Skip to main content
SpringerLink
Log in

Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Community smells are sub-optimal developer community structures that hinder productivity. Prior studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community shepherds. Simultaneously, refactoring smells also requires bottom-up effort from every developer. However, supportive measures and guidelines for them are not available at a fine-grained level. Since recent work revealed developers’ personalities and working states could influence community smells’ emergence and variation, we build prediction models with experience, sentiment, and development process features of developers considering three smells including Organizational Silo, Lone Wolf, and Bottleneck, as well as two related classes including smelly developer and smelly quitter. We predict the five classes in the individual granularity, and we also generate forecasts for the number of smelly developers in the community granularity. The proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project, time-wise, and cross-project prediction, and mean R2 performance of 0.68 in community-wide Smelly Developer prediction. We also exploit SHAP (SHapley Additive exPlanations) to assess feature importance to explain our predictors. In conclusion, we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities, and we recommend community shepherds to use the forecasting model for refactoring planning.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Tamburri D A, Palomba F, Kazman R. Exploring community smells in open-source: An automated approach. IEEE Trans. Softw. Eng., 2021, 47(3): 630-652. https://doi.org/10.1109/TSE.2019.2901490.

    Article  Google Scholar 

  2. Johnson B, Song Y, Murphy-Hill E, Bowdidge R. Why don’t software developers use static analysis tools to find bugs? In Proc. the 35th IEEE/ACM Int. Conference on Software Engineering, May 2013, pp.672-681. https://doi.org/10.1109/ICSE.2013.6606613.

  3. Pecorelli F, Palomba F, Khomh F, De Lucia A. Developer-driven code smell prioritization. In Proc. the 17th Int. Conference on Mining Software Repositories, June 2020, pp.220-231. https://doi.org/10.1145/3379597.3387457.

  4. Sae-Lim N, Hayashi S, Saeki M. Context-based code smells prioritization for prefactoring. In Proc. the 24th IEEE Int. Conference on Program Comprehension, May 2016. https://doi.org/10.1109/ICPC.2016.7503705.

  5. Martin F, Kent B, John B, William O, Don R. Refactoring: Improving the Design of Existing Code (1st edition). Addison-Wesley, 1999.

  6. Conejero J M, Rodríguez-Echeverría R, Hernández J, Clemente P J, Ortiz-Caraballo C, Jurado E, Sánchez-Figueroa F. Early evaluation of technical debt impact on maintainability. J. Syst. Softw., 2018, 142: 92-114. https://doi.org/10.1016/j.jss.2018.04.035.

    Article  Google Scholar 

  7. Tamburri D A. Software architecture social debt: Managing the incommunicability factor. IEEE Trans. Comput. Soc. Syst., 2019, 6(1): 20-37. https://doi.org/10.1109/TCSS.2018.2886433.

    Article  Google Scholar 

  8. Palomba F, Tamburri D A, Arcelli Fontana F, Oliveto R, Zaidman A, Serebrenik A. Beyond technical aspects: How do community smells influence the intensity of code smells? IEEE Trans. Softw. Eng., 2021, 47(1): 108-129. https://doi.org/10.1109/TSE.2018.2883603.

    Article  Google Scholar 

  9. Palomba F, Tamburri D A. Predicting the emergence of community smells using socio-technical metrics: A machine-learning approach. J. Syst. Softw., 2021, 171: Article No. 110847. https://doi.org/10.1016/j.jss.2020.110847.

  10. Catolino G, Palomba F, Tamburri D A, Serebrenik A, Ferrucci F. Refactoring community smells in the wild: The practitioner’s field manual. In Proc. the 42nd ACM/IEEE Int. Conference on Software Engineering: Software Engineering in Society, June 27-July 19, 2020, pp.25-34. https://doi.org/10.1145/3377815.3381380.

  11. Catolino G, Palomba F, Tamburri D A, Serebrenik A. Understanding community smells variability: A statistical approach. In Proc. the 43rd ACM/IEEE Int. Conference on Software Engineering: Software Engineering in Society, May 2021, pp.77-86. https://doi.org/10.1109/ICSESEIS52602.2021.00017.

  12. Ferreira I, Stewart K, German D, Adams B. A longitudinal study on the maintainers’ sentiment of a large scale open source ecosystem. In Proc. the 4th IEEE/ACM Int. Workshop on Emotion Awareness in Software Engineering, May 2019, pp.17-22. https://doi.org/10.1109/SEmotion.2019.00011.

  13. Tamburri D A, Kazman R, Fahimi H. The architect’s role in community shepherding. IEEE Softw., 2016, 33(6): 70-79. https://doi.org/10.1109/MS.2016.144.

    Article  Google Scholar 

  14. Yue Y, Yu X, You X, Wang Y, Redmiles D. Ideology in open source development. In Proc. the 13th IEEE/ACM Int. Workshop on Cooperative and Human Aspects of Software Engineering, May 2021, pp.71-80. https://doi.org/10.1109/CHASE52884.2021.00016.

  15. Ducheneaut N. Socialization in an open source software community: A socio-technical analysis. Comput. Support. Coop. Work, 2005, 14(4): 323-368. https://doi.org/10.1007/s10606-005-9000-1.

    Article  Google Scholar 

  16. Mäntylä M, Adams B, Destefanis G, Graziotin D, Ortu M. Mining valence, arousal, and dominance: Possibilities for detecting burnout and productivity? In Proc. the 13th Int. Conference on Mining Software Repositories, May 2016, pp.247-258. https://doi.org/10.1145/2901739.2901752.

  17. Cheruvelil J, Da Silva B C. Developers’ sentiment and issue reopening. In Proc. the 4th Int. Workshop on Emotion Awareness in Software Engineering, May 2019, pp.29-33. https://doi.org/10.1109/SEmotion.2019.00013.

  18. Huq S F, Sadiq A Z, Sakib K. Understanding the effect of developer sentiment on Fix-Inducing Changes: An exploratory study on Github pull requests. In Proc. the 26th Asia-Pacific Software Engineering Conference, December 2019, pp.514-521. https://doi.org/10.1109/APSEC48747.2019.00075.

  19. Valdivia-Garcia H, Shihab E, Nagappan M. Characterizing and predicting blocking bugs in open source projects. J. Syst. Softw., 2018, 143: 44-58. https://doi.org/10.1016/j.jss.2018.03.053.

    Article  Google Scholar 

  20. Ortu M, Murgia A, Destefanis G, Tourani P, Tonelli R, Marchesi M, Adams B. The emotional side of software developers in JIRA. In Proc. the 13th International Conference on Mining Software Repositories, May 2016, pp.480-483. https://doi.org/10.1145/2901739.2903505.

  21. Huang Z, Shao Z, Fan G, Gao J, Zhou Z, Yang K, Yang X. Predicting community smells’ occurrence on individual developers by sentiments. In Proc. the 29th IEEE/ACM Int. Conference on Program Comprehension, May 2021, pp.230-241. https://doi.org/10.1109/ICPC52881.2021.00030.

  22. Magnoni S. An approach to measure community smells in software development communities [Master Thesis]. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 2016.

  23. Catolino G, Palomba F, Tamburri D A, Serebrenik A, Ferrucci F. Gender diversity and community smells: Insights from the trenches. IEEE Softw., 2020, 37(1): 10-16. https://doi.org/10.1109/MS.2019.2944594.

    Article  Google Scholar 

  24. Jongeling R, Datta S, Serebrenik A. Choosing your weapons: On sentiment analysis tools for software engineering research. In Proc. the 31st IEEE Int. Conference on Software Maintenance and Evolution, September 29–October 1, 2015, pp.531-535. 10.1109/ICSM.2015.7332508.

  25. Ortu M, Destefanis G, Kassab M, Counsell S, Marchesi M, Tonelli R. Would you mind fixing this issue? — An empirical analysis of politeness and attractiveness in software developed using agile boards. In Proc. the 16th Int. Conference on Agile Software Development, May 2015, pp.129-140. https://doi.org/10.1007/978-3-319-18612-2_11.

  26. Ortu M, Adams B, Destefanis G, Tourani P, Marchesi M, Tonelli R. Are bullies more productive? Empirical study of affectiveness vs. issue fixing time. In Proc. the 12th IEEE/ACM Working Conference on Mining Software Repositories, May 2015, pp.303-313. https://doi.org/10.1109/MSR.2015.35.

  27. Bell R M, Ostrand T J, Weyuker E J. The limited impact of individual developer data on software defect prediction. Empir. Softw. Eng., 2013, 18(3): 478-505. https://doi.org/10.1007/s10664-011-9178-4.

    Article  Google Scholar 

  28. Catolino G, Palomba F, Tamburri D A. The secret life of software communities: What we know and what we don’t know. In Proc. the 18th Belgium-Netherlands Software Evolution Workshop, November 2019.

  29. Yang Y, Zhou Y, Liu J, Zhao Y, Lu H, Xu L, Xu B, Leung H. Effort-aware just-in-time defect prediction: Simple unsupervised models could be better than supervised models. In Proc. the 24th ACM SIGSOFT Int. Symp. Foundations of Software Engineering, November 2016, pp.157-168. https://doi.org/10.1145/2950290.2950353.

  30. McIntosh S, Kamei Y. Are fix-inducing changes a moving target? A longitudinal case study of just-in-time defect prediction. IEEE Trans. Softw. Eng., 2018, 44(5): 412-428. https://doi.org/10.1109/TSE.2017.2693980.

    Article  Google Scholar 

  31. Spadini D, Aniche M F, Bacchelli A. PyDriller: Python framework for mining software repositories. In Proc. the 26th ACM Joint Meeting on European Software Engineering Conference and Symp. the Foundations of Software Engineering, November 2018, pp.908-911. https://doi.org/10.1145/3236024.3264598.

  32. Jiarpakdee J, Tantithamthavorn C, Grundy J. Practitioners’ perceptions of the goals and visual explanations of defect prediction models. In Proc. the 18th IEEE/ACM Int. Conference on Mining Software Repositories, May 2021, pp.432-443. https://doi.org/10.1109/MSR52588.2021.00055.

  33. Jiarpakdee J, Tantithamthavorn C, Dam H K, Grundy J. An empirical study of model-agnostic techniques for defect prediction models. IEEE Trans. Softw. Eng.. https://doi.org/10.1109/TSE.2020.2982385.

  34. Rajbahadur G K, Wang S, Ansaldi G, Kamei Y, Hassan A E. The impact of feature importance methods on the interpretation of defect classifiers. IEEE Trans. Softw. Eng.. https://doi.org/10.1109/TSE.2021.3056941.

  35. Lundberg S M, Erion G, Chen H, DeGrave A, Prutkin J M, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S I. From local explanations to global understanding with explainable ai for trees. Nat. Mach. Intell., 2020, 2(1): 56-67. https://doi.org/10.1038/s42256-019-0138-9.

    Article  Google Scholar 

  36. Graziotin D, Fagerholm F, Wang X, Abrahamsson P. What happens when software developers are (un)happy. J. Syst. Softw., 2018, 140: 32-47. https://doi.org/10.1016/j.jss.2018.02.041.

    Article  Google Scholar 

  37. Graziotin D, Wang X, Abrahamsson P. Software developers, moods, emotions, and performance. IEEE Softw., 2014, 31(4): 24-27. https://doi.org/10.1109/MS.2014.94.

    Article  Google Scholar 

  38. Thelwall M, Buckley K, Paltoglou G. Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Tec., 2012, 63(1): 163-173. https://doi.org/10.1002/asi.21662.

    Article  Google Scholar 

  39. Danescu-Niculescu-Mizil C, Sudhof M, Jurafsky D, Leskovec J, Potts C. A computational approach to politeness with application to social factors. In Proc. the 51st Annual Meeting of the Association for Computational Linguistics, August 2013, pp.250-259.

  40. De Smedt T, Daelemans W. Pattern for Python. J. Mach. Learn. Res., 2012, 13: 2063-2067.

    Google Scholar 

  41. Islam M R, Zibran M F. Towards understanding and exploiting developers’ emotional variations in software engineering. In Proc. the 14th IEEE Int. Conference on Software Engineering Research, Management and Applications, June 2016, pp.185-192. https://doi.org/10.1109/SERA.2016.7516145.

  42. Tantithamthavorn C, McIntosh S, Hassan A E, Matsumoto K. An empirical comparison of model validation techniques for defect prediction models. IEEE Trans. Softw. Eng., 2017, 43(1): 1-18. https://doi.org/10.1109/TSE.2016.2584050.

    Article  Google Scholar 

  43. Scott A J, Knott M. A cluster analysis method for grouping means in the analysis of variance. Biometrics, 1974, 30(3): 507-512. https://doi.org/10.2307/2529204.

    Article  MATH  Google Scholar 

  44. Pedregosa F, Varoquaux G, Gramfort A et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res., 2011, 12: 2825-2830.

    MathSciNet  MATH  Google Scholar 

  45. Palomba F, Zanoni M, Fontana F A, De Lucia A, Oliveto R. Toward a smell-aware bug prediction model. IEEE Trans. Softw. Eng., 2019, 45(2): 194-218. https://doi.org/10.1109/TSE.2017.2770122.

    Article  Google Scholar 

  46. Esteves G, Figueiredo E, Veloso A, Viggiato M, Ziviani N. Understanding machine learning software defect predictions. Autom. Softw. Eng., 2020, 27(3): 369-392. https://doi.org/10.1007/s10515-020-00277-4.

    Article  Google Scholar 

  47. Shapley L S. A value for n-person games. In Contributions to the Theory of Games II, Annals of Mathematics Studies, Kuhn H W, Tucker A W (eds.), Princeton University Press, 1953, pp.307-317.

  48. Palomba F, Panichella A, Zaidman A, Oliveto R, Lucia A D. The scent of a smell: An extensive comparison between textual and structural smells. IEEE Trans. Softw. Eng., 2018, 44(10): 977-1000. https://doi.org/10.1109/TSE.2017.2752171.

    Article  Google Scholar 

  49. Kirbas S, Caglayan B, Hall T, Counsell S, Bowes D, Sen A, Bener A. The relationship between evolutionary coupling and defects in large industrial software. J. Softw.: Evol. Process, 2017, 29(4): Article No. e1842. https://doi.org/10.1002/smr.1842.

  50. Chicco D, Warrens M J, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Comput. Sci., 2021, 7: Article No. e263. https://doi.org/10.7717/peerj-cs.623.

  51. Yu X, Bennin K E, Liu J, Keung J W, Yin X, Xu Z. An empirical study of learning to rank techniques for effort-aware defect prediction. In Proc. the 26th IEEE Int. Conference on Software Analysis, Evolution and Reengineering, February 2019, pp.298-309. https://doi.org/10.1109/SANER.2019.8668033.

  52. Saini M, Kaur K. Fuzzy analysis and prediction of commit activity in open source software projects. IET Softw., 2016, 10(5): 136-146. https://doi.org/10.1049/iet-sen.2015.0087.

    Article  Google Scholar 

  53. Manzano M, Ayala C, Gómez C, Cuesta L L. A software service supporting software quality forecasting. In Proc. the 19th IEEE Int. Conference on Software Quality, Reliability and Security Companion, July 2019, pp.130-132. https://doi.org/10.1109/QRS-C.2019.00037.

  54. Ahammed T, Asad M, Sakib K. Understanding the involvement of developers in missing link community smell: An exploratory study on Apache projects. In Proc. the 8th Int. Workshop on Quantitative Approaches to Software Quality, December 2020, pp.64-70.

  55. Hofmann H, Wickham H, Kafadar K. Letter-value plots: Boxplots for large data. J. Comput. Graph. Stat., 2017, 26(3): 469-477. https://doi.org/10.1080/10618600.2017.1305277.

    Article  MathSciNet  Google Scholar 

  56. Graziotin D, Wang X, Abrahamsson P. Happy software developers solve problems better: Psychological measurements in empirical software engineering. PeerJ, 2014, 2: Article No. e289. https://doi.org/10.7717/peerj.289.

  57. Müller S C, Fritz T. Stuck and frustrated or in flow and happy: Sensing developers’ emotions and progress. In Proc. the 37th IEEE/ACM Int. Conference on Software Engineering, May 2015, pp.688-699. https://doi.org/10.1109/ICSE.2015.334.

  58. Lin B, Zampetti F, Bavota G, Di Penta M, Lanza M, Oliveto R. Sentiment analysis for software engineering: How far can we go? In Proc. the 40th IEEE/ACM Int. Conference on Software Engineering, May 27–June 3, 2018, pp.94-104. https://doi.org/10.1145/3180155.3180195.

  59. Jiarpakdee J, Tantithamthavorn C, Treude C. AutoSpearman: Automatically mitigating correlated software metrics for interpreting defect models. In Proc. the 34th IEEE Int. Conference on Software Maintenance and Evolution, September 2018, pp.92-103. https://doi.org/10.1109/ICSME.2018.00018.

Download references

Author information

Author notes

  1. Zhi-Qing Shao helped with the original idea and theoretic design. Gui-Sheng Fan significantly helped with the implementation and writing. They both guided the work of the paper and contributed equally.

Authors and Affiliations

  1. Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Zi-Jie Huang, Zhi-Qing Shao, Gui-Sheng Fan, Hui-Qun Yu, Xing-Guang Yang & Kang Yang

  2. Shanghai Key Laboratory of Computer Software Testing and Evaluating, Shanghai, 200237, China

    Gui-Sheng Fan

  3. Shanghai Engineering Research Center of Smart Energy, Shanghai, 200237, China

    Hui-Qun Yu

Authors
  1. Zi-Jie Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhi-Qing Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gui-Sheng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui-Qun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xing-Guang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhi-Qing Shao or Gui-Sheng Fan.

Supplementary Information

ESM 1

(PDF 865 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, ZJ., Shao, ZQ., Fan, GS. et al. Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics. J. Comput. Sci. Technol. 37, 182–206 (2022). https://doi.org/10.1007/s11390-021-1596-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-021-1596-1

Keywords

Search

Navigation