Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2017 | Vol. 11, nr 1 | 103--116
Tytuł artykułu

NRFixer: Sentiment Based Model for Predicting the Fixability of Non-Reproducible Bugs

Treść / Zawartość
Warianty tytułu
Języki publikacji
Software maintenance is an essential step in software development life cycle. Nowadays, software companies spend approximately 45% of total cost in maintenance activities. Large software projects maintain bug repositories to collect, organize and resolve bug reports. Sometimes it is difficult to reproduce the reported bug with the information present in a bug report and thus this bug is marked with resolution non-reproducible (NR). When NR bugs are reconsidered, a few of them might get fixed (NR-to-fix) leaving the others with the same resolution (NR). To analyse the behaviour of developers towards NR-to-fix and NR bugs, the sentiment analysis of NR bug report textual contents has been conducted. The sentiment analysis of bug reports shows that NR bugs’ sentiments incline towards more negativity than reproducible bugs. Also, there is a noticeable opinion drift found in the sentiments of NR-to-fix bug reports. Observations driven from this analysis were an inspiration to develop a model that can judge the fixability of NR bugs. Thus a framework, NRFixer, which predicts the probability of NR bug fixation, is proposed. NRFixer was evaluated with two dimensions. The first dimension considers meta-fields of bug reports (model-1) and the other dimension additionally incorporates the sentiments (model-2) of developers for prediction. Both models were compared using various machine learning classifiers (Zero-R, naive Bayes, J48, random tree and random forest). The bug reports of Firefox and Eclipse projects were used to test NRFixer. In Firefox and Eclipse projects, J48 and Naive Bayes classifiers achieve the best prediction accuracy, respectively. It was observed that the inclusion of sentiments in the prediction model shows a rise in the prediction accuracy ranging from 2 to 5% for various classifiers.

Opis fizyczny
Bibliogr. 22 poz., tab., rys.
  • [1] M. Erfani Joorabchi, M. Mirzaaghaei, and A. Mesbah, “Works for me! characterizing non-reproducible bug reports,” in Proceedings of the 11th Working Conference on Mining Software Repositories. ACM, 2014, pp. 62–71.
  • [2] E. Murphy-Hill, T. Zimmermann, C. Bird, and N. Nagappan, “The design space of bug fixes and how developers navigate it,” IEEE Transactions on Software Engineering, Vol. 41, No. 1, 2015, pp. 65–81.
  • [3] T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis,” in Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2005, pp. 347–354.
  • [4] 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.
  • [5] 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. ACM, 2014, pp. 262–271.
  • [6] 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. IBM Corp., 2014, pp. 34–44.
  • [7] D. Garcia, M.S. Zanetti, and F. Schweitzer, “The role of emotions in contributors activity: A case study on the Gentoo community,” in The Third International Conference on Cloud and Green Computing (CGC). IEEE, 2013, pp. 410–417.
  • [8] 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. ACM, 2014, pp. 348–351.
  • [9] 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. ACM, 2014, pp. 352–355.
  • [10] G. Destefanis, M. Ortu, S. Counsell, S. Swift, M. Marchesi, and R. Tonelli, “Software development: Do good manners matter?” PeerJ Computer Science, Vol. 2, 2016, p. e73.
  • [11] H. Valdivia Garcia and E. Shihab, “Characterizing and predicting blocking bugs in open source projects,” in Proceedings of the 11th Working Conference on Mining Software Repositories. ACM, 2014, pp. 72–81.
  • [12] E. Shihab, A. Ihara, Y. Kamei, W.M. Ibrahim, M. Ohira, B. Adams, A.E. Hassan, and K.i. Matsumoto, “Studying re-opened bugs in open source software,” Empirical Software Engineering,Vol. 18, No. 5, 2013, pp. 1005–1042.
  • [13] R. Hewett and P. Kijsanayothin, “On modeling software defect repair time,” Empirical Software Engineering, Vol. 14, No. 2, 2009, p. 165.
  • [14] P.J. Guo, T. Zimmermann, N. Nagappan, and B. Murphy, “Characterizing and predicting which bugs get fixed: An empirical study of Microsoft Windows,” in ACM/IEEE 32nd International Conference on Software Engineering, Vol. 1. IEEE, 2010, pp. 495–504.
  • [15] T. Zimmermann, N. Nagappan, P.J. Guo, and B. Murphy, “Characterizing and predicting which bugs get reopened,” in Proceedings of the 34th International Conference on Software Engineering. IEEE Press, 2012, pp. 1074–1083.
  • [16] Python NLTK sentiment analysis with text classification demo, (2016, Sep.). [Online].
  • [17] A. Padhye, Classification methods, (2016, Sep.).[Online]. Chapter5.html
  • [18] J.R. Quinlan, C4.5: programs for machine learning. Elsevier, 2014.
  • [19] L. Breiman, “Random forests,” Machine learning, Vol. 45, No. 1, 2001, pp. 5–32.
  • [20] J. Han, J. Pei, and M. Kamber, Data mining: concepts and techniques. Elsevier, 2011.
  • [21] M. Mitchell, An introduction to genetic algorithms. MIT press, 1998.
  • [22] R. Jongeling, S. Datta, and A. Serebrenik, “Choosing your weapons: On sentiment analysis tools for software engineering research,” in IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, 2015, pp. 531–535.
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.