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ForkXplorer: an approach of fork summary generation

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Abstract

Pull-based development has become an important paradigm for distributed software development. In this model, each developer independently works on a copied repository (i.e., a fork) from the central repository. It is essential for developers to maintain awareness of the state of other forks to improve collaboration efficiency. In this paper, we propose a method to automatically generate a summary of a fork. We first use the random forest method to generate the label of a fork, i.e., feature implementation or a bug fix. Based on the information of the fork-related commits, we then use the TextRank algorithm to generate detailed activity information of the fork. Finally, we apply a set of rules to integrate all related information to construct a complete fork summary. To validate the effectiveness of our method, we conduct 30 groups of manual experiment and 77 groups of case studies on Github. We propose Feaavg to evaluate the performance of the generated fork summary, considering the content accuracy, content integrity, sentence fluency, and label extraction accuracy. The results show that the average of Feaavg of the fork summary generated by this method is 0.672. More than 63% of project maintainers and the contributors believe that the fork summary can improve development efficiency.

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References

  1. Gousios G, Storey M A, Bacchelli A. Work practices and challenges in pull-based development: the contributor’s perspective. In: Proceedings of IEEE/ACM International Conference on Software Engineering. 2016, 285–296

  2. Lu Y, Mao X, Wang T, Yin G, Li Z. Improving students’ programming quality with the continuous inspection process: a social coding perspective. Frontiers of Computer Science, 2020, 14(5): 1–18

    Article  Google Scholar 

  3. Jiang J, Lo D, He J, Xia X, Kochhar P S, Zhang L. Why and how developers fork what from whom in GitHub. Empirical Software Engineering, 2017, 22(1): 547–578

    Article  Google Scholar 

  4. Bitzer J, Schröder P J H. The Economics of open source software development. 1st ed. Kidlington: Elsevier, 2006

    Google Scholar 

  5. Abdullah R, Lakulu M, Ibrahim H, Selamat M H, Nor M Z M. The challenges of open source software development with collaborative environment. In: Proceedings of IEEE International Conference on Computer Technology and Development. 2009, 251–255

  6. Padhye R, Mani S, Sinha V S. A study of external community contribution to open-source projects on GitHub. In: Proceedings of the Working Conference on Mining Software Repositories. 2014, 332–335

  7. Ren L, Zhou S, Kästner C, Wąsowski A. Identifying redundancies in fork-based development. In: Proceedings of IEEE International Conference on Software Analysis, Evolution and Reengineering. 2019, 230–241

  8. Stănciulescu Ş, Schulze S, Wąsowski A. Forked and integrated variants in an open-source firmware project. In: Proceedings of IEEE International Conference on Software Maintenance and Evolution. 2015, 151–160

  9. Ren L, Zhou S, Kästner C. Poster: Forks insight: providing an overview of GitHub forks. In: Proceedings of ACM/IEEE International Conference on Software Engineering. 2018, 179–180

  10. Zhou S, Stanciulescu S, Leßenich O, Xiong Y, Wasowski A, Kästner C. Identifying features in forks. In: Proceedings of ACM/IEEE International Conference on Software Engineering. 2018, 105–116

  11. Yu Y, Li Z, Yin G, Wang T, Wang H M. A dataset of duplicate pullrequests in Github. In: Proceedings of International Conference on Mining Software Repositories. 2018, 22–25

  12. Zhu J, Zhou M, Mockus A. Effectiveness of code contribution: from patch-based to pull-request-based tools. In: Proceedings of ACM SIGSOFT International Symposium on Foundations of Software Engineering. 2016, 871–882

  13. Li L, Ren Z, Li X, Zou W, Jiang H. How are issue units linked? Empirical study on the linking behavior in GitHub. In: Proceedings of IEEE Asia-Pacific Software Engineering Conference. 2018, 386–395

  14. Li Z, Yin G, Yu Y, Wang T, Wang H. Detecting duplicate pull-requests in github. In: Proceedings of Asia-Pacific Symposium on Internetware. 2017, 1–6

  15. Ruan H, Chen B, Peng X, Zhao W. DeepLink: Recovering issuecommit links based on deep learning. Journal of Systems and Software, 2019, 158: 110406

    Article  Google Scholar 

  16. Sun Y, Chen C, Wang Q, Boehm, B. Improving missing issue-commit link recovery using positive and unlabeled data. In: Proceedings of IEEE/ACM International Conference on Automated Software Engineering. 2017, 147–152

  17. Salton G, Wong A, Yang C S. A vector space model for automatic indexing. Communications of the ACM, 1975, 18(11): 613–620

    Article  MATH  Google Scholar 

  18. Salton G, Buckley C. Term-weighting approaches in automatic text retrieval. Information processing & management, 1988, 24(5): 513–523

    Article  Google Scholar 

  19. James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. 1st ed. New York: Springer, 2013

    Book  MATH  Google Scholar 

  20. Liu Z, Chen X, Sun M. Mining the interests of Chinese microbloggers via keyword extraction. Frontiers of Computer Science, 2012, 6(1): 76–87

    MathSciNet  Google Scholar 

  21. Mihalcea R, Tarau P. Textrank: Bringing order into text. In: Proceedings of Conference on Empirical Methods in Natural Language Processing. 2004, 404–411

  22. Gambhir M, Gupta V. Recent automatic text summarization techniques: a survey. Artificial Intelligence Review, 2017, 47(1): 1–66

    Article  Google Scholar 

  23. Nyman L, Mikkonen T. To fork or not to fork: Fork motivations in SourceForge projects. International Journal of Open Source Software and Processes, 2011, 3(3): 1–9

    Article  Google Scholar 

  24. Robles G, González-Barahona J M. A comprehensive study of software forks: dates, reasons and outcomes. In: Proceedings of IFIP International Conference on Open Source Systems. 2012, 1–14

  25. Stänciulescu Ş, Schulze S, Wąsowski A. Forked and integrated variants in an open-source firmware project. In: Proceedings of IEEE International Conference on Software Maintenance and Evolution. 2015, 151–160

  26. Gousios G, Pinzger M, Deursen A. An exploratory study of the pullbased software development model. In: Proceedings of International Conference on Software Engineering. 2014, 345–355

  27. Dabbish L, Stuart C, Tsay J, Herbsleb J. Social coding in GitHub: transparency and collaboration in an open software repository. In: Proceedings of ACM Conference on Computer Supported Cooperative Work. 2012, 1277–1286

  28. Dabbish L, Stuart C, Tsay J, Herbsleb J. Leveraging transparency. IEEE Software, 2012, 30(1): 37–43

    Article  Google Scholar 

  29. Kuhn A, Ducasse S, Gírba T. Semantic clustering: Identifying topics in source code. Information and Software Technology, 2007, 49(3): 230–243

    Article  Google Scholar 

  30. Murphy G C. Lightweight structural summarization as an aid to software evolution. Seattle: University of Washington, 1996

    Google Scholar 

  31. Poshyvanyk D, Marcus A. Combining formal concept analysis with information retrieval for concept location in source code. In: Proceedings of IEEE International Conference on Program Comprehension. 2007, 37–48

  32. Storey M A, Cheng L T, Bull I, Rigby P. Shared waypoints and social tagging to support collaboration in software development. In: Proceedings of ACM Anniversary Conference on Computer Supported Cooperative Work. 2006, 195–198

  33. Khatavkar V, Kulkarni P. Comparison of support vector machines with and without latent semantic analysis for document classification. In: Proceedings of International Conference on Data Management, Analytics & Innovation. 2019, 263–274

  34. Nazar N, Jiang H, Gao G, Zhang T, Li X, Ren Z. Source code fragment summarization with small-scale crowdsourcing based features. Frontiers of Computer Science, 2016, 10(3): 504–517

    Article  Google Scholar 

  35. Cortés-Coy L F, Linares-Vásquez M, Aponte J, Poshyvanyk, D. On automatically generating commit messages via summarization of source code changes. In: Proceedings of IEEE International Working Conference on Source Code Analysis and Manipulation. 2014, 275–284

  36. Jiang S, Armaly A, McMillan C. Automatically generating commit messages from diffs using neural machine translation. In: Proceedings of IEEE/ACM International Conference on Automated Software Engineering. 2017, 135–146

  37. Liu Z, Xia X, Hassan A E, Lo D, Xing Z, Wang X. Neural-machinetranslation-based commit message generation: how far are we? In: Proceedings of ACM/IEEE International Conference on Automated Software Engineering. 2018, 373–384

  38. Zaidi A. Summarizing git commits and Github pull requests using sequence to sequence neural attention models. California: Stanford University, 2017

    Google Scholar 

  39. Liu Z, Xia X, Treude C, Lo D, Li S. Automatic generation of pull request descriptions. In: Proceedings of IEEE/ACM International Conference on Automated Software Engineering. 2019, 176–188

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFB1004202).

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Authors and Affiliations

  1. Key Laboratory of Software Engineering for Complex Systems, College of Computer, National University of Defense Technology, Changsha, 410073, China

    Zhang Zhang, Xinjun Mao, Chao Zhang & Yao Lu

Authors
  1. Zhang Zhang
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  2. Xinjun Mao
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  3. Chao Zhang
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  4. Yao Lu
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Corresponding author

Correspondence to Xinjun Mao.

Additional information

Zhang Zhang is a master candidate in the College of Computer, National University of Defense Technology, China. His work interests include open source software engineering, data mining, and crowdsourced learning.

Xinjun Mao is a professor in the College of Computer, National University of Defense Technology, China. He received his PhD degree in computer science from National University of Defense Technology, China in 1998. His research interests include software engineering, multi-agent system, robot system, self-adaptive system, and crowdsourcing.

Chao Zhang is a master in the College of Computer, National University of Defense Technology, China. His work interests include open source software engineering and crowdsourced learning.

Yao Lu is a lecturer in the College of Computer, National University of Defense Technology, China. He received his PhD degree in software engineering from National University of Defense Technology, China in 2019. His research interests include open source software engineering, data mining, and crowdsourced learning.

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Zhang, Z., Mao, X., Zhang, C. et al. ForkXplorer: an approach of fork summary generation. Front. Comput. Sci. 16, 162202 (2022). https://doi.org/10.1007/s11704-020-0047-4

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