learning (MARL…”
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