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Joint Effect of Team Structure and Software Architecture in OSS

Mengru Zhao 7482645 Yiyi Dai 7067041. Joint Effect of Team Structure and Software Architecture in OSS. submitted to Professor Shervin Shirmohammadi in partial fulfillment of the requirements for the course ELG 5100. Introduction. What is OSS? Open source software Why OSS?

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Joint Effect of Team Structure and Software Architecture in OSS

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  1. Mengru Zhao 7482645 Yiyi Dai 7067041 Joint Effect of Team Structure and Software Architecture in OSS submitted to Professor Shervin Shirmohammadi in partial fulfillment of the requirements for the course ELG 5100

  2. Introduction • What is OSS? Open source software • Why OSS? • Self-organized • Allow a clear view of inner workings • How OSS? Coherent research Team structure - social Software architecture - technical Mengru Zhao 7482645 Yiyi Dai 7067041 slide 2

  3. Agenda • Background • Hypotheses • Data Analysis and Result • Conclusion • Limitations and Future Research • Reference Mengru Zhao 7482645 Yiyi Dai 7067041 slide 3

  4. Background • Role of Social & Technical “match” Inseparable role of organizational structure--Social task characteristics--Technical • Moderating effect of task characteristics on the causal pathway from organizational structure to work Performance. • Organizational structure may moderate the effect of task characteristics on work performance due to the endogeneityof software development task characteristics. • Equally important and mutually moderating roles Mengru Zhao 7482645 Yiyi Dai 7067041 slide 4

  5. Background • Socio-Technical • Social-Development Team Structure Each collaboration network includes developers of a single OSS project as nodes and collaboration incidences on tasks of the same project as links Intraprojectnetwork is more relevant The basic unit of work is a file in the OSS CVS system (concurrent versioning system) Two commonly used measures: network size & network centralization Mengru Zhao 7482645 Yiyi Dai 7067041 slide 5

  6. Background 2) Technical-Software Architecture Structural interdependency: The strength of association established by a connection from one module to another. Past development activities Mengru Zhao 7482645 Yiyi Dai 7067041 slide 6

  7. Background • Interaction Between Team Structure and Software Architecture Reters to the mutually moderating or mutually contingent relationship between team structure and software architecture in OSS development. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 7

  8. Hypotheses • Hypotheses 1 Collaboration network size and software structural interdependency mutually and positively moderate each other’s impact on OSS project performance. Software structural interdependency is higher impact of network size on project performance is more likely to be positive. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 8

  9. Hypotheses • Hypotheses 2 Collaboration network centralization and software structural interdependency mutually and positively moderate each other’s impact on OSS project performance. Software structural interdependency is higher impact of network centralization on project performance is more likely to be positive Tendency for software structural interdependency to negatively affect can be strong in a team with a chain-like structure. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 9

  10. Data Analysis and Result • Data Collection Three key considerations • Detailed CVS information in the public domain; • Between Feb 2001 and Nov 2001; • At least ten developersin the collaboration network. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 10

  11. Data Analysis and Result • Construct Operationalization • Collaboration Network Structure • The higher the value, the more centralized the network is. • Software Structural Interdependency • The average number of source code task files (CVS files) per folder in the CVS file tree, to measure software structural interdependency (SSI). • Capture both the total number of CVS files in an OSS project and CVS tree structure of the same project. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 11

  12. Data Analysis and Result • OSS Development Performance • We choose the number of code commits per developer per day as our measure of OSS development performance. • Objective Mengru Zhao 7482645 Yiyi Dai 7067041 slide 12

  13. Data Analysis and Result • Control Variables • Product Size: LOC • Programming Language: 4 binary variances • License Type: GPL=1, others=0 Mengru Zhao 7482645 Yiyi Dai 7067041 slide 13

  14. Data Analysis and Result • Analysis Nonlinear relationship between project performance and network size and product size. • Results The ordinary least squares (OLS) Mengru Zhao 7482645 Yiyi Dai 7067041 slide 14

  15. Data Analysis and Result Mengru Zhao 7482645 Yiyi Dai 7067041 slide 15

  16. Data Analysis and Result Mengru Zhao 7482645 Yiyi Dai 7067041 slide 16

  17. Data Analysis and Result H1 results Fig. 1. Simple Slope of Network Size Fig. 2. Simple Slope of SSI Mengru Zhao 7482645 Yiyi Dai 7067041 slide 17

  18. Data Analysis and Result H2 results Fig. 3. Simple Slope of Centralization Fig. 4. Simple Slope of SSI Mengru Zhao 7482645 Yiyi Dai 7067041 slide 18

  19. Conclusion • Research implication • IT properties and organization properties as equally important and tightly intertwined factors in work performance outcomes. • Conceptualize and empirically evaluate the role of socio-technical interactions in OSS projects. • Practical Implication • Gain the best of both worlds by adopting a hybrid software development process that incorporates strengths of traditional software development model and OSS model. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 19

  20. Conclusion • Self-organized OSS development process jointly enacted by developer behaviors and project characteristics. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 20

  21. Limitations and Future Research • Gain analytical tractability, we employ a surrogate measure of software structural interdependency. • Based our analysis on the dataset collected from the CVS archives of the OSS projects. • Code commits do not reflect quality of code changes. • Team structure measures are extracted from CVS files. Mengru Zhao 7482645 Yiyi Dai 7067041 slide 21

  22. Reference • [1] Nakagawa, E.Y.; de Sousa, E.P.M.; de Brito Murata, K.; de FariaAndery, G.; Morelli, L.B.; Maldonado, J.C., "Software Architecture Relevance in Open Source Software Evolution: A Case Study," Computer Software and Applications, 2008. COMPSAC '08. 32nd Annual IEEE International , pp.1234,1239, July 28 2008-Aug. 1 2008; • [2] Badri, M.; Drouin, N.; Toure, F., "On understanding software quality evolution from a defect perspective: A case study on an open source software system," Computer Systems and Industrial Informatics (ICCSII), 2012 International Conference, pp.1,6, 18-20 Dec. 2012; • [3] Krishna, V.; Basu, A., "Software Architecture for large/critical applications," Software Engineering (CONSEG), 2012 CSI Sixth International Conference, pp.1,4, 5-7 Sept. 2012; • [4] Petrov, P.; Buy, U.; Nord, R.L., "Enhancing the software architecture analysis and design process with inferred macro-architectural requirements," Twin Peaks of Requirements and Architecture (Twin Peaks), 2012 IEEE First International Workshop, pp.20,26, 25-25 Sept. 2012 • [5] Ning Nan; Kumar, S., "Joint Effect of Team Structure and Software Architecture in Open Source Software Development," Engineering Management, IEEE Transactions, vol.60, no.3, pp.592,603, Aug. 2013----main reference Mengru Zhao 7482645 Yiyi Dai 7067041 slide 22

  23. THANK YOU!

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