H. Ramampiaro, D. Cruzes, R. Conradi, Manoel G. Mendonça
{"title":"Supporting evidence-based Software Engineering with collaborative information retrieval","authors":"H. Ramampiaro, D. Cruzes, R. Conradi, Manoel G. Mendonça","doi":"10.4108/ICST.COLLABORATECOM.2010.9","DOIUrl":null,"url":null,"abstract":"The number of scientific publications is constantly increasing, and the results published on Empirical Software Engineering are growing even faster. Some software engineering publishers have began to collaborate with research groups to make available repositories of software engineering empirical data. However, these initiatives are limited due to issues related to the available search tools. As a result, many researchers in the area have adopted a semi-automated approach for performing searches for systematic reviews as a mean to extract empirical evidence from published material. This makes this activity labor intensive and error prone. In this paper, we argue that the use of techniques from information retrieval, as well as text mining, can support systematic reviews and improve the creation of repositories of SE empirical evidence.","PeriodicalId":354101,"journal":{"name":"6th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2010)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"6th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/ICST.COLLABORATECOM.2010.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
The number of scientific publications is constantly increasing, and the results published on Empirical Software Engineering are growing even faster. Some software engineering publishers have began to collaborate with research groups to make available repositories of software engineering empirical data. However, these initiatives are limited due to issues related to the available search tools. As a result, many researchers in the area have adopted a semi-automated approach for performing searches for systematic reviews as a mean to extract empirical evidence from published material. This makes this activity labor intensive and error prone. In this paper, we argue that the use of techniques from information retrieval, as well as text mining, can support systematic reviews and improve the creation of repositories of SE empirical evidence.