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.
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References
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Jalbert, N., Weimer, W.: Automated Duplicate Detection for Bug Tracking Systems. In: International Conference on Dependable Systems & Networks, pp. 52–61 (2008)
Kanwal, J., Maqbool, O.: Bug Prioritization to Facilitate Bug Report Triage. Journal of Computer Science and Technology 27(2), 397–412 (2012)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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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
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DOI: https://doi.org/10.1007/978-3-319-11218-3_44
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