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Analysis of Factors Influencing Developers' Sentiments in Commit Logs: Insights from Applying Sentiment Analysis

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Języki publikacji
EN
Abstrakty
EN
Background: In the open source software paradigm, software development depends upon efforts of volunteer members that are geographically dispersed and collaborate with each other over the Internet. Communication artifacts like mailing lists, forums, and issue tracking systems are used by developers for communication. The way they express themselves through these communication channels greatly influences their productivity, efficiency of development activities, and survival of the project as well. Therefore, it is essential to understand affective state of developers’ contributions to make software engineering more effective. Aim: This study examined commit logs of seven GitHub projects to analyze developers’ sentiments. This study also investigated the relationship of developers’ sentiments in commit logs with team size of project, type of change activity, and contribution volume. Method: Sentiments of developers are calculated using SentiStrength-SE tool that is specialized in software engineering domain. Results: Our findings revealed that the majority of sentiments conveyed by developers in commit logs were neutral. Furthermore, we found that team size, change activity, and commit contribution volume influenced sentiments conveyed in commit logs. Conclusion: Our findings will help project managers to better understand developer sentiments while performing different software development tasks/activities. It will be beneficial in improving developer productivity and retention.
Rocznik
Strony
art. no. 220102
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
autor
  • Department of Computer Science, Guru Nanak Dev University, Amritsar, India
  • Department of Computer Science, Guru Nanak Dev University, Amritsar, India
autor
  • Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India
Bibliografia
  • 1. D. Graziotin and F. Fagerholm, “Happiness and the productivity of software engineers,” in Rethinking Productivity in Software Engineering. Springer, 2019, pp. 109–124.
  • 2. M. De Choudhury and S. Counts, “Understanding affect in the workplace via social media,” in Proceedings of the Conference on Computer Supported Cooperative Work, 2013, pp. 303–316.
  • 3. B. Liu et al., “Sentiment analysis and subjectivity,” Handbook of Natural Language Processing, Vol. 2, No. 2010, 2010, pp. 627–666.
  • 4. E. Guzman, D. Azócar, and Y. Li, “Sentiment analysis of commit comments in GitHub: An empirical study,” in Proceedings of the 11th Working Conference on Mining Software Repositories, 2014, pp. 352–355.
  • 5. V. Sinha, A. Lazar, and B. Sharif, “Analyzing developer sentiment in commit logs,” in Proceedings of the 13th International Conference on Mining Software Repositories, 2016, pp. 520–523.
  • 6. N. Singh and P. Singh, “How do code refactoring activities impact software developers’ sentiments? – An empirical investigation into GitHub commits,” in 24th Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2017, pp. 648–653.
  • 7. M.R. Islam and M.F. Zibran, “Sentiment analysis of software bug related commit messages,” Network, Vol. 740, 2018, p. 740.
  • 8. P. Tourani, Y. Jiang, and B. Adams, “Monitoring sentiment in open source mailing lists: exploratory study on the apache ecosystem,” in Proceedings of 24th Annual International Conference on Computer Science and Software Engineering, Vol. 14, 2014, pp. 34–44.
  • 9. J. Ding, H. Sun, X. Wang, and X. Liu, “Entity-level sentiment analysis of issue comments,” in Proceedings of the 3rd International Workshop on Emotion Awareness in Software Engineering, 2018, pp. 7–13.
  • 10. F. Jurado and P. Rodriguez, “Sentiment analysis in monitoring software development processes: An exploratory case study on GitHub’s project issues,” Journal of Systems and Software,Vol. 104, 2015, pp. 82–89.
  • 11. R. Paul, A. Bosu, and K.Z. Sultana, “Expressions of sentiments during code reviews: Male vs. female,” in 26th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2019, pp. 26–37.
  • 12. D. Garcia, M.S. Zanetti, and F. Schweitzer, “The role of emotions in contributors activity: A case study on the gentoo community,” in International Conference on Cloud and Green Computing. IEEE, 2013, pp. 410–417.
  • 13. M.R. Islam and M.F. Zibran, “Exploration and exploitation of developers’ sentimental variations in software engineering,” in Research Anthology on Recent Trends, Tools, and Implications of Computer Programming. IGI Global, 2021, pp. 1889–1910.
  • 14. A. Murgia, P. Tourani, B. Adams, and M. Ortu, “Do developers feel emotions? An exploratory analysis of emotions in software artifacts,” in Proceedings of the 11th Working Conference on Mining Software Repositories, 2014, pp. 262–271.
  • 15. M.R. Islam and M.F. Zibran, “Leveraging automated sentiment analysis in software engineering,” in 14th International Conference on Mining Software Repositories (MSR). IEEE, 2017, pp. 203–214.
  • 16. N. Novielli, F. Calefato, F. Lanubile, and A. Serebrenik, “Assessment of off-the-shelf SE-specific sentiment analysis tools: An extended replication study,” Empirical Software Engineering, Vol. 26, No. 4, 2021, pp. 1–29.
  • 17. K. Sun, H. Gao, H. Kuang, X. Ma, G. Rong et al., “Exploiting the unique expression for improved sentiment analysis in software engineering text,” arXiv preprint arXiv:2103.13154, 2021.
  • 18. E. Biswas, M.E. Karabulut, L. Pollock, and K. Vijay-Shanker, “Achieving reliable sentiment analysis in the software engineering domain using BERT,” in International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2020, pp. 162–173.
  • 19. N. Novielli, F. Calefato, D. Dongiovanni, D. Girardi, and F. Lanubile, “Can we use SE-specific sentiment analysis tools in a cross-platform setting?” in Proceedings of the 17th International Conference on Mining Software Repositories, 2020, pp. 158–168.
  • 20. M.R. Wrobel, “The impact of lexicon adaptation on the emotion mining from software engineering artifacts,” IEEE Access, Vol. 8, 2020, pp. 48 742–48 751.
  • 21. M. Obaidi and J. Klünder, “Development and application of sentiment analysis tools in software engineering: A systematic literature review,” Evaluation and Assessment in Software Engineering, 2021, pp. 80–89.
  • 22. S.F. Huq, A.Z. Sadiq, and K. Sakib, “Is developer sentiment related to software bugs: An exploratory study on GitHub commits,” in 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 2020, pp. 527–531.
  • 23. R. Kaur and K.K. Chahal, “Analysis of developers’ sentiments in commit comments,” in International Conference on Advanced Informatics for Computing Research. Springer, 2020, pp. 3–12.
  • 24. S. Bharti and H. Singh, “Investigating developers’ sentiments associated with software cloning practices,” in International Conference on Advanced Informatics for Computing Research. Springer, 2018, pp. 397–406.
  • 25. R. Souza and B. Silva, “Sentiment analysis of Travis CI builds,” in 14th International Conference on Mining Software Repositories (MSR). IEEE, 2017, pp. 459–462.
  • 26. D. Pletea, B. Vasilescu, and A. Serebrenik, “Security and emotion: sentiment analysis of security discussions on GitHub,” in Proceedings of the 11th Working Conference on Mining Software Repositories, 2014, pp. 348–351.
  • 27. I.A. Khan, W.P. Brinkman, and R.M. Hierons, “Do moods affect programmers’ debug performance?” Cognition, Technology and Work, Vol. 13, No. 4, 2011, pp. 245–258.
  • 28. S.C. Müller and T. Fritz, “Stuck and frustrated or in flow and happy: Sensing developers’ emotions and progress,” in 37th International Conference on Software Engineering, Vol. 1. IEEE, 2015, pp. 688–699.
  • 29. D. Graziotin, X. Wang, and P. Abrahamsson, “Happy software developers solve problems better: Psychological measurements in empirical software engineering,” PeerJ, Vol. 2, 2014, p. e289.
  • 30. M.R. Wrobel, “Emotions in the software development process,” in 6th International Conference on Human System Interactions (HSI). IEEE, 2013, pp. 518–523.
  • 31. M.R. Islam and M.F. Zibran, “SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text,” Journal of Systems and Software, Vol. 145, 2018, pp. 125–146.
  • 32. S.F. Huq, A.Z. Sadiq, and K. Sakib, “Understanding the effect of developer sentiment on fix-inducing changes: an exploratory study on GitHub pull requests,” in 26th Asia-Pacific Software Engineering Conference (APSEC). IEEE, 2019, pp. 514–521.
  • 33. M. Ortu, A. Murgia, G. Destefanis, P. Tourani, R. Tonelli et al., “The emotional side of software developers in JIRA,” in 13th Working Conference on Mining Software Repositories (MSR). IEEE, 2016, pp. 480–483.
  • 34. M.M. Rahman, C.K. Roy, and I. Keivanloo, “Recommending insightful comments for source code using crowdsourced knowledge,” in 15th International Working Conference on Source Code Analysis and Manipulation (SCAM). IEEE, 2015, pp. 81–90.
  • 35. R. Jongeling, P. Sarkar, S. Datta, and A. Serebrenik, “On negative results when using sentiment analysis tools for software engineering research,” Empirical Software Engineering, Vol. 22, No. 5, 2017, pp. 2543–2584.
  • 36. F. Calefato, F. Lanubile, F. Maiorano, and N. Novielli, “Sentiment polarity detection for software development,” Empirical Software Engineering, Vol. 23, No. 3, 2018, pp. 1352–1382.
  • 37. R. Jongeling, S. Datta, and A. Serebrenik, “Choosing your weapons: On sentiment analysis tools for software engineering research,” in International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2015, pp. 531–535.
  • 38. K. Beecher, C. Boldyreff, A. Capiluppi, and S. Rank, “Evolutionary success of open source software: An investigation into exogenous drivers,” Electronic Communications of the EASST, 2008.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3d113816-fa56-405e-b9d9-a3d6fca4d962
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