research-article

Focus-shifting patterns of OSS developers and their congruence with call graphs

Authors:

Premkumar Devanbu,

Vladimir FilkovAuthors Info & Claims

FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

Pages 401 - 412

https://doi.org/10.1145/2635868.2635914

Published: 11 November 2014 Publication History

Abstract

Developers in complex, self-organized open-source projects often work on many different files, and over time switch focus between them. Shifting focus can have impact on the software quality and productivity, and is thus an important topic of investigation. In this paper, we study focus shifting patterns (FSPs) of developers by comparing trace data from a dozen open source software (OSS) projects of their longitudinal commit activities and file dependencies from the projects call graphs. Using information theoretic measures of network structure, we find that fairly complex focus-shifting patterns emerge, and FSPs in the same project are more similar to each other. We show that developers tend to shift focus along with, rather than away from, software dependency links described by the call graphs. This tendency becomes weaker as either the interval between successive commits, or the organizational distance between committed files (i.e. directory distance), gets larger. Interestingly, this tendency appears stronger with more productive developers. We hope our study will initiate interest in further understanding of FSPs, which can ultimately help to (1) improve current recommender systems to predict the next focus of developers, and (2) provide insight into better call graph design, so as to facilitate developers' work.

References

[1]

C. Y. Baldwin and K. B. Clark. Design Rules: The Power of Modularity. MIT Press, 2000.

[2]

E. T. Barr, C. Bird, P. C. Rigby, A. Hindle, D. M. German, and P. Devanbu. Cohesive and isolated development with branches. In FASE, pages 316–331. Springer, 2012.

Digital Library

[3]

S. Bates and S. Horwitz. Incremental program testing using program dependence graphs. In POPL, pages 384–396. ACM, 1993.

Digital Library

[4]

C. Bird, N. Nagappan, H. Gall, B. Murphy, and P. Devanbu. Putting it all together: Using socio-technical networks to predict failures. In ISSRE, pages 109–119. IEEE, 2009.

Digital Library

[5]

C. Bird, N. Nagappan, B. Murphy, H. Gall, and P. Devanbu. Don’t touch my code!: examining the effects of ownership on software quality. In FSE, pages 4–14. ACM, 2011.

Digital Library

[6]

C. Bird, D. Pattison, R. D’Souza, V. Filkov, and P. Devanbu. Latent social structure in open source projects. In FSE, pages 24–35. ACM, 2008.

Digital Library

[7]

K.-Y. Cai and B.-B. Yin. Software execution processes as an evolving complex network. Information Sciences, 179(12):1903–1928, 2009.

Digital Library

[8]

M. Cataldo and J. D. Herbsleb. Coordination breakdowns and their impact on development productivity and software failures. IEEE Transactions on Software Engineering, 39(3):343–360, 2013.

Digital Library

[9]

M. Cataldo, A. Mockus, J. A. Roberts, and J. D. Herbsleb. Software dependencies, work dependencies, and their impact on failures. IEEE Transactions on Software Engineering, 35(6):864–878, 2009.

Digital Library

[10]

M. Cataldo, P. A. Wagstrom, J. D. Herbsleb, and K. M. Carley. Identification of coordination requirements: implications for the design of collaboration and awareness tools. In CSCW, pages 353–362. ACM, 2006.

Digital Library

[11]

D. Centola. The spread of behavior in an online social network experiment. Science, 329(5996):1194–1197, 2010.

[12]

D. Cherney, T. Denton, and A. Waldron. Linear Algebra. University of California Davis, 2013.

[13]

L. Dabbish, C. Stuart, J. Tsay, and J. Herbsleb. Social coding in GitHub: transparency and collaboration in an open software repository. In CSCW, pages 1277–1286. ACM, 2012.

Digital Library

[14]

N. R. Draper and H. Smith. Applied Regression Analysis 2nd ed. New York John Wiley and Sons, 1981.

[15]

J. Feller and B. Fitzgerald. Understanding Open Source Software Development. Addison-Wesley London, 2002.

Digital Library

[16]

W. Harrison. An entropy-based measure of software complexity. IEEE Transactions on Software Engineering, 18(11):1025–1029, 1992.

Digital Library

[17]

J. D. Herbsleb. Global software engineering: The future of socio-technical coordination. In Future of Software Engineering, pages 188–198. IEEE Computer Society, 2007.

Digital Library

[18]

J. D. Herbsleb, A. Mockus, and J. A. Roberts. Collaboration in software engineering projects: A theory of coordination. In ICIS, 2006.

[19]

E. Hill, L. Pollock, and K. Vijay-Shanker. Exploring the neighborhood with dora to expedite software maintenance. In ASE, pages 14–23. ACM, 2007.

Digital Library

[20]

I. Jolliffe. Principal Component Analysis. Wiley Online Library, 2005.

[21]

S. H. Kan. Metrics and Models in Software Quality Engineering. Addison-Wesley Longman Publishing Co., Inc., 2002.

Digital Library

[22]

M. Kersten and G. C. Murphy. Using task context to improve programmer productivity. In FSE, pages 1–11. ACM, 2006.

Digital Library

[23]

A. J. Ko, B. A. Myers, M. J. Coblenz, and H. H. Aung. An exploratory study of how developers seek, relate, and collect relevant information during software maintenance tasks. IEEE Transactions on Software Engineering, 32(12):971–987, 2006.

Digital Library

[24]

J. Lawrance, C. Bogart, M. Burnett, R. Bellamy, K. Rector, and S. D. Fleming. How programmers debug, revisited: An information foraging theory perspective. IEEE Transactions on Software Engineering, 39(2):197–215, 2013.

Digital Library

[25]

A. MacCormack, J. Rusnak, and C. Y. Baldwin. Exploring the structure of complex software designs: An empirical study of open source and proprietary code. Management Science, 52(7):1015–1030, 2006.

Digital Library

[26]

C. Mao. Structure visualization and analysis for software dependence network. In GrC, pages 439–444. IEEE, 2011.

[27]

T. J. McCabe. A complexity measure. IEEE Transactions on Software Engineering, (4):308–320, 1976.

Digital Library

[28]

A. Mockus and D. M. Weiss. Predicting risk of software changes. Bell Labs Technical Journal, 5(2):169–180, 2000.

[29]

C. R. Myers. Software systems as complex networks: Structure, function, and evolvability of software collaboration graphs. Physical Review E, 68(4):046116, 2003.

[30]

T. H. Nguyen, B. Adams, and A. E. Hassan. Studying the impact of dependency network measures on software quality. In ICSM, pages 1–10. IEEE, 2010.

Digital Library

[31]

G. Palla, A.-L. Barabási, and T. Vicsek. Quantifying social group evolution. Nature, 446(7136):664–667, 2007.

[32]

D. Posnett, R. D’Souza, P. Devanbu, and V. Filkov. Dual ecological measures of focus in software development. In ICSE, pages 452–461. IEEE, 2013.

Digital Library

[33]

F. Rahman and P. Devanbu. Ownership, experience and defects: a fine-grained study of authorship. In ICSE, pages 491–500. ACM, 2011.

Digital Library

[34]

M. P. Robillard. Automatic generation of suggestions for program investigation. In ACM SIGSOFT Software Engineering Notes, volume 30, pages 11–20. ACM, 2005.

Digital Library

[35]

M. P. Robillard, W. Coelho, and G. C. Murphy. How effective developers investigate source code: An exploratory study. IEEE Transactions on Software Engineering, 30(12):889–903, 2004.

Digital Library

[36]

G. Rothermel and M. J. Harrold. A safe, efficient regression test selection technique. ACM Transactions on Software Engineering and Methodology, 6(2):173–210, 1997.

Digital Library

[37]

Z. M. Saul, V. Filkov, P. Devanbu, and C. Bird. Recommending random walks. In FSE, pages 15–24. ACM, 2007.

Digital Library

[38]

A. Serebrenik and M. van den Brand. Theil index for aggregation of software metrics values. In ICSM, pages 1–9. IEEE, 2010.

Digital Library

[39]

C. Song, Z. Qu, N. Blumm, and A.-L. Barabási. Limits of predictability in human mobility. Science, 327(5968):1018–1021, 2010.

[40]

K. Ulrich. The role of product architecture in the manufacturing firm. Research Policy, 24(3):419–440, 1995.

[41]

B. Vasilescu, A. Serebrenik, and M. van den Brand. You can’t control the unfamiliar: A study on the relations between aggregation techniques for software metrics. In ICSM, pages 313–322. IEEE, 2011.

Digital Library

[42]

Q. Xuan, F. Du, L. Yu, and G. Chen. Reaction-diffusion processes and metapopulation models on duplex networks. Physical Review E, 87(3):032809, 2013.

[43]

Q. Xuan and V. Filkov. Building it together: synchronous development in OSS. In ICSE, pages 222–233. ACM, 2014.

Digital Library

[44]

Q. Xuan, M. Gharehyazie, P. T. Devanbu, and V. Filkov. Measuring the effect of social communications on individual working rhythms: A case study of open source software. In Social Informatics, pages 78–85. IEEE, 2012.

Digital Library

[45]

A. Zaidman, B. Van Rompaey, A. van Deursen, and S. Demeyer. Studying the co-evolution of production and test code in open source and industrial developer test processes through repository mining. Empirical Software Engineering, 16(3):325–364, 2011.

Digital Library

[46]

X. Zheng, D. Zeng, H. Li, and F. Wang. Analyzing open-source software systems as complex networks. Physica A: Statistical Mechanics and its Applications, 387(24):6190–6200, 2008.

[47]

T. Zimmerman, N. Nagappan, K. Herzig, R. Premraj, and L. Williams. An empirical study on the relation between dependency neighborhoods and failures. In ICST, pages 347–356. IEEE, 2011.

Digital Library

[48]

T. Zimmermann and N. Nagappan. Predicting defects using network analysis on dependency graphs. In ICSE, pages 531–540. ACM, 2008. Introduction Related Work Research Questions Methodology FSN and FDN Layered Networks Measures of Network Structure Multiple Linear Regression and Orthogonal Decomposition Results and Discussion RQ1: Shifting Focus along Call Links RQ2: Comparing FSPs within and between Projects RQ3: File Organization and FSPs RQ4: FSPs and Productivity RQ5: Structural Effects on Productivity Threats to Validity Conclusions Acknowledgments References

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Index Terms

Focus-shifting patterns of OSS developers and their congruence with call graphs

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Published In

cover image ACM Conferences

FSE 2014: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering

November 2014

856 pages

ISBN:9781450330565

DOI:10.1145/2635868

General Chair:
Shing-Chi Cheung
Hong Kong University of Science and Technology, China
,
Program Chairs:
Alessandro Orso
Georgia Institute of Technology, USA
,
Margaret-Anne Storey
University of Victoria, Canada

Copyright © 2014 ACM.

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Published: 11 November 2014

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SIGSOFT/FSE'14: 22nd ACM SIGSOFT Symposium on the Foundations of Software Engineering

November 16 - 21, 2014

Hong Kong, China

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https://dl.acm.org/doi/10.1145/3457913.3457914
Fu CZheng YLiu YXuan QChen G(2020)NES-TL: Network Embedding Similarity-Based Transfer LearningIEEE Transactions on Network Science and Engineering10.1109/TNSE.2019.29423417:3(1607-1618)Online publication date: 1-Jul-2020
https://doi.org/10.1109/TNSE.2019.2942341
Sarker FVasilescu BBlincoe KFilkov VAtlee JBultan TWhittle J(2019)Socio-technical work-rate increase associates with changes in work patterns in online projectsProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00099(936-947)Online publication date: 25-May-2019
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