Bug Triaging Based on Tossing Sequence Modeling
Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug r...
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| Published in | Journal of computer science and technology Vol. 34; no. 5; pp. 942 - 956 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.09.2019
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-9000 1860-4749 |
| DOI | 10.1007/s11390-019-1953-5 |
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| Abstract | Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose
i
T
riage
which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches. |
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| AbstractList | Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose iTriage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches. Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose iTriage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches. Keywords bug triaging, tossing sequence, software repository mining Bug triaging, which routes the bug reports to potential fixers, is an integral step in software development and maintenance. To make bug triaging more efficient, many researchers propose to adopt machine learning and information retrieval techniques to identify some suitable fixers for a given bug report. However, none of the existing proposals simultaneously take into account the following three aspects that matter for the efficiency of bug triaging: 1) the textual content in the bug reports, 2) the metadata in the bug reports, and 3) the tossing sequence of the bug reports. To simultaneously make use of the above three aspects, we propose i T riage which first adopts a sequence-to-sequence model to jointly learn the features of textual content and tossing sequence, and then uses a classification model to integrate the features from textual content, metadata, and tossing sequence. Evaluation results on three different open-source projects show that the proposed approach has significantly improved the accuracy of bug triaging compared with the state-of-the-art approaches. |
| Audience | Academic |
| Author | Xi, Sheng-Qu Xiao, Xu-Sheng Xu, Feng Yao, Yuan Lv, Jian |
| Author_xml | – sequence: 1 givenname: Sheng-Qu surname: Xi fullname: Xi, Sheng-Qu organization: State Key Laboratory for Novel Software Technology, Nanjing University – sequence: 2 givenname: Yuan surname: Yao fullname: Yao, Yuan email: y.yao@nju.edu.cn organization: State Key Laboratory for Novel Software Technology, Nanjing University – sequence: 3 givenname: Xu-Sheng surname: Xiao fullname: Xiao, Xu-Sheng organization: Department of Electrical Engineering and Computer Science, Case Western Reserve University – sequence: 4 givenname: Feng surname: Xu fullname: Xu, Feng organization: State Key Laboratory for Novel Software Technology, Nanjing University – sequence: 5 givenname: Jian surname: Lv fullname: Lv, Jian organization: State Key Laboratory for Novel Software Technology, Nanjing University |
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| Cites_doi | 10.4304/jsw.8.9.2185-2190 10.1016/j.infsof.2015.10.004 10.1049/iet-sen.2017.0159 10.1109/ISSRE.2014.17 10.1145/1368088.1368151 10.1109/ESEM.2013.24 10.1109/APSEC.2017.39 10.1109/ICSM.2008.4658082 10.1145/3275219.3275228 10.1109/CSMR.2013.42 10.1109/MSP.2012.2205597 10.1145/1718918.1718972 10.1145/1134285.1134336 10.1609/aaai.v25i1.7839 10.1109/COMPSAC.2011.80 10.1145/1595696.1595715 10.1145/1370750.1370786 10.1109/ICSE.2013.6606653 10.1109/APSEC.2014.43 10.1145/2025113.2025163 10.1109/CSMR.2012.48 10.1109/MSR.2013.6623999 10.18653/v1/N16-1174 10.1007/s11219-011-9164-5 10.1109/TSE.2016.2576454 10.1109/CSMR.2010.52 10.1109/TSE.2018.2864217 10.3115/v1/D14-1162 10.1145/2351676.2351687 10.1145/1961189.1961199 10.1109/ICSE.2007.32 10.1109/ESEM.2009.5315994 10.1109/ICSM.2010.5609736 10.1145/3106237.3117776 10.1109/COMPSAC.2014.16 10.1145/1321631.1321639 |
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| References | Zhang X F, Yao Y, Wang Y J, Xu F, Lu J. Exploring metadata in bug reports for bug localization. In Proc. the 24th Asia-Pacific Software Engineering Conference, December 2017, pp.328-337. HintonGDengLYuDDeep neural networks for acoustic modeling in speech recognition: The shared views of four research groupsIEEE Signal Processing Magazine2012296829710.1109/MSP.2012.2205597 Hiew L. Assisted detection of duplicate bug reports [Ph.D. Thesis]. University of British Columbia, 2006. Jeong G, Kim S, Zimmermann T. Improving bug triage with bug tossing graphs. In Proc. the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, August 2009, pp.111-120. Park J, Lee M W, Kim J, Hwang S, Kim S. CosTriage: A cost-aware triage algorithm for bug reporting systems. In Proc. the 25th AAAI Conference on Artificial Intelligence, August 2011, Article No. 22. Bertram D, Voida A, Greenberg S, Walker R. Communication, collaboration, and bugs: The social nature of issue tracking in small, collocated teams. In Proc. the 2010 ACM Conference on Computer Supported Cooperative Work, February 2010, pp.291-300. XiaXLoDDingYAl-KofahiJMNguyenTNWangXImproving automated bug triaging with specialized topic modelIEEE Transactions on Software Engineering201743327229710.1109/TSE.2016.2576454 Johnson R, Zhang T. Supervised and semi-supervised text categorization using LSTM for region embeddings. arXiv:1602.02373, 2016. https://arxiv.org/pdf/1602.02373.pdf, June 2019. Lin Z P, Shu F D, Ye Y, Hu C Y, Wang Q. An empirical study on bug assignment automation using Chinese bug data. In Proc. the 3rd International Symposium on Empirical Software Engineering & Measurement, October 2009, pp.451-455. Xi S Q, Yao Y, Xiao X S, Xu F, Lu J. An effective approach for routing the bug reports to the right fixers. In Proc. the 10th Asia-Pacific Symposium on Internetware, September 2018, Article No. 11. WuHRLiuHYMaYTEmpirical study on developer factors affecting tossing path length of bug reportsIET Software201812325827010.1049/iet-sen.2017.0159 Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. In Proc. the 30th International Conference on Machine Learning, June 2013, pp.1139-1147. CavalcantiYCAlmeidaESCunhaCEAThe bug report duplication problem: An exploratory studySoftware Quality Journal2013211396610.1007/s11219-011-9164-5 XiSQYaoYXuFLuJBug triaging approach based on recurrent neural networksJournal of Software20182982322233507028896(in Chinese) Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014. https://arxiv.org/pdf/1409.0473.pdf, June 2019. AleneziMMagelKBanitaanSEfficient bug triaging using text miningJournal of Software2013892185219010.4304/jsw.8.9.2185-2190 Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): Article No. 27. Yang Z C, Yang D Y, Dyer C, He X D, Smola A, Hovy E. Hierarchical attention networks for document classification. In Proc. the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2016, pp.1480-1489. Hoffman M, Bach F R, Blei D M. Online learning for latent Dirichlet allocation. In Proc. the 24th Annual Conference on Neural Information Processing Systems, December 2010, pp.856-864. Zanetti M S, Scholtes I, Tessone C J, Schweitzer F. Categorizing bugs with social networks: A case study on four open source software communities. In Proc. the 35th International Conference on Software Engineering, May 2013, pp.1032-1041. Cho K, van Merrienboer B, G¨ul¸cehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014. https://arxiv.org/pdf/1406.1078.pdf, June 2019. Runeson P, Alexandersson M, Nyholm O. Detection of duplicate defect reports using natural language processing. In Proc. the 29th International Conference on Software Engineering, May 2007, pp.499-510. Cubranic D, Murphy G C. Automatic bug triage using text categorization. In Proc. the 16th International Conference on Software Engineering & Knowledge Engineering, June 2004, pp.92-97. Fan Y R, Xia X, Lo D, Hassan A E. Chaff from the wheat: Characterizing and determining valid bug reports. IEEE Transactions on Software Engineering. doi:https://doi.org/10.1109/TSE.2018.2864217. Tian Y, Sun C, Lo D. Improved duplicate bug report identification. In Proc. the 16th European Conference on Software Maintenance and Reengineering, March 2012, pp.385-390. Naguib H, Narayan N, Br¨ugge B, Helal D. Bug report assignee recommendation using activity profiles. In Proc. the 10th Working Conference on Mining Software Repositories, May 2013, pp.22-30. ZouWQ, Hu Y, Xuan J F, Jiang H. Towards training set reduction for bug triage. In Proc. the 35th Annual Computer Software and Applications Conference, July 2011, pp.576-581. Hinton G E, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580, 2012. https://arxiv.org/pdf/1207.0580.pdf, June 2019. Bhattacharya P, Neamtiu I. Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging. In Proc. the 2010 IEEE International Conference on Software Maintenance, September 2010, Article No. 41. Mani S, Sankaran A, Aralikatte R. DeepTriage: Exploring the effectiveness of deep learning for bug triaging. arXiv:1801.01275, 2018. https://arxiv.org/pdf/1801.01275.pdf, June 2019. Hu H, Zhang H Y, Xuan J F, Sun W G. Effective bug triage based on historical bug-fix information. In Proc. the 25th International Symposium on Software Reliability Engineering, November 2014, pp.122-132. Herraiz I, Germán D M, González-Barahona J M, Robles G. Towards a simplification of the bug report form in Eclipse. In Proc. the 2008 International Working Conference on Mining Software Repositories, May 2008, pp.145-148. ZhangWWangSWangQKSAP: An approach to bug report assignment using KNN search and heterogeneous proximityInformation and Software Technology201670688410.1016/j.infsof.2015.10.004 Anvik J, Hiew L, Murphy G C. Who should fix this bug? In Proc. the 28th International Conference on Software Engineering, May 2006, pp.361-370. Tamrawi A, Nguyen T T, Al-Kofahi J M, Nguyen T N. Fuzzy set and cache-based approach for bug triaging. In Proc. the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, September 2011, pp.365-375. Pennington J, Socher R, Manning C. Glove: Global vectors for word representation. In Proc. the 2014 Conference on Empirical Methods in Natural Language Processing, October 2014, pp.1532-1543. Hooimeijer P, Weimer W. Modeling bug report quality. In Proc. the 22nd IEEE/ACM International Conference on Automated Software Engineering, November 2007, pp.34-43. Demeyer S, Lamkanfi A. Predicting reassignments of bug reports —An exploratory investigation. In Proc. the 17th European Conference on Software Maintenance and Reengineering, March 2013, pp.327-330. Zhang T, Yang G, Lee B, Lua E K. A novel developer ranking algorithm for automatic bug triage using topic model and developer relations. In Proc. the 21st Asia-Pacific Software Engineering Conference, December 2014, pp.223-230. Nguyen A T, Nguyen T T, Nguyen T N, Lo D, Sun C. Duplicate bug report detection with a combination of information retrieval and topic modeling. In Proc. the 27th IEEE/ACM International Conference on Automated Software Engineering, September 2012, pp.70-79. Wu L, Xie B Y, Kaiser G E, Passonneau R J. BUGMINER: Software reliability analysis via data mining of bug reports. In Proc the 23rd International Conference on Software Engineering & Knowledge Engineering, July 2011, pp.95-100. Lee S R, Heo M J, Lee C G, Kim M, Jeong G. Applying deep learning based automatic bug triager to industrial projects. In Proc. the 11th Joint Meeting on Foundations of Software Engineering, September 2017, pp.926-931. Bettenburg N, Premraj R, Zimmermann T, Kim S. Duplicate bug reports considered harmful ... really? In Proc. the 24th International Conference on Software Maintenance, September 2008, pp.337-345. Wang S, Zhang W, Yang Y, Wang Q. DevNet: Exploring developer collaboration in heterogeneous networks of bug repositories. In Proc. the 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, October 2013, pp.193-202. Cavalcanti Y C, de Almeida E S, da Cunha C E A, Lucrédio D, Meira S R. An initial study on the bug report duplication problem. In Proc. the 14th European Conference on Software Maintenance & Reengineering, March 2010, pp.264-267. Yang G, Zhang T, Lee B. Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports. In Proc. the 38th Annual Computer Software and Applications Conference, July 2014, pp.97-106. Wang X Y, Zhang L, Xie T, Anvik J, Sun J. An approach to detecting duplicate bug reports using natural language and execution information. In Proc. the 30th International Conference on Software Engineering, May 2008, pp.461-470. 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| References_xml | – reference: Bhattacharya P, Neamtiu I. Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging. In Proc. the 2010 IEEE International Conference on Software Maintenance, September 2010, Article No. 41. – reference: Lee S R, Heo M J, Lee C G, Kim M, Jeong G. Applying deep learning based automatic bug triager to industrial projects. In Proc. the 11th Joint Meeting on Foundations of Software Engineering, September 2017, pp.926-931. – reference: Mani S, Sankaran A, Aralikatte R. DeepTriage: Exploring the effectiveness of deep learning for bug triaging. arXiv:1801.01275, 2018. https://arxiv.org/pdf/1801.01275.pdf, June 2019. – reference: Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014. https://arxiv.org/pdf/1409.0473.pdf, June 2019. – reference: HintonGDengLYuDDeep neural networks for acoustic modeling in speech recognition: The shared views of four research groupsIEEE Signal Processing Magazine2012296829710.1109/MSP.2012.2205597 – reference: Cavalcanti Y C, de Almeida E S, da Cunha C E A, Lucrédio D, Meira S R. An initial study on the bug report duplication problem. In Proc. the 14th European Conference on Software Maintenance & Reengineering, March 2010, pp.264-267. – reference: Tian Y, Sun C, Lo D. Improved duplicate bug report identification. In Proc. the 16th European Conference on Software Maintenance and Reengineering, March 2012, pp.385-390. – reference: AleneziMMagelKBanitaanSEfficient bug triaging using text miningJournal of Software2013892185219010.4304/jsw.8.9.2185-2190 – reference: ZouWQ, Hu Y, Xuan J F, Jiang H. Towards training set reduction for bug triage. In Proc. the 35th Annual Computer Software and Applications Conference, July 2011, pp.576-581. – reference: WuHRLiuHYMaYTEmpirical study on developer factors affecting tossing path length of bug reportsIET Software201812325827010.1049/iet-sen.2017.0159 – reference: Sutskever I, Martens J, Dahl G, Hinton G. On the importance of initialization and momentum in deep learning. In Proc. the 30th International Conference on Machine Learning, June 2013, pp.1139-1147. – reference: Cho K, van Merrienboer B, G¨ul¸cehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014. https://arxiv.org/pdf/1406.1078.pdf, June 2019. – reference: Bettenburg N, Premraj R, Zimmermann T, Kim S. Duplicate bug reports considered harmful ... really? In Proc. the 24th International Conference on Software Maintenance, September 2008, pp.337-345. – reference: Tamrawi A, Nguyen T T, Al-Kofahi J M, Nguyen T N. Fuzzy set and cache-based approach for bug triaging. In Proc. the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, September 2011, pp.365-375. – reference: Lin Z P, Shu F D, Ye Y, Hu C Y, Wang Q. An empirical study on bug assignment automation using Chinese bug data. In Proc. the 3rd International Symposium on Empirical Software Engineering & Measurement, October 2009, pp.451-455. – reference: Hoffman M, Bach F R, Blei D M. Online learning for latent Dirichlet allocation. In Proc. the 24th Annual Conference on Neural Information Processing Systems, December 2010, pp.856-864. – reference: XiaXLoDDingYAl-KofahiJMNguyenTNWangXImproving automated bug triaging with specialized topic modelIEEE Transactions on Software Engineering201743327229710.1109/TSE.2016.2576454 – reference: Cubranic D, Murphy G C. Automatic bug triage using text categorization. In Proc. the 16th International Conference on Software Engineering & Knowledge Engineering, June 2004, pp.92-97. – reference: CavalcantiYCAlmeidaESCunhaCEAThe bug report duplication problem: An exploratory studySoftware Quality Journal2013211396610.1007/s11219-011-9164-5 – reference: Yang Z C, Yang D Y, Dyer C, He X D, Smola A, Hovy E. Hierarchical attention networks for document classification. In Proc. the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, June 2016, pp.1480-1489. – reference: Park J, Lee M W, Kim J, Hwang S, Kim S. CosTriage: A cost-aware triage algorithm for bug reporting systems. In Proc. the 25th AAAI Conference on Artificial Intelligence, August 2011, Article No. 22. – reference: Hiew L. Assisted detection of duplicate bug reports [Ph.D. Thesis]. University of British Columbia, 2006. – reference: Wang X Y, Zhang L, Xie T, Anvik J, Sun J. An approach to detecting duplicate bug reports using natural language and execution information. In Proc. the 30th International Conference on Software Engineering, May 2008, pp.461-470. – reference: Anvik J, Hiew L, Murphy G C. Who should fix this bug? In Proc. the 28th International Conference on Software Engineering, May 2006, pp.361-370. – reference: Naguib H, Narayan N, Br¨ugge B, Helal D. Bug report assignee recommendation using activity profiles. In Proc. the 10th Working Conference on Mining Software Repositories, May 2013, pp.22-30. – reference: Demeyer S, Lamkanfi A. Predicting reassignments of bug reports —An exploratory investigation. In Proc. the 17th European Conference on Software Maintenance and Reengineering, March 2013, pp.327-330. – reference: Wu L, Xie B Y, Kaiser G E, Passonneau R J. BUGMINER: Software reliability analysis via data mining of bug reports. In Proc the 23rd International Conference on Software Engineering & Knowledge Engineering, July 2011, pp.95-100. – reference: Zhang T, Yang G, Lee B, Lua E K. A novel developer ranking algorithm for automatic bug triage using topic model and developer relations. In Proc. the 21st Asia-Pacific Software Engineering Conference, December 2014, pp.223-230. – reference: Zhang X F, Yao Y, Wang Y J, Xu F, Lu J. Exploring metadata in bug reports for bug localization. In Proc. the 24th Asia-Pacific Software Engineering Conference, December 2017, pp.328-337. – reference: Runeson P, Alexandersson M, Nyholm O. Detection of duplicate defect reports using natural language processing. In Proc. the 29th International Conference on Software Engineering, May 2007, pp.499-510. – reference: Hu H, Zhang H Y, Xuan J F, Sun W G. Effective bug triage based on historical bug-fix information. In Proc. the 25th International Symposium on Software Reliability Engineering, November 2014, pp.122-132. – reference: Pennington J, Socher R, Manning C. Glove: Global vectors for word representation. In Proc. the 2014 Conference on Empirical Methods in Natural Language Processing, October 2014, pp.1532-1543. – reference: Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): Article No. 27. – reference: Zanetti M S, Scholtes I, Tessone C J, Schweitzer F. Categorizing bugs with social networks: A case study on four open source software communities. In Proc. the 35th International Conference on Software Engineering, May 2013, pp.1032-1041. – reference: Fan Y R, Xia X, Lo D, Hassan A E. Chaff from the wheat: Characterizing and determining valid bug reports. IEEE Transactions on Software Engineering. doi:https://doi.org/10.1109/TSE.2018.2864217. – reference: Bertram D, Voida A, Greenberg S, Walker R. Communication, collaboration, and bugs: The social nature of issue tracking in small, collocated teams. In Proc. the 2010 ACM Conference on Computer Supported Cooperative Work, February 2010, pp.291-300. – reference: Hinton G E, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580, 2012. https://arxiv.org/pdf/1207.0580.pdf, June 2019. – reference: Nguyen A T, Nguyen T T, Nguyen T N, Lo D, Sun C. Duplicate bug report detection with a combination of information retrieval and topic modeling. In Proc. the 27th IEEE/ACM International Conference on Automated Software Engineering, September 2012, pp.70-79. – reference: Xi S Q, Yao Y, Xiao X S, Xu F, Lu J. An effective approach for routing the bug reports to the right fixers. In Proc. the 10th Asia-Pacific Symposium on Internetware, September 2018, Article No. 11. – reference: Wang S, Zhang W, Yang Y, Wang Q. DevNet: Exploring developer collaboration in heterogeneous networks of bug repositories. In Proc. the 2013 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, October 2013, pp.193-202. – reference: Hooimeijer P, Weimer W. Modeling bug report quality. In Proc. the 22nd IEEE/ACM International Conference on Automated Software Engineering, November 2007, pp.34-43. – reference: XiSQYaoYXuFLuJBug triaging approach based on recurrent neural networksJournal of Software20182982322233507028896(in Chinese) – reference: Johnson R, Zhang T. Supervised and semi-supervised text categorization using LSTM for region embeddings. arXiv:1602.02373, 2016. https://arxiv.org/pdf/1602.02373.pdf, June 2019. – reference: Herraiz I, Germán D M, González-Barahona J M, Robles G. Towards a simplification of the bug report form in Eclipse. In Proc. the 2008 International Working Conference on Mining Software Repositories, May 2008, pp.145-148. – reference: Jeong G, Kim S, Zimmermann T. Improving bug triage with bug tossing graphs. In Proc. the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on The Foundations of Software Engineering, August 2009, pp.111-120. – reference: Yang G, Zhang T, Lee B. Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports. In Proc. the 38th Annual Computer Software and Applications Conference, July 2014, pp.97-106. – reference: ZhangWWangSWangQKSAP: An approach to bug report assignment using KNN search and heterogeneous proximityInformation and Software Technology201670688410.1016/j.infsof.2015.10.004 – volume: 8 start-page: 2185 issue: 9 year: 2013 ident: 1953_CR27 publication-title: Journal of Software doi: 10.4304/jsw.8.9.2185-2190 – volume: 70 start-page: 68 year: 2016 ident: 1953_CR31 publication-title: Information and Software Technology doi: 10.1016/j.infsof.2015.10.004 – volume: 12 start-page: 258 issue: 3 year: 2018 ident: 1953_CR33 publication-title: IET Software doi: 10.1049/iet-sen.2017.0159 – ident: 1953_CR9 – ident: 1953_CR30 doi: 10.1109/ISSRE.2014.17 – ident: 1953_CR40 – ident: 1953_CR26 – ident: 1953_CR42 doi: 10.1145/1368088.1368151 – ident: 1953_CR32 doi: 10.1109/ESEM.2013.24 – ident: 1953_CR11 doi: 10.1109/APSEC.2017.39 – ident: 1953_CR45 doi: 10.1109/ICSM.2008.4658082 – ident: 1953_CR10 doi: 10.1145/3275219.3275228 – ident: 1953_CR23 – ident: 1953_CR35 doi: 10.1109/CSMR.2013.42 – volume: 29 start-page: 82 issue: 6 year: 2012 ident: 1953_CR13 publication-title: IEEE Signal Processing Magazine doi: 10.1109/MSP.2012.2205597 – ident: 1953_CR1 doi: 10.1145/1718918.1718972 – ident: 1953_CR3 doi: 10.1145/1134285.1134336 – ident: 1953_CR29 doi: 10.1609/aaai.v25i1.7839 – ident: 1953_CR28 doi: 10.1109/COMPSAC.2011.80 – ident: 1953_CR2 doi: 10.1145/1595696.1595715 – ident: 1953_CR36 doi: 10.1145/1370750.1370786 – ident: 1953_CR38 doi: 10.1109/ICSE.2013.6606653 – ident: 1953_CR7 doi: 10.1109/APSEC.2014.43 – ident: 1953_CR18 doi: 10.1145/2025113.2025163 – ident: 1953_CR44 doi: 10.1109/CSMR.2012.48 – ident: 1953_CR25 doi: 10.1109/MSR.2013.6623999 – ident: 1953_CR15 doi: 10.18653/v1/N16-1174 – volume: 21 start-page: 39 issue: 1 year: 2013 ident: 1953_CR47 publication-title: Software Quality Journal doi: 10.1007/s11219-011-9164-5 – volume: 29 start-page: 2322 issue: 8 year: 2018 ident: 1953_CR17 publication-title: Journal of Software – volume: 43 start-page: 272 issue: 3 year: 2017 ident: 1953_CR6 publication-title: IEEE Transactions on Software Engineering doi: 10.1109/TSE.2016.2576454 – ident: 1953_CR37 – ident: 1953_CR46 doi: 10.1109/CSMR.2010.52 – ident: 1953_CR39 doi: 10.1109/TSE.2018.2864217 – ident: 1953_CR20 – ident: 1953_CR21 doi: 10.3115/v1/D14-1162 – ident: 1953_CR22 – ident: 1953_CR43 doi: 10.1145/2351676.2351687 – ident: 1953_CR19 doi: 10.1145/1961189.1961199 – ident: 1953_CR41 doi: 10.1109/ICSE.2007.32 – ident: 1953_CR24 doi: 10.1109/ESEM.2009.5315994 – ident: 1953_CR4 doi: 10.1109/ICSM.2010.5609736 – ident: 1953_CR16 – ident: 1953_CR8 doi: 10.1145/3106237.3117776 – ident: 1953_CR14 – ident: 1953_CR5 doi: 10.1109/COMPSAC.2014.16 – ident: 1953_CR12 – ident: 1953_CR34 doi: 10.1145/1321631.1321639 |
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| Title | Bug Triaging Based on Tossing Sequence Modeling |
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