Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Empirical Study and Motivation
Data Collection
Implications and Our Approach
Overview
Comparison Results
Discussions and Comparisons
Related Work
Conclusions
References

Fuzzy set and cache-based approach for bug triaging

Profile image of Tien NguyenTien Nguyen

2011, Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering - SIGSOFT/FSE '11

Abstract

Bug triaging aims to assign a bug to the most appropriate fixer. That task is crucial in reducing time and efforts in a bug fixing process. In this paper, we propose Bugzie, a novel approach for automatic bug triaging based on fuzzy set and cache-based modeling of the bug-fixing expertise of developers. Bugzie considers a software system to have multiple technical aspects, each of which is associated with technical terms. For each technical term, it uses a fuzzy set to represent the developers who are capable/competent of fixing the bugs relevant to the corresponding aspect. The fixing correlation of a developer toward a technical term is represented by his/her membership score toward the corresponding fuzzy set. The score is calculated based on the bug reports that (s)he has fixed, and is updated as the newly fixed bug reports are available. For a new bug report, Bugzie combines the fuzzy sets corresponding to its terms and ranks the developers based on their membership scores toward that combined fuzzy set to find the most capable fixers. Our empirical results show that Bugzie achieves significantly higher accuracy and time efficiency than existing state-of-the-art approaches.

Related papers

Novel Metrics for Bug Triage
Akila Govindasamy

Bug Triaging is a vital part of issue management systems. Bug triaging deals with assigning a developer the task of an incoming bug. This activity is error prone and time consuming if done manually. There is a need for automated support to accelerate this process. The current automated bug triaging systems exploits the text contents of the bug and the tossing relations among the developers. The automated bug triaging systems estimate the optimal bath between the first assignee of the bug and the bug resolver using the tossing relations. The metrics used for assessing the efficiency of bug triaging systems that are based on tossing relations is Mean number of Steps To Resolve (MSTR). This metric quantifies the number of steps reduced by the predicted path compared to the original path. It does not capture how far the retrieved path is in alignment with the actual path. MSTR does reveal the information regarding the extent to which the order of the developers in the retrieved path is in line with that of the original path. In addition, there are no indicators for measuring the strength of the retrieved path. In this paper, we propose two metrics (i) Path Similarity Metric which quantifies path alignment based on pair wise path alignment and (ii) Path Alignment Indicator that measures the effectiveness of the retrieved path based on degree centrality. The effectiveness of the two proposed metrics is validated using bug reports extracted from the Eclipse project.

View PDFchevron_right
DABT: A Dependency-aware Bug Triaging Method
Kritika Chhabra

Evaluation and Assessment in Software Engineering, 2021

In software engineering practice, fixing a bug promptly reduces the associated costs. On the other hand, the manual bug fixing process can be time-consuming, cumbersome, and error-prone. In this work, we introduce a bug triaging method, called Dependency-aware Bug Triaging (DABT), which leverages natural language processing and integer programming to assign bugs to appropriate developers. Unlike previous works that mainly focus on one aspect of the bug reports, DABT considers the textual information, cost associated with each bug, and dependency among them. Therefore, this comprehensive formulation covers the most important aspect of the previous works while considering the blocking effect of the bugs. We report the performance of the algorithm on three open-source software systems, i.e., EclipseJDT, LibreOffice, and Mozilla. Our result shows that DABT is able to reduce the number of overdue bugs up to 12%. It also decreases the average fixing time of the bugs by half. Moreover, it reduces the complexity of the bug dependency graph by prioritizing blocking bugs.

View PDFchevron_right
A time-based approach to automatic bug report assignment
Zarinah Mohd. Kasirun

Bug assignment is one of the important activities in bug triaging that aims to assign bugs to the appropriate developers for fixing. Many recommended automatic bug assignment approaches are based on text analysis methods such as machine learning and information retrieval methods. Most of these approaches use term-weighting techniques, such as term frequency-inverse document frequency (tf-idf), to determine the value of terms. However, the existing term-weighting techniques only deal with frequency of terms without considering the metadata associated with the terms that exist in software repositories. This paper aims to improve automatic bug assignment by using time-metadata in tf-idf (Time-tf-idf). In the Time-tf-idf technique, the recency of using the term by the developer is considered in determining the values of the developer expertise. An evaluation of the recommended automatic bug assignment approach that uses Time-tf-idf, called ABA-Time-tf-idf, was conducted on three open-source projects. The evaluation shows accuracy and mean reciprocal rank (MRR) improvements of up to 11.8% and 8.94%, respectively, in comparison to the use of tf-idf. Moreover, the ABA-Time-tf-idf approach outperforms the accuracy and MRR of commonly used approaches in automatic bug assignment by up to 45.52% and 55.54%, respectively. Consequently, consideration of time-metadata in term weighting reasonably leads to improvements in automatic bug assignment.

View PDFchevron_right

References (38)

  1. REFERENCES
  2. J. Al-Kofahi, A. Tamrawi, T. Nguyen, H. Nguyen, and T.N. Nguyen. Fuzzy Set Approach for Automatic Tagging in Evolving Software. In ICSM'10. IEEE CS, 2010.
  3. Apache. https://issues.apache.org/jira/
  4. J. Anvik, L. Hiew, and G. C. Murphy. Who should fix this bug? In ICSE '06, pages 361-370. ACM Press, 2006.
  5. O. Baysal, M. W. Godfrey, and R. Cohen. A bug you like: A framework for automated assignment of bugs. In ICPC'09, pages 297-298. IEEE CS, 2009.
  6. P. Bhattacharya and I. Neamtiu. Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging. In ICSM'10. IEEE CS, 2010.
  7. N. Bettenburg, S. Just, A. Schröter, C. Weiss, R. Premraj, T. Zimmermann. What makes a good bug report? In FSE'08, pages 308-318. ACM Press, 2008.
  8. N. Bettenburg, R. Premraj, T. Zimmermann, S. Kim. Duplicate bug reports considered harmful... really? In ICSM' 08, pages 337-345. IEEE CS, 2008.
  9. G. Canfora and L. Cerulo. How software repositories can help in resolving a new change request. In Workshop on Empirical Studies in Reverse Eng., 2005.
  10. D. Blei, A.Y. Ng, and M. Jordan. Latent Dirichlet Allocation. J. Mach. Learn. Res. 3 (2003), 993-1022.
  11. G. Canfora and L. Cerulo. Supporting change request assignment in open source development. In SAC'06: ACM symposium on Applied computing, pages 1767-1772. ACM Press, 2006.
  12. D. Cubranic and G. Murphy. Automatic bug triage using text categorization. In SEKE'04: the 16th International Conference on Software Engineering and Knowledge Engineering, pages 92-97. KSI Press, 2004.
  13. G. Di-Lucca, M. Di-Penta, and S. Gradara. An approach to classify software maintenance requests. In ICSM'02, pages 93-102, IEEE CS, 2002.
  14. FireFox. https://bugzilla.mozilla.org/
  15. FreeDesktop. https://bugs.freedesktop.org/
  16. M. Fischer, M. Pinzger, H. Gall. Analyzing and relating bug report data for feature tracking. In WCRE'03, pages 90-99. IEEE CS, 2003.
  17. Gcc. http://gcc.gnu.org/bugzilla/
  18. J. Han, M. Kamber, and J. Pei. Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems, 2006.
  19. L. Hiew. Assisted detection of duplicate bug reports. Master's thesis, University of British Columbia, 2006.
  20. P. Hooimeijer, W. Weimer. Modeling bug report quality. In ASE '07, pages 34-43. ACM Press, 2007.
  21. D. Jannach, M. Zanker, A. Felfernig, G. Friedrich. Recommender Systems: An Introduction. Cambridge University Press, 2011.
  22. G. Jeong, S. Kim, T. Zimmermann. Improving bug triage with bug tossing graphs. In FSE'09, pages 111-120. ACM Press, 2009.
  23. S. Kim, T. Zimmermann, J. Whitehead Jr., and A. Zeller. Predicting faults from cached history. In ICSE'07, pages 489-498. IEEE CS, 2007.
  24. S. Kim, E.J. Whitehead, Jr. How long did it take to fix bugs? In MSR '06, pages 173-174. ACM Press, 2006.
  25. G.J. Klir and Bo Yuan. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, 1995.
  26. A.J. Ko, A.B. Myers, D.H. Chau. A linguistic analysis of how people describe software problems. In VLHCC'06, pagess 127-134. IEEE CS, 2006.
  27. Z. Lin, F. Shu, Y. Yang, C. Hu, and Q. Wang. An empirical study on bug assignment automation using Chinese bug data. In ESEM'09, pages 451-455. IEEE CS, 2009.
  28. D. Matter, A. Kuhn, and O. Nierstrasz. Assigning bug reports using a vocabulary-based expertise model of developers. In MSR'09, pp. 131-140. IEEE CS, 2009.
  29. T. Menzies, A.Marcus. Automated severity assessment of software defect reports. In ICSM'08, pages 346-355. IEEE CS, 2008.
  30. Netbeans. http://netbeans.org/bugzilla/
  31. A. Podgurski, D. Leon, P. Francis, W. Masri, M. Minch, J. Sun, B. Wang. Automated support for classifying software failure reports. In ICSE'03, pages 465-475. IEEE CS, 2003.
  32. M. Rahman, G. Ruhe, T. Zimmermann. Optimized assignment of developers for fixing bugs an initial evaluation for Eclipse projects. In ESEM'09, pages 439-442. IEEE CS, 2009.
  33. P. Runeson, M. Alexandersson, O. Nyholm. Detection of duplicate defect reports using natural language processing. In ICSE '07, pp. 499-510. IEEE CS, 2007.
  34. C. Sun, D. Lo, X. Wang, J. Jiang, S.C. Khoo. A discriminative model approach for accurate duplicate bug report retrieval. In ICSE '10, pages 45-54. ACM Press, 2010.
  35. A. Tamrawi, T. Nguyen, J. Al-Kofahi, and T.N. Nguyen. Fuzzy Set-based Automatic Bug Triaging. In ICSE'11 (NIER). ACM, 2011.
  36. X. Wang, L. Zhang, T. Xie, J. Anvik, J. Sun. An approach to detecting duplicate bug reports using natural language and execution information. In ICSE'08, pages 461-470. ACM Press, 2008.
  37. C. Weiss, R. Premraj, T. Zimmermann, A. Zeller. How long will it take to fix this bug? In MSR'07, IEEE CS, 2007.
  38. Weka: Data mining software in Java. http://www.cs.waikato.ac.nz/ml/weka/.

Related papers

Fuzzy set-based automatic bug triaging
Tung Nguyen

Proceeding of the 33rd international conference on Software engineering - ICSE '11, 2011

Assigning a bug to the right developer is a key in reducing the cost, time, and efforts for developers in a bug fixing process. This assignment process is often referred to as bug triaging. In this paper, we propose Bugzie, a novel approach for automatic bug triaging based on fuzzy set-based modeling of bug-fixing expertise of developers. Bugzie considers a system to have multiple technical aspects, each is associated with technical terms. Then, it uses a fuzzy set to represent the developers who are capable/competent of fixing the bugs relevant to each term. The membership function of a developer in a fuzzy set is calculated via the terms extracted from the bug reports that (s)he has fixed, and the function is updated as new fixed reports are available. For a new bug report, its terms are extracted and corresponding fuzzy sets are union'ed. Potential fixers will be recommended based on their membership scores in the union'ed fuzzy set. Our preliminary results show that Bugzie achieves higher accuracy and efficiency than other state-of-the-art approaches.

View PDFchevron_right
Fuzzy set-based automatic bug triaging: NIER track
Tung Nguyen

… (ICSE), 2011 33rd …, 2011

Assigning a bug to the right developer is a key in reducing the cost, time, and efforts for developers in a bug fixing process. This assignment process is often referred to as bug triaging. In this paper, we propose Bugzie, a novel approach for automatic bug triaging based on fuzzy set-based modeling of bug-fixing expertise of developers. Bugzie considers a system to have multiple technical aspects, each is associated with technical terms. Then, it uses a fuzzy set to represent the developers who are capable/competent of fixing the bugs relevant to each term. The membership function of a developer in a fuzzy set is calculated via the terms extracted from the bug reports that (s)he has fixed, and the function is updated as new fixed reports are available. For a new bug report, its terms are extracted and corresponding fuzzy sets are union'ed. Potential fixers will be recommended based on their membership scores in the union'ed fuzzy set. Our preliminary results show that Bugzie achieves higher accuracy and efficiency than other state-of-the-art approaches.

View PDFchevron_right
Ranking of software developers based on expertise score for bug triaging
asmita yadav

Information and Software Technology, 2019

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  This work use metrics (priority weighted fixed issues, versatility and breadth index metrics and developer average bug fixing time) to evaluate developer individual performance and contribution assessment for bug fixing.  These metrics are applied on three bug reports features to generate developer expertise score which are priority, product name and bug time-stamp.  The proposed approach demonstrates that developer expertise plays an important role to fix the bug within minimum time and to reduce bug tossing length.  Performance evaluation of the DES is done on five open-source projects: Mozilla, Eclipse, Netbeans, Firefox and Freedesktop by using:  Three criteria i.e. priority, versatility and avg-fix-time.  Three similarity measures: feature-based, cosine-similarity, Jaccard  Calculate DES accuracy of reassignment and hit ratio.  DES comparison is done with other state-of-arts as well as against machine learning based bug triaging approaches namely: Naïve bayes, support vector machine and C4.5.

View PDFchevron_right
An Efficient Model for Effective Bug Triage
komal jadhav

2016

Most of the software companies needs to deal with large number of software bugs every day. Software bugs are unavoidable and fixing software bugs is an expensive task. The goal of effective bug triaging software is to assign potentially experienced developers to new-coming bug reports. To reduce time and cost of bug triaging, an automatic approach is proposed in this paper that predicts a developer with relevant experience to solve or fix the new coming bug report. In this paper, the five term selection methods on the accuracy of bug assignment are used. In addition, the load between developers based on their experience is re-balanced. The proposed system is built with intention to suggest or recommend the bug and not to automatically assign it. This allows a window to handle real time crisis that come up during project development lifecycle. IndexTerms— Mining software repositories, application of data pre-processing, data management in bug repositories, bug data reduction, feature...

View PDFchevron_right
A novel and improved developer rank algorithm for bug assignment
asmita yadav

International Journal of Intelligent Systems Technologies and Applications, 2020

Analytical studies on automatic bug triaging approach have the main objective to recommend appropriate developer for bug report with reduced bug tossing length, time and effort in bug resolution. In bug triaging process, if the first recommended developer cannot fix a bug, it is tossed to another developer and the tossing process is continued till the bug gets assigned and resolved. Existing approaches to the best of our knowledge have not considered developer's contributions and performance assessment metrics for bug triaging process. In this paper, we proposed a novel and improved two phase bug triager that involves a developer profile creation and assignment phases. In this, developer profile is built by using individual contributions (IC) and performance assessment (PA) metrics. Contribution and performance of a developer in prefixed bug reports are analysed to calculate a developer's weighted score. This score indicates the level of expertise to fix and resolve a newly reported bug. This approach is tested on two open source projects-Eclipse and Mozilla. Empirical results show that proposed approach has achieved a significantly higher F-score up to 90% for both projects and has effectively reduced bug tossing length up to 11.8% as compared to existing approaches.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved