{"title":"半结构化软件工程数据基于概念探索的通用框架","authors":"Gillian J. Greene","doi":"10.1109/ASE.2015.34","DOIUrl":null,"url":null,"abstract":"Software engineering meta-data (SE data), such as revision control data, Github project data or test reports, is typically semi-structured, it comprises a mixture of formatted and free-text fields and is often self-describing. Semi-structured SE data cannot be queried in a SQL-like manner because of its lack of structure. Consequently, there are a variety of customized tools built to analyze specific datasets but these do not generalize. We propose to develop a generic framework for exploration and querying of semi-structured SE data. Our approach investigates the use of a formal concept lattice as a universal data structure and a tag cloud as an intuitive interface to support data exploration.","PeriodicalId":6586,"journal":{"name":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"41 1","pages":"894-897"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A Generic Framework for Concept-Based Exploration of Semi-Structured Software Engineering Data\",\"authors\":\"Gillian J. Greene\",\"doi\":\"10.1109/ASE.2015.34\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Software engineering meta-data (SE data), such as revision control data, Github project data or test reports, is typically semi-structured, it comprises a mixture of formatted and free-text fields and is often self-describing. Semi-structured SE data cannot be queried in a SQL-like manner because of its lack of structure. Consequently, there are a variety of customized tools built to analyze specific datasets but these do not generalize. We propose to develop a generic framework for exploration and querying of semi-structured SE data. Our approach investigates the use of a formal concept lattice as a universal data structure and a tag cloud as an intuitive interface to support data exploration.\",\"PeriodicalId\":6586,\"journal\":{\"name\":\"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"volume\":\"41 1\",\"pages\":\"894-897\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ASE.2015.34\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASE.2015.34","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Generic Framework for Concept-Based Exploration of Semi-Structured Software Engineering Data
Software engineering meta-data (SE data), such as revision control data, Github project data or test reports, is typically semi-structured, it comprises a mixture of formatted and free-text fields and is often self-describing. Semi-structured SE data cannot be queried in a SQL-like manner because of its lack of structure. Consequently, there are a variety of customized tools built to analyze specific datasets but these do not generalize. We propose to develop a generic framework for exploration and querying of semi-structured SE data. Our approach investigates the use of a formal concept lattice as a universal data structure and a tag cloud as an intuitive interface to support data exploration.
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