{"title":"强化学习在需求工程中的应用:需求跟踪","authors":"Hakim Sultanov, J. Hayes","doi":"10.1109/RE.2013.6636705","DOIUrl":null,"url":null,"abstract":"We posit that machine learning can be applied to effectively address requirements engineering problems. Specifically, we present a requirements traceability method based on the machine learning technique Reinforcement Learning (RL). The RL method demonstrates a rather targeted generation of candidate links between textual requirements artifacts (high level requirements traced to low level requirements, for example). The technique has been validated using two real-world datasets from two problem domains. Our technique demonstrated statistically significant better results than the Information Retrieval technique.","PeriodicalId":6342,"journal":{"name":"2013 21st IEEE International Requirements Engineering Conference (RE)","volume":"47 1","pages":"52-61"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"43","resultStr":"{\"title\":\"Application of reinforcement learning to requirements engineering: requirements tracing\",\"authors\":\"Hakim Sultanov, J. Hayes\",\"doi\":\"10.1109/RE.2013.6636705\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We posit that machine learning can be applied to effectively address requirements engineering problems. Specifically, we present a requirements traceability method based on the machine learning technique Reinforcement Learning (RL). The RL method demonstrates a rather targeted generation of candidate links between textual requirements artifacts (high level requirements traced to low level requirements, for example). The technique has been validated using two real-world datasets from two problem domains. Our technique demonstrated statistically significant better results than the Information Retrieval technique.\",\"PeriodicalId\":6342,\"journal\":{\"name\":\"2013 21st IEEE International Requirements Engineering Conference (RE)\",\"volume\":\"47 1\",\"pages\":\"52-61\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"43\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 21st IEEE International Requirements Engineering Conference (RE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RE.2013.6636705\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 21st IEEE International Requirements Engineering Conference (RE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RE.2013.6636705","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of reinforcement learning to requirements engineering: requirements tracing
We posit that machine learning can be applied to effectively address requirements engineering problems. Specifically, we present a requirements traceability method based on the machine learning technique Reinforcement Learning (RL). The RL method demonstrates a rather targeted generation of candidate links between textual requirements artifacts (high level requirements traced to low level requirements, for example). The technique has been validated using two real-world datasets from two problem domains. Our technique demonstrated statistically significant better results than the Information Retrieval technique.