从需求出发的在线测试合成:用博弈论加强强化学习

Ocan SankurDEVINE, UR, Thierry JéronDEVINE, UR, Nicolas MarkeyDEVINE, UR, David MentréMERCE-France, Reiya Noguchi
{"title":"从需求出发的在线测试合成:用博弈论加强强化学习","authors":"Ocan SankurDEVINE, UR, Thierry JéronDEVINE, UR, Nicolas MarkeyDEVINE, UR, David MentréMERCE-France, Reiya Noguchi","doi":"arxiv-2407.18994","DOIUrl":null,"url":null,"abstract":"We consider the automatic online synthesis of black-box test cases from\nfunctional requirements specified as automata for reactive implementations. The\ngoal of the tester is to reach some given state, so as to satisfy a coverage\ncriterion, while monitoring the violation of the requirements. We develop an\napproach based on Monte Carlo Tree Search, which is a classical technique in\nreinforcement learning for efficiently selecting promising inputs. Seeing the\nautomata requirements as a game between the implementation and the tester, we\ndevelop a heuristic by biasing the search towards inputs that are promising in\nthis game. We experimentally show that our heuristic accelerates the\nconvergence of the Monte Carlo Tree Search algorithm, thus improving the\nperformance of testing.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory\",\"authors\":\"Ocan SankurDEVINE, UR, Thierry JéronDEVINE, UR, Nicolas MarkeyDEVINE, UR, David MentréMERCE-France, Reiya Noguchi\",\"doi\":\"arxiv-2407.18994\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the automatic online synthesis of black-box test cases from\\nfunctional requirements specified as automata for reactive implementations. The\\ngoal of the tester is to reach some given state, so as to satisfy a coverage\\ncriterion, while monitoring the violation of the requirements. We develop an\\napproach based on Monte Carlo Tree Search, which is a classical technique in\\nreinforcement learning for efficiently selecting promising inputs. Seeing the\\nautomata requirements as a game between the implementation and the tester, we\\ndevelop a heuristic by biasing the search towards inputs that are promising in\\nthis game. We experimentally show that our heuristic accelerates the\\nconvergence of the Monte Carlo Tree Search algorithm, thus improving the\\nperformance of testing.\",\"PeriodicalId\":501316,\"journal\":{\"name\":\"arXiv - CS - Computer Science and Game Theory\",\"volume\":\"45 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computer Science and Game Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2407.18994\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computer Science and Game Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.18994","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

我们考虑的是根据功能需求自动在线合成黑盒测试用例。测试人员的目标是达到某个给定的状态,以满足覆盖标准,同时监控对需求的违反情况。我们开发了一种基于蒙特卡洛树搜索的方法,它是强化学习中的一种经典技术,用于高效选择有前途的输入。我们将自动测试要求视为实现者和测试者之间的一场博弈,通过偏向于搜索在这场博弈中有希望的输入来开发启发式方法。实验表明,我们的启发式加速了蒙特卡洛树搜索算法的收敛,从而提高了测试性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory
We consider the automatic online synthesis of black-box test cases from functional requirements specified as automata for reactive implementations. The goal of the tester is to reach some given state, so as to satisfy a coverage criterion, while monitoring the violation of the requirements. We develop an approach based on Monte Carlo Tree Search, which is a classical technique in reinforcement learning for efficiently selecting promising inputs. Seeing the automata requirements as a game between the implementation and the tester, we develop a heuristic by biasing the search towards inputs that are promising in this game. We experimentally show that our heuristic accelerates the convergence of the Monte Carlo Tree Search algorithm, thus improving the performance of testing.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
相关文献
二甲双胍通过HDAC6和FoxO3a转录调控肌肉生长抑制素诱导肌肉萎缩
IF 8.9 1区 医学Journal of Cachexia, Sarcopenia and MusclePub Date : 2021-11-02 DOI: 10.1002/jcsm.12833
Min Ju Kang, Ji Wook Moon, Jung Ok Lee, Ji Hae Kim, Eun Jeong Jung, Su Jin Kim, Joo Yeon Oh, Sang Woo Wu, Pu Reum Lee, Sun Hwa Park, Hyeon Soo Kim
具有疾病敏感单倍型的非亲属供体脐带血移植后的1型糖尿病
IF 3.2 3区 医学Journal of Diabetes InvestigationPub Date : 2022-11-02 DOI: 10.1111/jdi.13939
Kensuke Matsumoto, Taisuke Matsuyama, Ritsu Sumiyoshi, Matsuo Takuji, Tadashi Yamamoto, Ryosuke Shirasaki, Haruko Tashiro
封面:蛋白质组学分析确定IRSp53和fastin是PRV输出和直接细胞-细胞传播的关键
IF 3.4 4区 生物学ProteomicsPub Date : 2019-12-02 DOI: 10.1002/pmic.201970201
Fei-Long Yu, Huan Miao, Jinjin Xia, Fan Jia, Huadong Wang, Fuqiang Xu, Lin Guo
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs Mechanism Design for Extending the Accessibility of Facilities Common revenue allocation in DMUs with two stages based on DEA cross-efficiency and cooperative game The common revenue allocation based on modified Shapley value and DEA cross-efficiency On Robustness to $k$-wise Independence of Optimal Bayesian Mechanisms
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1