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Improved Searchability of Bug Reports Using Content-Based Labeling with Machine Learning of Sentences

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Knowledge-Based Software Engineering: 2018 (JCKBSE 2018)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 108))

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Abstract

Most stakeholders refer to past bug reports when they encounter a problem since bug reports contain useful information. However, searching for specific content is difficult because there are many bug reports. The desired content depends on the viewpoint of the stakeholder. A full text search includes unwanted content, which is costly. Although this problem has been previously noted, a solution has yet to be proposed. Herein we propose Content-based Labeling Method as a solution. This method organizes information in a bug report by labeling each sentence based on its contents, allowing stakeholders’ viewpoints to be considered. We evaluate the improvement in searchability. The Content-based Labeling Method improves the searchability according to the F-measure and precision of the experimental results.

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References

  1. Bettenburg, N., Just, S., Schroter, A., Weiss, C., Premraj, R., Zimmermann,T.: What makes a good bug report? In: Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 308–318 (2008)

    Google Scholar 

  2. Yusop, N.S.M.Y., Grundy, J., Vasa, R.: Reporting usability defects: do reporters report what software developers need? In: Proceedings of the 24th Australasian Software Engineering Conference, pp. 38–45 (2015)

    Google Scholar 

  3. Rastkar, S., Murphy, G.C., Murray, G.: Automatic summarization of bug reports. IEEE Trans. Softw. Eng. 40(4), 366–380 (2014)

    Article  Google Scholar 

  4. Rastkar, S., Murphy, G.C., Murray, G.: Summarizing software artifacts: a case study of bug reports. In: Proceedings of the 32nd International Conference on Software Engineering, pp. 505–514 (2010)

    Google Scholar 

  5. Ferreira, E.C., Vieira, V., Mourao, F.: Bug report summarization: an evaluation of ranking techniques. In: X Brazilian Symposium on Components, Architectures and Reuse Software, pp. 101–110 (2016)

    Google Scholar 

  6. Mani, S., Catherine, R., Sinha, V.S., Dubey, A.: AUSUM: approach for unsupervised bug report summarization. In: Proceedings of the 20th ACM SIGSOFT International Symposium on the Foundations of Software Engineering, pp. 1–11 (2012)

    Google Scholar 

  7. Yusop, N.S.M.Y., Grundy, J., Vasa, R.: Reporting usability defects: do reporters report what software developers need? In: Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, pp. 1–10 (2016)

    Google Scholar 

  8. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of the 10th European Conference on Machine Learning, pp. 137–142 (1998)

    Google Scholar 

  9. Zhang, H., Li, D.: Naïve Bayes text classifier. In: Proceedings of the IEEE International Conference on Granular Computing, pp. 708–711 (2007)

    Google Scholar 

  10. Wu, Q., Ye, Y., Zhang, H., Ng, M.K., Ho, S.-S.: ForesTexter: an efficient random forest algorithm for imbalanced text categorization. Knowl. Based Syst. 67, 105–116 (2014)

    Article  Google Scholar 

  11. Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)

    Article  Google Scholar 

  12. Scikit-learn machine learning in Python. http://scikit-learn.org/

  13. Gensim topic modelling for humans. https://radimrehurek.com/gensim/

  14. Garca, S., Herrera, F.: Evolutionary under-sampling for classification with imbalanced data sets: proposals and taxonomy. Evol. Comput. 17(3), 275–306 (2009)

    Article  Google Scholar 

  15. Hripcsak, G., Rothschild, A.S.: Agreement, the F-Measure, and reliability in information retrieval. J. Am. Inform. Assoc. 12(3), 296–298 (2005)

    Article  Google Scholar 

  16. Watanabe, Y., et al.: ID3P: iterative data-driven development of persona based on quantitative evaluation and revision. In: Proceedings of the 10th International Workshop on Cooperative and Human Aspects of Software Engineering, pp. 49–55 (2017)

    Google Scholar 

  17. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Waseda University, Tokyo, Japan

    Yuki Noyori, Hironori Washizaki & Yoshiaki Fukazawa

  2. Hitachi, Ltd. Research & Development Group, Tokyo, Japan

    Hideyuki Kanuka, Keishi Ooshima & Ryosuke Tsuchiya

Authors
  1. Yuki Noyori
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  2. Hironori Washizaki
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  3. Yoshiaki Fukazawa
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  4. Hideyuki Kanuka
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  5. Keishi Ooshima
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  6. Ryosuke Tsuchiya
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Corresponding author

Correspondence to Yuki Noyori .

Editor information

Editors and Affiliations

  1. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  2. Nippon Institute of Technology, Miyashiro, Japan

    Fumihiro Kumeno

  3. Department of Informatics, Ionian University, Corfu, Greece

    Konstantinos Oikonomou

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Noyori, Y., Washizaki, H., Fukazawa, Y., Kanuka, H., Ooshima, K., Tsuchiya, R. (2019). Improved Searchability of Bug Reports Using Content-Based Labeling with Machine Learning of Sentences. In: Virvou, M., Kumeno, F., Oikonomou, K. (eds) Knowledge-Based Software Engineering: 2018. JCKBSE 2018. Smart Innovation, Systems and Technologies, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-319-97679-2_8

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