1. 软件开发环境国家重点实验室(北京航空航天大学),北京 100191
2. 北京航空航天大学大数据科学与脑机智能高精尖创新中心,北京 100191
3. 北京航空航天大学计算机学院,北京 100191
[ "张建(1994- ),男,北京航空航天大学计算机学院博士生,主要研究方向为软件工程、源代码分析、自然语言理" ]
[ "孟祥鑫(1995- ),男,北京航空航天大学计算机学院博士生,主要研究方向为基于模板的程序自动修复与基于度学习的程序自动修复" ]
[ "孙海龙(1979- ),男,博士,北京航空航天大学计算机学院教授、博士生导师,主要研究方向为智能软件工程、体智能和分布式系统" ]
[ "王旭(1986- ),男,博士,北京航空航天大学计算机学院讲师,主要研究方向为基于大数据的软件分析和智能化开发" ]
[ "刘旭东(1965- ),男,博士,北京航空航天大学计算机学院教授、博士生导师,北京航空航天大学计算机学院计算机新技术研究所所长,可信网络计算技术国防重点学科实验室主任,主要研究方向为网络化软件开发方法、可信软件技术、软件中间件技术和信息化标准" ]
网络首发:2021-01,
纸质出版:2021-01-15
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张建, 孟祥鑫, 孙海龙, 等. 数据驱动的软件开发者智能协作技术[J]. 大数据, 2021,7(1):2021006-1.
Jian ZHANG, Xiangxin MENG, Hailong SUN, et al. Data driven intelligent collaboration of software developers[J]. Big Data Research, 2021, 7(1): 2021006-1.
张建, 孟祥鑫, 孙海龙, 等. 数据驱动的软件开发者智能协作技术[J]. 大数据, 2021,7(1):2021006-1. DOI: 10.11959/j.issn.2096-0271.2021006.
Jian ZHANG, Xiangxin MENG, Hailong SUN, et al. Data driven intelligent collaboration of software developers[J]. Big Data Research, 2021, 7(1): 2021006-1. DOI: 10.11959/j.issn.2096-0271.2021006.
通过挖掘并利用软件大数据中蕴含的知识来提高软件开发的智能化水平已成为软件工程领域的热点研究问题。然而,对软件开发者及其群体协作方法的研究尚未形成系统化的研究成果。针对此问题,以开发者群体为研究对象,通过深入分析开发者的行为历史数据,研究面向智能协作的关键技术,并以此为基础研制相应的支撑环境。首先,收集并分析了海量的开发者相关数据;第二,给出了软件开发者能力特征模型及其协作关系模型,并构建了开发者知识图谱;第三,以开发者知识图谱为支撑,阐述了基于智能推荐的协作开发方法。基于以上关键技术,研发了相应的支撑工具,并构建了智能协作开发环境系统;最后,对未来的工作进行了展望。
Mining big software data and utilizing the knowledge contained in it to explore intelligent methods for software development is an active research topic. However
existing researches on software developer and crowd collaboration have not yet formed systematic methods. Therefore
the key technologies for intelligent collaboration through in-depth analysis of developer behavior were studied. Besides
the corresponding support environment was also developed on the basis of the key technologies to improve the efficiency and quality of software development. Firstly
a large amount of data related to developers were collected and analyzed. Secondly
a systematic approach of analyzing developers and their collaboration which is called developer knowledge graph was proposed. Thirdly
supported by the developer knowledge graph
the collaborative development method based on intelligent recommendation was introduced thoroughly. Depending on the above technologies
the corresponding supporting tools were developed
and a system of intelligent collaborative development environment was provided. Finally
the future work was prospected.
THONGTANUNAM P , TANTITHAMTHAVORN C , KULA R G , et al . Who should review my code? A file location-based codereviewer recommendation approach for modern code review [C ] // 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering . Piscataway: IEEE Press , 2015 : 141 - 150 .
BROOKSF P , et al . The mythical man-month (anniversary ed.) [C ] // Boston: AddisonWesley Longman Publishing Co., Inc . 1995 .
RAHMAN M M , ROY C K , REDL J , et al . CORRECT: code reviewer recommendation at GitHub for Vendasta technologies [C ] // The 31st IEEE/ACM International Conference on Automated Software Engineering . Piscataway: IEEE Press , 2016 : 792 - 797 .
ASTHANA S , KUMAR R , BHAGWAN R , et al . WhoDo: automating reviewer suggestions at scale [C ] // The 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering . New York: ACM Press , 2019 : 937 - 945 .
LIU H B , QIAO M , GREENIA D , et al . A machine learning approach to combining individual strength and team features for team recommendation [C ] // 2014 13th International Conference on Machine Learning and Applications . Piscataway:IEEE Press , 2014 : 213 - 218 .
SAPIENZA A , GOYAL P , FERRARA E . Deep neural networks for optimal team composition [J ] . Frontiers in Big Data , 2019 , 2 : 14 .
GAO D W , TONG Y X , SHE J Y , et al . Top-k team recommendation and its variants in spatial crowdsourcing [J ] . Data Science and Engineering , 2017 , 2 ( 2 ): 136 - 150 .
NGUYEN A T , HILTON M , CODOBAN M , et al . API code recommendation using statistical learning from finegrained changes [C ] // The 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering . New York: ACM Press , 2016 : 511 - 522 .
LUAN S F , YANG D , BARNABY C , et al . Aroma:code recommendation via structural code search [J ] . Proceedings of the ACM on Programming Languages , 2019 , 3 ( OOPSLA ): 1 - 28 .
SVYATKOVSKIY A , ZHAO Y , FU S Y , et al . Pythia: ai-assisted code completion system [C ] // The 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . New York: ACM Press , 2019 : 2727 - 2735 .
ZHANG X D , ZHU C G , LI Y , et al . Precfix:large-scale patch recommendation by mining defect-patch pairs [C ] // The ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice . New York: ACM Press , 2020 : 41 - 50 .
DEMARCO T , LISTER T . Peopleware:productive projects and teams [M ] . New Jersey : Addison-Wesley , 2013 .
JONES C . Programming productivity [M ] . New York : McGraw-Hill, Inc. , 1985 .
BOYD D M , ELLISON N B . Social network sites: definition, history, and scholarship [J ] . Journal of Computer‐Mediated Communication , 2007 , 13 ( 1 ): 210 - 230 .
MENEELY A , WILLIAMS L , SNIPES W , et al . Predicting failures with developer networks and social network analysis [C ] // The 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering . New York: ACM Press , 2008 : 13 - 23 .
WOLF T , SCHROTER A , DAMIAN D , et al . Predicting build failures using social network analysis on developer communication [C ] // The 31st International Conference on Software Engineering . Piscataway: IEEE Press , 2009 : 1 - 11 .
JERMAKOVICS A , SILLITTI A , SUCCI G . Mining and visualizing developer networks from version control systems [C ] // The 4th International Workshop on Cooperative and Human Aspects of Software Engineering . New York: ACM Press , 2011 : 24 - 31 .
CAGLAYAN B , BENER A B , MIRANSKYY A , et al . Emergence of developer teams in the collaboration network [C ] // 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering . Piscataway: IEEE Press , 2013 : 33 - 40 .
JOBLIN M , APEL S , HUNSEN C , et al . Classifying developers into core and peripheral: an empirical study on count and network metrics [C ] // The 39th International Conference on Software Engineering . Piscataway: IEEE Press , 2017 : 164 - 174 .
SINDHGATTA R . Identifying domain expertise of developers from source code [C ] // The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2008 : 981 - 989 .
MATTER D , KUHN A , NIERSTRASZ O , et al . Assigning bug reports using a vocabulary-based expertise model of developers [C ] // The 6th IEEE International Working Conference on Mining Software Repositories . Piscataway: IEEE Press , 2009 : 131 - 140 .
TEYTON C , PALYART M , FALLERI J R , et al . Automatic extraction of developer expertise [C ] // The 18th International Conference on Evaluation and Assessment in Software Engineering . New York: ACM Press , 2014 : 8 .
WANG Z Z , SUN H L , FU Y , et al . Recommending crowdsourced software developers in consideration of skill improvement [C ] // 2017 32nd IEEE/ACM International Conference on Automated Software Engineering . Piscataway: IEEE Press , 2017 : 717 - 722 .
WANG Z Z , SUN H L , HAN T . Predicting crowdsourcing worker performance with knowledge tracing [C ] // International Conference on Knowledge Science, Engineering and Management . Cham:Springer , 2020 : 352 - 359 .
WANG J , MENG X X , WANG H M , et al . An online developer profiling tool based on analysis of GitLab repositories [C ] // CCF Conference on Computer Supported Cooperative Work and Social Computing . Singapore: Springer , 2019 : 408 - 417 .
DING J , SUN H L , WANG X , et al . Entity-level sentiment analysis of issue comments [C ] // The 3rd International Workshop on Emotion Awareness in Software Engineering . New York: ACM Press , 2018 : 7 - 13 .
YAN J F , SUN H L , WANG X , et al . Profiling developer expertise across software communities with heterogeneous information network analysis [C ] // The 10th Asia-Pacific Symposium on Internetware . New York: ACM Press , 2018 : 1 - 9 .
SHAO B , YAN J F . Recommending answerers for stack overflow with LDA model [C ] // The 12th Chinese Conference on Computer Supported Cooperative Work and Social Computing . New York: ACM Press , 2017 : 80 - 86 .
XIA Z L , SUN H L , JIANG J , et al . A hybrid approach to code reviewer recommendation with collaborative filtering [C ] // 2017 6th International Workshop on Software Mining . Piscataway: IEEE Press , 2017 : 24 - 31 .
FU Y , SUN H L , YE L T , et al . Competitionaware task routing for contest based crowdsourced software development [C ] // 2017 6th International Workshop on Software Mining . Piscataway: IEEE Press , 2017 : 32 - 39 .
ZHANG Z Y , SUN H L , ZHANG H Y . Developer recommendation for Topcoder through a meta-learning based policy model [J ] . Empirical Software Engineering , 2019 , 25 ( 1 ): 1 - 31 .
YE L T , SUN H L , WANG X , et al . Personalized teammate recommendation for crowdsourced software developers [C ] // The 33rd ACM/IEEE International Conference on Automated Software Engineering . New York: ACM Press , 2018 : 808 - 813 .
SUNF M , WANGX , SUNH L , et al . Recommendflow: use topic model to automatically recommend stack overflow Q&A in IDE [C ] // International Conference on Collaborative Computing: Networking, Applications and Worksharing . Cham:Springer , 2016 : 521 - 526 .
TIAN Y F , WANG X , SUN H L , et al . Automatically generating API usage patterns from natural language queries [C ] // 2018 25th Asia-Pacific Software Engineering Conference . Piscataway: IEEE Press , 2018 : 59 - 68 .
ZHANG J , SUN H L , TIAN Y F , et al . Poster:semantically enhanced tag recommendation for software CQAs via deep learning [C ] // 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSECompanion) . Piscataway: IEEE Press , 2018 : 294 - 295 .
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