Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Soft Computing

ICAISC 2017: Artificial Intelligence and Soft Computing pp 31–42Cite as

Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

Abstract

The use of recommenders for bug report triage decisions is especially important in the context of large software development projects, where both the frequency of reported problems and a large number of active developers can pose problems in selecting the most appropriate developer to work on a certain issue. From a machine learning perspective, the triage problem of bug report assignment in software projects may be regarded as a classification problem which can be solved by a recommender system. We describe a highly scalable SVM-based bug report assignment recommender that is able to run on massive datasets. Unlike previous desktop-based implementations of bug report triage assignment recommenders, our recommender is implemented on a cloud platform. The system uses a novel sequence of machine learning processing steps and compares favorably with other SVM-based bug report assignment recommender systems with respect to prediction performance. We validate our approach on real-world datasets from the Netbeans, Eclipse and Mozilla projects.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    Data extracted from a Mozilla’s Bugzilla database dump as of March 4, 2016.

  2. 2.

    https://bugs.eclipse.org/bugs/.

  3. 3.

    http://2011.msrconf.org/msr-challenge.html.

  4. 4.

    https://netbeans.org/bugzilla/.

  5. 5.

    https://bugzilla.mozilla.org/.

  6. 6.

    https://www.bugzilla.org/docs/2.18/html/dbdoc.html.

  7. 7.

    The profiles table contains personal information, such as names and email addresses of the project members.

  8. 8.

    https://www.bugzilla.org/docs//2.18/html/dbdoc.html.

  9. 9.

    In cases where a bug report triager is also an active developer, the person will have been assigned to issues that are marked as FIXED.

  10. 10.

    Those terms tagged as either Noun {NN}, Noun Plural {NNS}, Proper Noun {NNP}, or Proper Noun Plural {NNPS}.

  11. 11.

    http://spark.apache.org/.

  12. 12.

    http://spark.apache.org/mllib/.

  13. 13.

    http://www.scala-lang.org/.

  14. 14.

    https://github.com/acflorea/columbugus.

  15. 15.

    https://cloud.google.com/dataproc/.

  16. 16.

    Without this reduction, the dataset was found to be too large for WEKA to process.

References

  1. Ahsan, S.N., Ferzund, J., Wotawa, F.: Automatic software bug triage system (BTS) based on latent semantic indexing and support vector machine. In: Fourth International Conference on Software Engineering Advances, ICSEA 2009, pp. 216–221, September 2009

    Google Scholar 

  2. Anvik, J.: Automating bug report assignment. In: Proceedings of the 28th International Conference on Software Engineering, ICSE 2006, NY, USA, pp. 937–940 (2006). http://doi.acm.org/10.1145/1134285.1134457

  3. Anvik, J., Hiew, L., Murphy, G.C.: Who should fix this bug? In: Proceedings of the 28th International Conference on Software Engineering, ICSE 2006, NY, USA, pp. 361–370 (2006). http://doi.acm.org/10.1145/1134285.1134336

  4. Anvik, J., Murphy, G.C.: Reducing the effort of bug report triage: recommenders for development-oriented decisions. ACM Trans. Softw. Eng. Methodol. 20(3), 10:1–10:35 (2011). http://doi.acm.org/10.1145/2000791.2000794

  5. Banitaan, S., Alenezi, M.: Tram: an approach for assigning bug reports using their metadata. In: 2013 Third International Conference on Communications and Information Technology, pp. 215–219, June 2013

    Google Scholar 

  6. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012). http://dl.acm.org/citation.cfm?id=2188385.2188395

  7. Bhattacharya, P., Neamtiu, I., Shelton, C.R.: Automated, highly-accurate, bug assignment using machine learning and tossing graphs. J. Syst. Softw. 85(10), 2275–2292 (2012)

    Article  Google Scholar 

  8. Blei, D.M., Ng, A.Y., Jordan, M.I., Lafferty, J.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  9. Cavalcanti, Y.C., da Mota Silveira Neto, P.A., do Carmo Machado, I., Vale, T.F., de Almeida, E.S., de Lemos Meira, S.R.: Challenges and opportunities for software change request repositories: a systematic mapping study. J. Softw. Evol. Process 26(7), 620–653 (2014). http://dx.doi.org/10.1002/smr.1639

  10. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm

  11. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). http://dx.doi.org/10.1023/A: 1022627411411

    MATH  Google Scholar 

  12. Cubranic, D., Murphy, G.C.: Automatic bug triage using text categorization. In: Proceedings of the Sixteenth International Conference on Software Engineering & Knowledge Engineering (SEKE 2004), Banff, Alberta, Canada, 20–24 June 2004, pp. 92–97 (2004)

    Google Scholar 

  13. Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)

    MATH  Google Scholar 

  14. Harris, D., Harris, S.: Digital Design and Computer Architecture, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco (2012)

    MATH  Google Scholar 

  15. Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28, 11–21 (1972)

    Article  Google Scholar 

  16. Nasim, S., Razzaq, S., Ferzund, J.: Automated change request triage using alpha frequency matrix. In: Frontiers of Information Technology (FIT), pp. 298–302, December 2011

    Google Scholar 

  17. Nguyen, T.T., Nguyen, A.T., Nguyen, T.N.: Topic-based, time-aware bug assignment. SIGSOFT Softw. Eng. Notes 39(1), 1–4 (2014). http://doi.acm.org/10.1145/2557833.2560585

    Article  Google Scholar 

  18. Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)

    Book  Google Scholar 

  19. Reis, C.R., de Mattos Fortes, R.P., Pontin, R., Fortes, M.: An overview of the software engineering process and tools in the mozilla project (2002)

    Google Scholar 

  20. Shinnar, A., Cunningham, D., Saraswat, V., Herta, B.: M3r: Increased performance for in-memory Hadoop jobs. Proc. VLDB Endow. 5(12), 1736–1747 (2012). http://dx.doi.org/10.14778/2367502.2367513

    Article  Google Scholar 

  21. Shokripour, R., Anvik, J., Kasirun, Z.M., Zamani, S.: Why so complicated? simple term filtering and weighting for location-based bug report assignment recommendation. In: Proceedings of the 10th Working Conference on Mining Software Repositories, MSR 2013, pp. 2–11. IEEE Press, Piscataway (2013). http://dl.acm.org/citation.cfm?id=2487085.2487089

  22. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009). http://dx.doi.org/10.1016/j.ipm.2009.03.002

    Article  Google Scholar 

  23. Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, vol. 1, pp. 173–180. Association for Computational Linguistics, Stroudsburg (2003). http://dx.doi.org/10.3115/1073445.1073478

  24. Wu, W., Zhang, W., Yang, Y., Wang, Q.: Drex: developer recommendation with k-nearest-neighbor search and expertise ranking. In: 2011 18th Asia Pacific Software Engineering Conference (APSEC), pp. 389–396, December 2011

    Google Scholar 

  25. Xia, X., Lo, D., Wang, X., Zhou, B.: Accurate developer recommendation for bug resolution. In: Proceedings of the 20th Working Conference on Reverse Engineering, pp. 72–81, October 2013

    Google Scholar 

  26. Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proceedings of the Fourteenth International Conference on Machine Learning, ICML 1997, pp. 412–420. Morgan Kaufmann Publishers Inc., San Francisco (1997). http://dl.acm.org/citation.cfm?id=645526.657137

Download references

Acknowledgment

The authors are grateful to the Mozilla Foundation for providing a dump of their Bugzilla database.

Author information

Authors and Affiliations

  1. Electronics and Computers Department, Transilvania University of Braşov, Braşov, Romania

    Adrian-Cătălin Florea & Răzvan Andonie

  2. Department of Mathematics and Computer Science, University of Lethbridge, Lethbridge, AB, Canada

    John Anvik

  3. Computer Science Department, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

Authors
  1. Adrian-Cătălin Florea
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John Anvik
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Răzvan Andonie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrian-Cătălin Florea .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Florea, AC., Anvik, J., Andonie, R. (2017). Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59060-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation