Abstract
Addressing issue reports is an integral part of open source software (OSS) projects. Although several studies have attempted to discover the factors that affect issue resolution, few pay attention to first responders, who is the first to post their comments after an issue report is published. We are interested at how first responders affect issue resolution process for OSS projects. Therefore, we extract the data from Github to perform our empirical study. By obtaining their identity types and speech acts, we analyze the impact of first responders with different identity types on the efficiency of issue resolution based on three metrics and find that identified users especially collaborators make first response can improve the efficiency of issue resolution. Furthermore, we make use of the identity type information of the first responders to forecast the issue lifetime and the results show that this information can also improve the accuracy for short-term prediction. It also verifies that first responders have a direct influence on the issue resolution process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
How to contribute to open source? [EB/OL]. https://opensource.guide/how-to-contribute
Al-Zubaidi, W.H.A., Dam, H.K., Ghose, A., Li, X.: Multi-objective search-based approach to estimate issue resolution time. In: Proceedings of the 13th International Conference on Predictive Models and Data Analytics in Software Engineering, pp. 53–62. ACM (2017)
Bertram, D., Voida, A., Greenberg, S., Walker, R.: Communication, collaboration, and bugs: the social nature of issue tracking in small, collocated teams. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, pp. 291–300. ACM (2010)
Destefanis, G., Ortu, M., Counsell, S., Swift, S., Marchesi, M., Tonelli, R.: Software development: do good manners matter? PeerJ Computer Science 2, 1–35 (2016)
Giger, E., Pinzger, M., Gall, H.: Predicting the fix time of bugs. In: Proceedings of the 2nd International Workshop on Recommendation Systems for Software Engineering, pp. 52–56. ACM (2010)
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, pp. 392–401. IEEE Press (2013)
Kikas, R., Dumas, M., Pfahl, D.: Issue dynamics in Github projects. In: Abrahamsson, P., Corral, L., Oivo, M., Russo, B. (eds.) PROFES 2015. LNCS, vol. 9459, pp. 295–310. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26844-6_22
Kikas, R., Dumas, M., Pfahl, D.: Using dynamic and contextual features to predict issue lifetime in Github projects. In: Proceedings of the 13th International Conference on Mining Software Repositories, pp. 291–302. ACM (2016)
Kochhar, P.S., Le, T.D.B., Lo, D.: It’s not a bug, it’s a feature: does misclassification affect bug localization? In: Proceedings of the 11th Working Conference on Mining Software Repositories, pp. 296–299. ACM (2014)
Liao, Z., He, D., Chen, Z., Fan, X., Zhang, Y., Liu, S.: Exploring the characteristics of issue-related behaviors in Github using visualization techniques. IEEE Access 6, 24003–24015 (2018)
Murgia, A., Concas, G., Tonelli, R., Ortu, M., Demeyer, S., Marchesi, M.: On the influence of maintenance activity types on the issue resolution time. In: Proceedings of the 10th International Conference on Predictive Models in Software Engineering, pp. 12–21. ACM (2014)
Panjer, L.D.: Predicting eclipse bug lifetimes. In: Proceedings of the Fourth International Workshop on Mining Software Repositories, p. 29. IEEE Computer Society (2007)
Rees-Jones, M., Martin, M., Menzies, T.: Better predictors for issue lifetime. arXiv preprint arXiv:1702.07735 (2017)
Weiss, C., Premraj, R., Zimmermann, T., Zeller, A.: How long will it take to fix this bug? In: Fourth International Workshop on Mining Software Repositories (MSR 2007: ICSE Workshops 2007), p. 1. IEEE (2007)
Yu, C.H., Ohlund, B.: Threats to validity of research design (2010). Accessed 12 Jan 2012
Zhang, F., Khomh, F., Zou, Y., Hassan, A.E.: An empirical study on factors impacting bug fixing time. In: 2012 19th Working Conference on Reverse Engineering, pp. 225–234. IEEE (2012)
Zhang, H., Gong, L., Versteeg, S.: Predicting bug-fixing time: an empirical study of commercial software projects. In: Proceedings of the 2013 International Conference on Software Engineering, pp. 1042–1051. IEEE Press (2013)
Acknowledgments
This work is partially supported by National Key Research and Development Plan (No. 2018YFB1003800) and China National Science Foundation (Granted Number 62072301).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y., Cao, J. (2021). Well Begun Is Half Done: How First Respondeners Affect Issue Resolution Process in Open Source Software Development?. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_46
Download citation
DOI: https://doi.org/10.1007/978-981-16-2540-4_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2539-8
Online ISBN: 978-981-16-2540-4
eBook Packages: Computer ScienceComputer Science (R0)
