The Application of Change Indicators in Mining Software Repositories

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

This paper presents a framework to identify a problematic or uncontrollable rise in the number of software change requests and to take right actions to fix it. With this work, we propose the use of an acceptable limit number of change requests as indicators to track the evolution of software change requests. The change indicators are used to identify a periodical sharp rise in demands of change requests fast enough and provide the right fix on time. Not only these indicators track the evolution of change request, but they also help to identify the right solution to address the triggers of these change requests.

Keywords

Change requests Indicators Maintenance 
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References

  1. 1.
    IEEE Std 1219-1998: IEEE Standard for Software Maintenance. IEEE (1998)Google Scholar
  2. 2.
    Brand, S.: How Buildings Learn: What Happens After They’re Built. Rev. pbk. ed. Phoenix, London (1997)Google Scholar
  3. 3.
    Antoniol, G., Ayari, K., Di Penta, M., Khomh, F., Guéhéneuc, Y.-G.: Is it a bug or an enhancement? A text-based approach to classify change requests. In: Proceedings of the 2008 Conference of the Center for Advanced Studies on Collaborative Research: Meeting of Minds, p. 23 (2008)Google Scholar
  4. 4.
    Métrailler, A., Estier, T.: EVOLIS framework: a method to study information systems evolution records. In: 2014 47th Hawaii International Conference on System Sciences (HICSS), pp. 3798–3807 (2014)Google Scholar
  5. 5.
    Di Lucca, G.A., Di Penta, M., Gradara, S.: An approach to classify software maintenance requests. In: 2002 Proceedings of International Conference on Software Maintenance, pp. 93–102 (2002)Google Scholar
  6. 6.
    Anvik, J., Hiew, L., Murphy, G.C.: Who should fix this bug? In: Proceedings of the 28th International Conference on Software Engineering, pp. 361–370 (2006)Google Scholar
  7. 7.
    Anvik, J., Hiew, L., Murphy, G.C.: Coping with an open bug repository, pp. 35–39 (2005)Google Scholar
  8. 8.
    Chapin, N., Hale, J.E., Khan, K.M., Ramil, J.F., Tan, W.-G.: Types of software evolution and software maintenance. J. Softw. Maint. Evol. Res. Pract. 13(1), 3–30 (2001)CrossRefGoogle Scholar
  9. 9.
    Swanson, E.B.: The dimensions of maintenance. In: Proceedings of the 2nd International Conference on Software Engineering, pp. 492–497 (1976)Google Scholar
  10. 10.
    Xia, X., Lo, D., Wang, X., Zhou, B.: Dual analysis for recommending developers to resolve bugs. J. Softw. Evol. Process 27(3), 195–220 (2015)CrossRefGoogle Scholar
  11. 11.
    da Mota Silveira Neto, P.A., Lucrédio, D., Vale, T., de Almeida, E.S., de Lemos Meira, S.R.: The bug report duplication problem: an exploratory study. Softw. Qual. J. 21(1), 39–66 (2013)CrossRefGoogle Scholar
  12. 12.
    Butler, R.W., Finelli, G.B.: The infeasibility of quantifying the reliability of life-critical real-time software. IEEE Trans. Softw. Eng. 19(1), 3–12 (1993)CrossRefGoogle Scholar
  13. 13.
    Ryan, K.T.: Software processes for a changing world: Software processes for a changing world. J. Softw. Evol. Process 28(4), 236–240 (2016)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ulziit, B., Warraich, Z.A., Gencel, C., Petersen, K.: A conceptual framework of challenges and solutions for managing global software maintenance. J. Softw. Evol. Process 27(10), 763–792 (2015)CrossRefGoogle Scholar
  15. 15.
    Lehman, M.M.: Programs, life cycles, and laws of software evolution. Proc. IEEE 68(9), 1060–1076 (1980)CrossRefGoogle Scholar
  16. 16.
    Jaafar, F., Guéhéneuc, Y.-G., Hamel, S., Antoniol, G.: Detecting asynchrony and dephase change patterns by mining software repositories. J. Softw. Evol. Process 26(1), 77–106 (2014)CrossRefGoogle Scholar
  17. 17.
    Hochstein, A., Rüdiger, Z., Walter, B.: ITIL as common practice reference model for IT service management: formal assessment and implications for practice. In: EEE 2005 Proceedings of e-Technology, e-Commerce and e-Service (2005)Google Scholar
  18. 18.
    Kagdi, H., Gethers, M., Poshyvanyk, D., Hammad, M.: Assigning change requests to software developers. J. Softw. Evol. Process 24(1), 3–33 (2012)CrossRefGoogle Scholar
  19. 19.
    Cavalcanti, Y.C., da Mota Silveira Neto, P.A., Machado, I.D.C., Vale, T.F., de Almeida, E.S., Meira, S.R.D.L.: Challenges and opportunities for software change request repositories: a systematic mapping study. J. Softw. Evol. Process 26(7), 620–653 (2014)CrossRefGoogle Scholar
  20. 20.
    Canfora, G., Cerulo, L.: Impact analysis by mining software and change request repositories, pp. 29–29 (2005)Google Scholar
  21. 21.
    Aljarah, I., Banitaan, S., Abufardeh, S., Jin, W., Salem, S.: Selecting discriminating terms for bug assignment: a formal analysis, pp. 1–7 (2011)Google Scholar
  22. 22.
    Yin, R.K.: Case Study Research, Design and Methods, 2nd edn. Sage Publications, Beverly Hills (1994)Google Scholar
  23. 23.
    Benbasat, I., Goldstein, D.K., Mead, M.: The case research strategy in studies of information systems. MIS Q. 11(3), 369 (1987)CrossRefGoogle Scholar
  24. 24.
  25. 25.
  26. 26.
    Kanwal, J., Maqbool, O.: Bug prioritization to facilitate bug report triage. J. Comput. Sci. Technol. 27(2), 397–412 (2012)CrossRefGoogle Scholar
  27. 27.
    Tian, Y., Lo, D., Sun, C.: DRONE: predicting priority of reported bugs by multi-factor analysis, pp. 200–209 (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.DESIUniversity of LausanneLausanneSwitzerland

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