On the impact of crosscutting concern projection on code measurement

Eduardo Figueiredo, Alessandro F. Garcia, M. Maia, G. Ferreira, Camila Nunes, J. Whittle
{"title":"On the impact of crosscutting concern projection on code measurement","authors":"Eduardo Figueiredo, Alessandro F. Garcia, M. Maia, G. Ferreira, Camila Nunes, J. Whittle","doi":"10.1145/1960275.1960287","DOIUrl":null,"url":null,"abstract":"Many concern metrics have been defined to quantify properties of crosscutting concerns, such as scattering, tangling, and dedication. To quantify these properties, concern metrics directly rely on the projection (assignment) of concerns into source code. Although concern identification tools have emerged over the last years, they are still rarely used in practice to support concern projection and, therefore, it is a task often performed manually. This means that the results of concern metrics are likely to be influenced by how accurately programmers assign concerns to code elements. Even though concern assignment is an important and long-standing problem in software engineering, its impact on accurate measures of crosscutting concerns has never been studied and quantified. This paper presents a series of 5 controlled experiments to quantify and analyse the impact of concern projection on crosscutting concern measures. A set of 80 participants from 4 different institutions projected 10 concern instances into the source code of two software systems. We analyse the accuracy of concern projections independently made by developers, and their impact on a set of 12 concern metrics. Our results suggest that: (i) programmers are conservative when projecting crosscutting concerns, (ii) all concern metrics suffer with such conservative behaviour, and (iii) fine-grained tangling measures are more sensitive to different concern projections than coarse-grained scattering metrics.","PeriodicalId":353153,"journal":{"name":"Aspect-Oriented Software Development","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aspect-Oriented Software Development","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1960275.1960287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23

Abstract

Many concern metrics have been defined to quantify properties of crosscutting concerns, such as scattering, tangling, and dedication. To quantify these properties, concern metrics directly rely on the projection (assignment) of concerns into source code. Although concern identification tools have emerged over the last years, they are still rarely used in practice to support concern projection and, therefore, it is a task often performed manually. This means that the results of concern metrics are likely to be influenced by how accurately programmers assign concerns to code elements. Even though concern assignment is an important and long-standing problem in software engineering, its impact on accurate measures of crosscutting concerns has never been studied and quantified. This paper presents a series of 5 controlled experiments to quantify and analyse the impact of concern projection on crosscutting concern measures. A set of 80 participants from 4 different institutions projected 10 concern instances into the source code of two software systems. We analyse the accuracy of concern projections independently made by developers, and their impact on a set of 12 concern metrics. Our results suggest that: (i) programmers are conservative when projecting crosscutting concerns, (ii) all concern metrics suffer with such conservative behaviour, and (iii) fine-grained tangling measures are more sensitive to different concern projections than coarse-grained scattering metrics.
横切关注点投影对代码度量的影响
已经定义了许多关注度量来量化横切关注的属性,例如分散、缠结和奉献。为了量化这些属性,关注度量直接依赖于关注在源代码中的投影(分配)。尽管关注点识别工具在过去的几年中出现了,但是它们在实践中仍然很少被用于支持关注点投影,因此,它通常是手动执行的任务。这意味着关注度量的结果很可能受到程序员如何准确地将关注分配给代码元素的影响。尽管关注点分配是软件工程中一个重要且长期存在的问题,但它对横切关注点的精确度量的影响从未被研究和量化过。本文提出了一系列5个对照实验来量化和分析关注投影对横切关注度量的影响。来自4个不同机构的80名参与者将10个关注实例投射到两个软件系统的源代码中。我们分析了由开发人员独立做出的关注预测的准确性,以及它们对一组12个关注度量的影响。我们的结果表明:(i)程序员在投射横切关注点时是保守的,(ii)所有的关注点度量都受到这种保守行为的影响,以及(iii)细粒度纠缠度量比粗粒度散射度量对不同的关注点预测更敏感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信