Computer Science ›› 2020, Vol. 47 ›› Issue (6): 51-58.doi: 10.11896/jsjkx.190300140

• Intelligent Software Engineering • Previous Articles     Next Articles

Analysis of Open Source Software Cliff Walls for Group Collaborative Development

HE Peng1,2, YU Lv-jun1   

  1. 1 School of Computer Science and Information Engineering,Hubei University,Wuhan 430062,China
    2 Hubei Key Laboratory of Applied Mathematics,Wuhan 430062,China
  • Received:2019-03-27 Online:2020-06-15 Published:2020-06-10
  • About author:HE Peng,born in 1988,Ph.D,associate professor,master supervisor,is a member of China Computer Federation.His main research interests include software measure,software defect prediction,and service recommendationand complex network.
  • Supported by:
    This work was supported by the National Key R & D Program of China (2018YFB1003801),National Natural Science Foundation of China(61902114),Hubei Province Education Department Youth Talent Project(Q20171008) and Hubei Provincial Key Laboratory of Applied Mathematics(HBAM201901)

Abstract: Due to the characteristics of low threshold and high freedom,open source software encounters slow progress,low efficiency and low quality in the development process.Software cliff wall as a criterion of project robustness,indicates unexpected acceleration in common incremental development activities over short periods of time,which is a potential threat to the sustainable development in software evolution.Therefore,analyzing the causes of software cliff walls is an effective method to deeply understand the development process of open source projects,to more accurately describe the evolution of software,and to improve the efficiency of software development.The experiment firstly constructes a series of developer collaboration networks (DCNs) over more than 150 thousand commits from 9 GitHub projects by month and quarter respectively.This paper consideres a single commit of more than 10 000 lines of code as a software cliffs.And then it introduces 9 metrics,such as the number of nodes,the number of connected edges,the node update rate,the module degree,the average path length,the average degree,the node penetration index,the node out-of-mean,and the diversity,to analyze the relationship between DCN and cliff walls from the perspectives of network scale,network structure and network quality.The results show that:1)smaller development teams and greater member turnover tend to cause a cliff wall;2)‘small world’ features among developers is helpful to avoid the emergence of software cliff walls;3)the relationship between DCN and software cliffs in the software development process is more appropriate in a quarterly cycle,and the diversity of the development team will also affect the creation of cliff walls in software development.

Key words: Developer collaboration network, Group collective development, Open source software, Software cliff walls, Software evolution

CLC Number: 

  • TP301
[1]BROWN A W,BOOCH G.Reusing Open-Source Software and Practices:The Impact of Open-Source on Commercial Vendors[C]//International Conference on Software Reuse.2002:123-136.
[2]YANG B,YU Q,ZHANG W,et al.Influence Factors Correlation Analysis in Github Open Source Software Development Process[J].Journal of Software,2017,28(6):1330-1342.
[3]HE P,LI B,YANG X H,et al.Research On Developer Preferential Collaboration in Open-Source Software Community[J].Computer Science,2015,42(2):161-166.
[4]ZHOU M H.Looking for micro-process in large-scale data[C]//Proceedings of the 2nd International Workshop on Evidential Assessment of Software Technologies.New York:ACM,2012:39-42.
[5]KALLIAMVAKOU E,GOUSIOS G,BLINCOE K,et al.The Promises and Perils of Mining Github[C]//Proceedings of the 11th Working Conference on Mining Software Repositories.New York:ACM,2014:92-101.
[6]LI W P,WANG J B,LIN Z Q,et al.Software Knowledge Graph Building Method for Open Source Project[J].Journal of Frontiers of Computer Science & Technology,2017,11(6):851-862.
[7]JUNG H W,KIM S G,CHUNG C S.Measuring Software Product Quality:A Survey of ISO/IEC 9126[J].IEEE Software,2004,21(5):88-92.
[8]GIRBEA A,SUCIU C,NECHIFOR S,et al.Design and Implementation of a Service-Oriented Architecture for the Optimization of Industrial Applications[J].IEEE Transactions on Industrial Informatics,2014,10(1):185-196.
[9]MENEELY A,WILLIAMS L,SNIPES W,et al.Predicting Failures with Developer Networks and Social Network Analysis[C]//Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering.New York:ACM,2008:13-23.
[10]LIMAM N,BOUTABA R.Assessing Software Service Quality and Trustworthiness at Selection Time[J].IEEE Transactions on Software Engineering,2010,36(4):559-574.
[11]MACLEAN A C,PRATT L J,KREIN J L,et al.Trends that Affect Temporal Analysis using Sourceforge data[C]//Proceedings of the 5th International Workshop on Public Data about Software Development.North Carolina,USA,2010:6-11.
[12]MACLEAN A C.Commit Patterns and Threats to Validity in Analysis of Open Source Software Repositories[D].Utah:Brigham Young University,2012.
[13]PRATT L J.Cliff Walls:Threats to Validity in Empirical Studies of Open Source Forges[D].Utah:Brigham Young University,2013.
[14]CHENG C,LI B,LI Z Y,et al.Developer Role Evolution in Open Source Software Ecosystem:An Explanatory Study on GNOME[J].Journal of Computer Science and Technology,2017,32(2):396-414.
[15]PRATT L J,MACLEAN A C,KNUTSON C D,et al.Cliff Walls:An Analysis of Monolithic Commits Using Latent Dirichlet Allocation[C]//IFIP International Conference on Open Source Systems.Springer,2011:282-298.
[16]CHEN D,WANG X,HE P,et al.Towards Understanding Existing Developers’ Collaborative Behavior In OSS Communities[J].ComputerScience,2016,43(6A):476-479.
[17]GRÖNLUND M,JEFFORD-BAKER J.Measuring correlation between commit frequency and popularity on GitHub[D].Stockholm:KTH Royal Institute of Technology,2017.
[18]SINHA V S,MANI S,SINHA S.Entering the circle of trust:developer initiation as committers in open-source projects[C]//Proceedings of the 8th Working Conference on Mining Software Repositories.2011:133-142.
[19]MA Y T,WU Y,XU Y W.Dynamics of open-source software developer's commit behavior[C]//Proceedings of the 29th Annual ACM Symposium on Applied Computing(SAC’14).New York,USA:ACM Press,2014:1171-1173.
[20]GOUSIOS G.The GHTorent dataset and tool suite[C]//Proceedings of the 10th Working Conference on Mining Software Repositories.2013:233-236.
[21]Struggling in IT.GitHub 2018 Annual Report[EB/OL].http://www.wh-ford.com/f828820/20181030A1WJZ800.html.
[22]HINDLE A,GERMAN D M,HOLT R C,et al.Automatic Classification of Large Changes into Maintenance Categories[C]//IEEE International Conference on Program Comprehension.IEEE,2009:99-108.
[23]ARAFAT O,RIEHLE D.The Commit Size Distribution of Open Source Software[C]//Hawaii International Conference on System Science.IEEE,2009:1-8.
[24]GU Q,CHEN D X.Validation and Simulation of Software System Evolution Rules Using Software Networks[J].Scientia Sinica Informationis,2014,44(1):20-36.
[25]GU Q,XIONG S J,CHEN D X.Correlations between characteristics of maximum influence and degree distributions in software networks[J].SCIENCE CHINA Information Sciences,2014,57(7):1-12.
[26]HE P,WANG P,LI B,et al.An Evolution Analysis of Software System Based On Multi-Granularity Software Network[J].Acta Electronica Sinica,2018,46(2):257-267.
[27]PAN W F,LI B,MA Y T,et al.Multi-Granularity Evolution Analysis of Software Using Complex Network Theory[J].Journal of Systems Science and Complexity,2011,24(6):1068-1082.
[28]NEWMAN M E J.Fast Algorithm for Detecting Community Structure in Networks[J].Physical Review E,2003,69(6):066133.
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