基于遗传规划的集成分类器链的社区气味检测

Nuri Almarimi, Ali Ouni, Moataz Chouchen, Islem Saidani, Mohamed Wiem Mkaouer
{"title":"基于遗传规划的集成分类器链的社区气味检测","authors":"Nuri Almarimi, Ali Ouni, Moataz Chouchen, Islem Saidani, Mohamed Wiem Mkaouer","doi":"10.1145/3372787.3390439","DOIUrl":null,"url":null,"abstract":"Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.CCS CONCEPTS• Software and its engineering → Software organization and properties.","PeriodicalId":313953,"journal":{"name":"2020 ACM/IEEE 15th International Conference on Global Software Engineering (ICGSE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"On the Detection of Community Smells Using Genetic Programming-based Ensemble Classifier Chain\",\"authors\":\"Nuri Almarimi, Ali Ouni, Moataz Chouchen, Islem Saidani, Mohamed Wiem Mkaouer\",\"doi\":\"10.1145/3372787.3390439\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.CCS CONCEPTS• Software and its engineering → Software organization and properties.\",\"PeriodicalId\":313953,\"journal\":{\"name\":\"2020 ACM/IEEE 15th International Conference on Global Software Engineering (ICGSE)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 ACM/IEEE 15th International Conference on Global Software Engineering (ICGSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3372787.3390439\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 ACM/IEEE 15th International Conference on Global Software Engineering (ICGSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3372787.3390439","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 23

摘要

社区气味是软件开发社区中组织和社会问题的症状,通常会增加项目成本并影响软件质量。最近的研究已经确定了各种各样的社区气味,并将它们定义为与软件开发社区中的组织社会结构相关的次优模式,例如缺乏沟通、协调和协作。认识到在软件项目中早期检测潜在社区气味的优势,我们引入了一种新的方法,该方法从各种社区组织和社会实践中学习,为检测社区气味提供自动化支持。特别是,我们的方法从一组交织的组织-社会症状中学习,这些症状表征了软件项目中存在的社区气味实例。我们建立了一个多标签学习模型来检测8种常见的社区气味。我们使用集成分类器链(ECC)模型将多标签问题转化为多个单标签问题,并使用遗传规划(GP)求解,以找到每种气味类型的最优检测规则。为了评估我们的方法的性能,我们对103个开源项目和407个社区气味实例的基准进行了实证研究。结果的统计测试表明,我们的方法可以检测到八种考虑的气味类型,平均f值为89%,与其他最先进的技术相比,取得了更好的性能。此外,我们发现最能表征社区气味的影响因素包括社交网络密度和亲密度中心性,以及每个时区和每个社区的开发者数量的标准差。•软件及其工程→软件组织和属性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Detection of Community Smells Using Genetic Programming-based Ensemble Classifier Chain
Community smells are symptoms of organizational and social issues within the software development community that often increase the project costs and impact software quality. Recent studies have identified a variety of community smells and defined them as sub-optimal patterns connected to organizational-social structures in the software development community such as the lack of communication, coordination and collaboration. Recognizing the advantages of the early detection of potential community smells in a software project, we introduce a novel approach that learns from various community organizational and social practices to provide an auto-mated support for detecting community smells. In particular, our approach learns from a set of interleaving organizational-social symptoms that characterize the existence of community smell in-stances in a software project. We build a multi-label learning model to detect 8 common types of community smells. We use the ensemble classifier chain (ECC) model that transforms multi-label problems into several single-label problems which are solved using genetic programming (GP) to find the optimal detection rules for each smell type. To evaluate the performance of our approach, we conducted an empirical study on a benchmark of 103 open source projects and 407 community smell instances. The statistical tests of our results show that our approach can detect the eight considered smell types with an average F-measure of 89% achieving a better performance compared to different state-of-the-art techniques. Furthermore, we found that the most influential factors that best characterize community smells include the social network density and closeness centrality as well as the standard deviation of the number of developers per time zone and per community.CCS CONCEPTS• Software and its engineering → Software organization and properties.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信