{"title":"Teaching and Training for Software Analytics","authors":"D. Zhang, Yingnong Dang, Shi Han, Tao Xie","doi":"10.1109/CSEET.2012.14","DOIUrl":null,"url":null,"abstract":"Software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services. When applying analytic technologies in practice of software analytics, one should incorporate (1) a broad spectrum of domain knowledge and expertise, e.g., management, machine learning, large-scale data processing and computing, and information visualization; and (2) investigate how practitioners take actions on the produced information, and provide effective support for such information-based action taking. This tutorial instructs materials to equip participants with skills and knowledge of conducting software analytics along with teaching and training students and practitioners for software analytics in university or industrial settings.","PeriodicalId":385043,"journal":{"name":"2012 IEEE 25th Conference on Software Engineering Education and Training","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 25th Conference on Software Engineering Education and Training","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSEET.2012.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services. When applying analytic technologies in practice of software analytics, one should incorporate (1) a broad spectrum of domain knowledge and expertise, e.g., management, machine learning, large-scale data processing and computing, and information visualization; and (2) investigate how practitioners take actions on the produced information, and provide effective support for such information-based action taking. This tutorial instructs materials to equip participants with skills and knowledge of conducting software analytics along with teaching and training students and practitioners for software analytics in university or industrial settings.