Search Results - • Software and its engineering → Software testing and debugging Reinforcement learning

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

“… To address these limitations, we present an adaptive REST API testing technique that incorporates reinforcement learning to prioritize…”
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Automatic Web Testing Using Curiosity-Driven Reinforcement Learning by Zheng, Yan, Liu, Yi, Xie, Xiaofei, Liu, Yepang, Ma, Lei, Hao, Jianye, Liu, Yang

“… WebExplor adopts a curiosity-driven reinforcement learning to generate high-quality action sequences (test cases…”
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DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning by Corradini, Davide, Montolli, Zeno, Pasqua, Michele, Ceccato, Mariano

“… However, current black-box testing approaches rely heavily on the information available in the API's formal documentation, i.e…”
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Intelligent software debugging: A reinforcement learning approach for detecting the shortest crashing scenarios by Durmaz, Engin, Tümer, M. Borahan

“… 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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DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems by 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

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

“… This paper addresses this scalability problem by introducing a reinforcement learning framework…”
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Can Cooperative Multi-Agent Reinforcement Learning Boost Automatic Web Testing? An Exploratory Study by Fan, Yujia, Wang, Sinan, Fei, Zebang, Qin, Yao, Li, Huaxuan, Liu, Yepang

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

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

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

“…Reinforcement learning from demonstrations (RLfD) is a promising approach to improve the exploration efficiency of reinforcement learning (RL…”
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Efficient state synchronisation in model-based testing through reinforcement learning by Turker, Uraz Cengiz, Hierons, Robert M., Mousavi, Mohammad Reza, Tyukin, Ivan Y.

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

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

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

“… mechanism to handle strict input requirements, and a reinforcement learning-based command generator selection…”
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AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL by Stennett, Tyler, Kim, Myeongsoo, Sinha, Saurabh, Orso, Alessandro

“…) with Multi-Agent Reinforcement Learning (MARL) and large language models (LLMs) for effective REST API testing…”
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Location is Key: Leveraging LLM for Functional Bug Localization in Verilog Design by Yao, Bingkun, Wang, Ning, Zhou, Jie, Wang, Xi, Gao, Hong, Jiang, Zhe, Guan, Nan

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

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

“…The development process for reinforcement learning applications is still exploratory rather than systematic…”
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Reward Augmentation in Reinforcement Learning for Testing Distributed Systems by Borgarelli, Andrea, Enea, Constantin, Majumdar, Rupak, Nagendra, Srinidhi

“… We describe a randomized testing approach for distributed protocol implementations based on reinforcement learning…”
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A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs by Kim, Myeongsoo, Stennett, Tyler, Sinha, Saurabh, Orso, Alessandro

“… a dependency-embedded multi-agent approach for REST API testing that integrates multi-agent reinforcement learning (MARL…”
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