Advertisement

Assessing the Software Developer’s Quality Using Fuzzy Estimates

  • Tatiana Afanasieva
  • Vlad Moiseev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

The software developer’s quality in the work has significant impact on the quality of a software, therefore the assessing developer’s quality is actual. The results of that assessment need to be understandable and should described developer’s quality in static and in dynamics. This article aimed on design of a methodology for obtaining these results. The proposed methodology for evaluating the quality of software developers is based on task metrics of a repository and uses fuzzy set theory for creating the linguistic estimates of developer’s quality. The stages of proposed methodology are described in application to real project, obtained results meet the requirements and show usability in software developer’s quality assessing.

Keywords

Software quality Developer Fuzzy assessment 

Notes

Acknowledgments

The authors acknowledge that this paper was partially supported by the Russian Foundation of Basic Research, projects № 16-07-00535 and № 16-47-730715.

References

  1. 1.
    Mockus, A.: Software support tools and experimental work. In: Basili, V., et al. (eds.): Empirical Software Engineering Issues: Critical Assessments and Future Directions. LNCS, vol. 4336, pp. 91–99. Springer, Heidelberg (2007)Google Scholar
  2. 2.
    Cosentino, V., Luis, J., Cabot J.: Findings from GitHub: methods, datasets and limitations. In: Proceedings of 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories, MSR 2016, 14–15 May 2016, Austin, TX, USA (2016).  https://doi.org/10.1145/2901739.2901776
  3. 3.
    Altinger, H., Siegl, S., Dajsurent, Y., Wotawa, F.: A novel industry grade dataset for fault prediction based on model-driven developed automotive embedded software. In: Proceedings of 12th Working Conference on Mining Software Repositories 2015, At Florence, Italy, MSR 2015 (2015).  https://doi.org/10.1109/msr.2015.72
  4. 4.
    McCabe, T.J.: A complexity measure. IEEE Trans. Softw. Eng. 4, 308–320 (1976)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Halstead, M.H.: Elements of Software Science (Operating and Programming Systems Series). Elsevier Science Inc., New York (1977)zbMATHGoogle Scholar
  6. 6.
    Kikas, R., Dumas, M., Pfahl, D.: Using dynamic and contextual features to predict issue lifetime in GitHub projects. In: Proceedings of 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories, MSR 2016, 14–15 May 2016, Austin, TX, USA (2016). http://dx.doi.org/10.1145/2901739.2901751
  7. 7.
    Vasilescu, B., Serebrenik, A., Filkov, V.: A data set for social diversity studies of GitHub teams. In: Proceedings of 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories (RSS) (2015).  https://doi.org/10.1109/msr.2015.77
  8. 8.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Memorandum ERL-M 411 Berkeley, October 1973Google Scholar
  9. 9.
    Afanasieva, T., Yarushkina, N., Gyskov, G.: ACL-scale as a tool for preprocessing of many-valued contexts. In: Proceedings of Second International Workshop on Soft Computing Applications and Knowledge Discovery (SCAD 2016), Moscow, Russia, pp. 2–11, 18–22 July 2016Google Scholar
  10. 10.
    Afanasieva, T., Sapunkov, A.: Selection of time series forecasting model using a combination of linguistic and numerical criteria. In: Proceedings of 2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT-2016), 12–14 October, Baku, Azerbaijan, pp. 341–345 (2016).  https://doi.org/10.1109/icaict.2016.7991715
  11. 11.
    Afanasieva, T., Yarushkina, N., Sibirev, I.: Time series clustering using numerical and fuzzy representations. In: Proceedings of Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS 2017), Otsu, Shiga, Japan, 27–30 June 2017.  https://doi.org/10.1109/ifsa-scis.2017.8023356

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Ulyanovsk State Technical UniversityUlyanovskRussia

Personalised recommendations