[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

A Novel Way of Assigning Software Bug Priority Using Supervised Classification on Clustered Bugs Data

  • Conference paper
Advances in Intelligent Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 320))

Abstract

Bug Triaging is an important part of testing process in software development organizations. But it takes up considerable amount of time of the Bug Triager, costing time and resources of the organization. Hence it is worth while to develop an automated system to address this issue. Researchers have addressed various aspects of this by using techniques of data mining, like classification etc. Also there is a study which claims that when classification is done on the data which is previously clustered; it significantly improves its performance. In this work, this approach has been used for the first time in the field of software testing for predicting the priority of the software bugs to find if classifier performance improves when it is preceded with clustering. Using this system, clustering was performed on problem title attribute of the bugs to group similar bugs together using clustering algorithms. Classification was then applied to the clusters obtained, to assign priority to the bugs based on their attributes severity or component using classification algorithms. It was then studied that which combination of clustering and classification algorithms used provided the best results.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 143.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 179.99
Price includes VAT (United Kingdom)
  • 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ahsan, S., Ferzund, J., Wotawa, F.: Automatic Software Bug Triage System (BTS) Based on Latent Semantic Indexing and Support Vector Machine. In: Proceedings of the 4th International Conference on Software Engineering Advances, pp. 216–221 (2009)

    Google Scholar 

  2. Anvik, J., Murphy, G.C.: Reducing the Effort of Bug Report Triage: Recommenders for Development-Oriented Decisions. ACM Transactions on Software Engineering and Methodology 20(3), 1–35 (2011)

    Article  Google Scholar 

  3. 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 

  4. Banerjee, S., Cukic, B., Adjeroh, D.: Automated Duplicate Bug Report Classification using Subsequence Matching. In: Proceedings of IEEE 14th International Symposium on High-Assurance Systems Engineering, pp. 74–81 (2012)

    Google Scholar 

  5. Candillier, L., Tellier, I., Torre, F., Bousquet, O.: Cascade Evaluation of Clustering Algorithms. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 574–581. Springer, Heidelberg (2006)

    Google Scholar 

  6. Chaturvedi, K.K., Singh, V.B.: Determining Bug Severity using Machine Learning Techniques. In: Proceedings of the 6th International Conference on Software Engineering, pp. 1–6 (2012)

    Google Scholar 

  7. Čubranić, D., Murphy, G.C.: Automatic bug triage using text categorization. In: Proceedings of the Sixteenth International Conference on Software Engineering and Knowledge Engineering, pp. 92–97 (2004)

    Google Scholar 

  8. Dang, Y., Wu, R., Zhang, H., Zhang, D., Nobel, P.: ReBucket: A Method for Clustering Duplicate Crash Reports Based on Call Stack Similarity. In: Proceedings of International Conference on Software Engineering, pp. 1084–1093 (2012)

    Google Scholar 

  9. Dhiman, P., Manish, M., Chawla, R.: A Clustered Approach to Analyze the Software Quality using Software Defects. In: Proceedings of 2nd International Conference on Advanced Computing & Communication Technologies, pp. 36–40 (2012)

    Google Scholar 

  10. Gegick, M., Rotella, P., Xie, T.: Identifying Security Bug Reports via Text Mining: An Industrial Case Study. In: Proceedings of 7th IEEE Working Conference on Mining Software Repositories, pp. 11–20 (2010)

    Google Scholar 

  11. Goyal, N., Aggarwal, N., Dutta, M.: A Novel Way of Assigning Software Bug Priority using Classification and Clustering. In: Proceedings of International conference on Computer Networks and Information Technology, pp. 535–547 (2014)

    Google Scholar 

  12. Hanchate, D.B., Sayyad, S., Shinde, S.A.: Defect classification as problem classification for Quality control in the software project management by DTL. In: Proceedings of the 2nd International Conference on Computer Engineering and Technology, pp. 623–627 (2010)

    Google Scholar 

  13. Jalbert, N., Weimer, W.: Automated Duplicate Detection for Bug Tracking Systems. In: International Conference on Dependable Systems & Networks, pp. 52–61 (2008)

    Google Scholar 

  14. Kanwal, J., Maqbool, O.: Bug Prioritization to Facilitate Bug Report Triage. Journal of Computer Science and Technology 27(2), 397–412 (2012)

    Article  Google Scholar 

  15. Lamkanfi, A., Demeyer, S., Giger, E., Goethals, B.: Predicting the Severity of a Reported Bug. In: Proceedings of 7th IEEE Working Conference on Mining Software Repositories, pp. 1–10 (2010)

    Google Scholar 

  16. Lamkanfi, A., Demeyer, S., Soetens, Q.D., Verdonck, T.: Comparing Mining Algorithms for Predicting the Severity of a Reported Bug. In: Proceedings of the 15th European Conference on Software Maintenance and Reengineering, pp. 249–258 (2011)

    Google Scholar 

  17. Nagwani, N.K., Verma, S.: Software Bug Classification using Suffix Tree Clustering (STC) Algorithm. International Journal of Computer Science and Technology 2(1), 36–41 (2011)

    Google Scholar 

  18. Nagwani, N.K., Verma, S.: CLUBAS: An Algorithm and Java Based Tool for Software Bug Classification Using Bug Attributes Similarities. Journal of Software Engineering and Applications 5(6), 436–447 (2012)

    Article  Google Scholar 

  19. Neelofar, M., Javed, Y., Mohsin, H.: An Automated Approach for Software Bug Classification. In: Proceedings of the 6th International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 414–419 (2012)

    Google Scholar 

  20. Nigam, A., Nigam, B., Bhaisare, C., Arya, N.: Classifying the Bugs Using Multi-Class Semi Supervised Support Vector Machine. In: Proceedings of the International Conference on Pattern Recognition, Informatics and Medical Engineering, pp. 393–397 (2012)

    Google Scholar 

  21. Pelleg, D., Moore, A.W.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: Proceedings of the 17th International Conference on Machine Learning, pp. 727–734 (2000)

    Google Scholar 

  22. Podgurski, A., Leon, D., Francis, P., Sun, J., Wang, B., Masri, W., Minch, M.: Automated support for classifying software failure reports. In: Proceedings of the 25th International Conference on Software Engineering, pp. 465–476 (2003)

    Google Scholar 

  23. Runeson, P., Alexandersson, M., Nyholm, O.: Detection of Duplicate Defect Reports Using Natural Language Processing. In: Proceedings of 29th International Conference on Software Engineering, pp. 499–510 (2007)

    Google Scholar 

  24. Rus, V., Nan, X., Shiva, S., Chen, Y.: Clustering of Defect Reports Using Graph Partitioning Algorithms. In: Proceedings of the 21st International Conference on Software Engineering and Knowledge Engineering, pp. 442–445 (2009)

    Google Scholar 

  25. Sharma, M., Bedi, P., Chaturvedi, K.K., Singh, V.B.: Predicting the Priority of a Reported Bug using Machine Learning Techniques and Cross Project Validation. In: Proceedings of the 12th International Conference on Intelligent Systems Design and Application, pp. 539–546 (2012)

    Google Scholar 

  26. Vilalta, R., Achari, M.K., Eick, C.F.: Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers. In: Proceedings of the 3rd International Conference on Data Mining, pp. 673–676 (2003)

    Google Scholar 

  27. Wang, X., Zhang, L., Xie, T., Anvik, J., Sun, J.: An approach to detecting duplicate bug reports using natural language and execution information. In: Proceedings of ACMIEEE 30th International Conference on Software Engineering, pp. 461–470 (2008)

    Google Scholar 

  28. Yu, L., Tsai, W.-T., Zhao, W., Wu, F.: Predicting Defect Priority Based on Neural Networks. In: Cao, L., Zhong, J., Feng, Y. (eds.) ADMA 2010, Part II. LNCS, vol. 6441, pp. 356–367. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  29. Platt, J.C., Schlökopf, B., Burges, C., Smola, A.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods—Support Vector Learning. MIT Press, Cambridge (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Goyal, N., Aggarwal, N., Dutta, M. (2015). A Novel Way of Assigning Software Bug Priority Using Supervised Classification on Clustered Bugs Data. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11218-3_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11217-6

  • Online ISBN: 978-3-319-11218-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics