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SCIENTIA SINICA Informationis, Volume 49, Issue 11: 1399-1411(2019) https://doi.org/10.1360/N112018-00319

Software digital sociology

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  • ReceivedDec 13, 2018
  • AcceptedMar 13, 2019
  • PublishedNov 4, 2019

Abstract

With the continuous development of the internet, software development (especially global open-source development) is facing critical challenges, such as individual differences in developers distributed worldwide, the increasing difficulty of group collaboration, and the complex ecosystems formed by extensive social participation. These problems exhibit strong sociological characteristics in regard to the activity of software development. This study proposes the concept of software digital sociology that involves mining abundant software-activity data to investigate individual learning, group collaboration, and sustainable ecosystems. We discuss the primary investigation of the critical issues associated with the open-source supply chain and suggest that software digital sociology can inspire software-development researchers to understand the key challenges posed by extensive (computer-mediated) social participation to promote exploration of solutions using a different paradigm.


Funded by

国家自然科学基金(61432001,61825201)

国家重点研发计划(2018YFB10044200)


References

[1] Witt J. SOC 2018. 6. New York: McGraw-Hill Higher Education, 2018. 121--135. Google Scholar

[2] Lazer D, Pentland A, Adamic L. Social science. Computational social science.. Science, 2009, 323: 721-723 CrossRef PubMed Google Scholar

[3] Brooks F. The mythical Man-Month: essays on software engineering (anniversary edition). Anniversary Edition, 2/E. Pearson Education India, 1995. 25--35. Google Scholar

[4] Mockus A, Fielding R T, Herbsleb J. A case study of open source software development: the Apache server. In: Proceedings of the 22nd International Conference on Software Engineering. New York: ACM, 2000. 263--272. Google Scholar

[5] Zhou M, Chen Q, Mockus A, et al. On the scalability of Linux kernel maintainers' work. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. New York: ACM, 2017. 27--37. Google Scholar

[6] Astromskis S, Bavota G, Janes A. Patterns of developers behaviour: A 1000-hour industrial study. J Syst Software, 2017, 132: 85-97 CrossRef Google Scholar

[7] Manikas K, Hansen K M. Software ecosystems - A systematic literature review. J Syst Software, 2013, 86: 1294-1306 CrossRef Google Scholar

[8] Vygotsky L. Interaction between learning and development. Readings on the Development of Children, 1978, 23: 34--41. Google Scholar

[9] Schach S R. Object-oriented and Classical Software Engineering. New York: McGraw-Hill, 2007. 125--135. Google Scholar

[10] Zhou M, Mockus A. What make long term contributors: willingness and opportunity in OSS community. In: Proceedings of International Conference on Software Engineering. IEEE, 2012. 518--528. Google Scholar

[11] Tan X, Qin H, Zhou M. Understanding the variation of software development tasks: a qualitative study. In: Proceedings of the 9th Asia-Pacific Symposium on Internetware. New York: ACM, 2017. Google Scholar

[12] National Research Council. How People Learn: Brain, Mind, Experience, and School. Expanded edition. Washington: National Academies Press, 2000. 134--145. Google Scholar

[13] Curtis B. Fifteen years of psychology in software engineering: individual differences and cognitive science. In: Proceedings of the 7th International Conference on Software Engineering. Piscataway: IEEE Press, 1984. 97--106. Google Scholar

[14] Neisser U, Boodoo G, Bouchard T J J. Intelligence: Knowns and unknowns.. Am Psychologist, 1996, 51: 77-101 CrossRef Google Scholar

[15] Ericsson K A, Krampe R T, Tesch-R?mer C. The role of deliberate practice in the acquisition of expert performance.. Psychological Rev, 1993, 100: 363-406 CrossRef Google Scholar

[16] Zhou M, Mockus A. Who Will Stay in the FLOSS Community? Modeling Participant's Initial Behavior. IIEEE Trans Software Eng, 2015, 41: 82-99 CrossRef Google Scholar

[17] Herbsleb J D, Moitra D. Global software development. IEEE Softw, 2001, 18: 16-20 CrossRef Google Scholar

[18] Zhou M, Mockus A. Mining micro-practices from operational data. In: Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York: ACM, 2014: 845--848. Google Scholar

[19] Zhu J, Zhou M, Mockus A. Patterns of folder use and project popularity: a case study of github repositories. In: Proceedings of ACM/IEEE International Symposium on Empirical Software Engineering & Measurement. New York: ACM, 2014. 1--4. Google Scholar

[20] Ohira M, Ohoka T, Kakimoto T, et al. Supporting knowledge collaboration using social networks in a large-scale online community of software development projects. In: Proceedings of the 12th Asia-Pacific Software Engineering Conference (APSEC'05), Taipei, 2005. 6. Google Scholar

[21] Xie J, Zheng Q, Zhou M, et al. Product assignment recommender. In: Proceedings of the 36th International Conference on Software Engineering. New York: ACM, 2014. 556--559. Google Scholar

[22] Tan X, Lin Z Y, Zhang Y X, et al. Analysis of contribution composition patterns of code files. J Softw, 2018, 29: 2283--2293 [谭鑫, 林泽燕, 张宇霞, 等. 代码文件贡献组成模式的分析. 软件学报, 2018, 29: 2283--2293]. Google Scholar

[23] Coelho J, Valente M T. Why modern open source projects fail. In: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering. New York: ACM, 2017. 186--196. Google Scholar

[24] Zhang Y X, Zhou M H, Zhang W, et al. How commercial organizations participate in openstack open source projects. J Softw, 2017, 28: 1343--1356. Google Scholar

[25] Zhou M, Mockus A, Ma X. Inflow and Retention in OSS Communities with Commercial Involvement. ACM Trans Softw Eng Methodol, 2016, 25: 1-29 CrossRef Google Scholar

[26] Chris Mellor. Aperi dies on its arise. The Register. https://www.theregister.co.uk/2009/01/30/aperi_is_dead/. Google Scholar

[27] Gamalielsson J, Lundell B, Lings B. Responsiveness as a measure for assessing the health of OSS ecosystems. In: Proceedings of the 2nd International Workshop on Building Sustainable Open Source Communities. Tampere: Tampere University of Technology, 2010. 1--8. Google Scholar

[28] Lehman M M, Ramil J F, Wernick P D, et al. Metrics and laws of software evolution-the nineties view. In: Proceedings of the 4th International Software Metrics Symposium, Albuquerque, 1997. 20--32. Google Scholar

[29] Zhang Y, Tan X, Zhou M, et al. Companies' domination in FLOSS development: an empirical study of OpenStack. In: Proceedings of the 40th International Conference on Software Engineering. New York: ACM, 2018. 440--441. Google Scholar

[30] Zheng Q, Mockus A, Zhou M. A method to identify and correct problematic software activity data: exploiting capacity constraints and data redundancies. In: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. New York: ACM, 2015. 637--648. Google Scholar

[31] Chen Q, Zhou M. A neural framework for retrieval and summarization of source code. In: Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering. New York: ACM, 2018. 826--831. Google Scholar

[32] Nagappan M, Zimmermann T, Bird C. Diversity in software engineering research. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering. New York: ACM, 2013. 466--476. Google Scholar

[33] Rahman F, Posnett D, Herraiz I, et al. Sample size vs. bias in defect prediction. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering. New York: ACM, 2013. 147--157. Google Scholar

[34] Tu F, Zhu J, Zheng Q, et al. Be careful of when: an empirical study on time-related misuse of issue tracking data. In: Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. New York: ACM, 2018. 307--318. Google Scholar

[35] Bird C, Bachmann A, Aune E, et al. Fair and balanced? Bias in bug-fix datasets. In: Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering. New York: ACM, 2009. 121--130. Google Scholar

[36] Bachmann A, Bird C, Rahman F, et al. The missing links: bugs and bug-fix commits. In: Proceedings of the 18th ACM SIGSOFT International Symposium on Foundations of Software Engineering. New York: ACM, 2010. 97--106. Google Scholar

[37] Herzig K, Just S, Zeller A. It's not a bug, it's a feature: how misclassification impacts bug prediction. In: Proceedings of the 2013 International Conference on Software Engineering. Piscataway: IEEE Press, 2013. 392--401. Google Scholar

[38] Tantithamthavorn C, McIntosh S, Hassan A E, et al. The impact of mislabelling on the performance and interpretation of defect prediction models. In: Proceedings of 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. Piscataway: IEEE Press, 2015. 1: 812--823. Google Scholar

[39] Laurent A M S. Understanding Open Source and Free Software Licensing: Guide to Navigating Licensing Issues in Existing & New Software. California: O'Reilly Media, 2004. Google Scholar

[40] Dey T, Mockus A. Are software dependency supply chain metrics useful in predicting change of popularity of npm packages? In: Proceedings of the 14th International Conference on Predictive Models and Data Analytics in Software Engineering. New York: ACM, 2018. 66--69. Google Scholar

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