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大数据背景下软件工程评估的利弊 .pdf

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1、Big Data for Software Engineering Estimation:Pros and ConsSrinivasa Gopal Ramanujan Society for Academic Research and Promotion of ScienceAbout the AuthorEducation:B.Tech in Mech Engg,Indian Institute of Technology,1990MS in Industrial Systems Engineering,University of Regina,Sask,Canada 1992MS in I

2、nformation Technology,International Institute of Information Technology,Bengaluru,2013Awards:Governor General of Canada Gold Medal-1992Work Experience :Work Experience :Mainly in IT and Quality Assurance in multinational companies such as Infosys,Emirates Airlines,Unisys Corp,Land Mark IT,MasTech,GA

3、VS Info ServicesCo-Founder Ramanujan Society for Academic Research and Promotion of ScienceSrinivasa GopalSrinivasa GopalWhat is Software Engineering Estimation?Software engineering estimation involves predicting effort,schedule,and manpower needed for a successful software project.Crucial for devel

4、opment,maintenance,and support,estimates guide budgets and planning.Outputs include effort(measured in person-hours),schedule(time required),and manpower.Accurate estimation is vital for effective project management,guiding project scope and resource allocation.StartDefine Project ScopeGather Estima

5、tion DataSelect Estimation MethodEstimate Effort ,Schedule and ManpowerReview and AdjustFinalize EstimatesEndWhat are different types of estimation models?Estimation methods in software engineering include analogy-based models,comparing the current project to past similar ones using historical data

6、such as the ISBSG project data set;parameter-based models,using statistical analysis based on software size,complexity,and team experience;size-based models,estimating effort from code size metrics;expert-based models,relying on experts judgment;and group-based models,employing collective estimation

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本文探讨了软件工程估计的重要性,方法,挑战以及如何利用大数据提高估计的准确性。软件工程估计涉及预测成功软件项目所需的努力,时间表和人力。准确估计对于项目开发,维护和支持至关重要。文章提到了各种估计模型,包括基于类比,参数,大小,专家和集体估计方法的模型,以及混合模型的存在。挑战部分指出,软件项目经常遇到由于不清晰或不断变化的要求而导致的延迟。估计模型依赖于历史数据,如ISBSG项目数据集,但过时的数据会降低准确性。文章还讨论了如何通过清晰的要求,更新的数据,适当的技巧,有效的范围管理,开放的沟通和持续的估计审查来提高准确性。大数据在软件工程估计中的应用被详细讨论,如何通过识别过去项目的模式来提高准确性,预测所需的努力。最后,文章提到了几个提供软件估计研究数据的公共仓库,并讨论了大数据在项目估计中的作用,挑战和好处。
"软件工程估算的利与弊有哪些?" "不同的估算模型有哪些特点和挑战?" "如何利用大数据提高软件工程估算的准确性?"
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