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Towards the identification of bug entities and relations in bug reports

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Abstract

During the bug fixing process, developers usually analyze the historical relevant bug reports in bug repository to support various bug analysis and fixing activities. There are rich semantics and relationships in the bug reports, which can be helpful for bug retrieval, recommendation, and repair. In this paper, our purpose is to quickly extract effective knowledge of bug report from two perspectives: entity recognition and relation extraction to assist bug understanding and fixing. Meanwhile, we hope to strengthen the relevance of bug reports through the effective extraction of bug knowledge. In order to effectively extract the bug entities and relations in the bug report, we first define 8 types of relations between the bug entities and incorporate neural network Recurrent Neural Network (RNN) and RNN based on shortest dependency path (SDP-RNN) to automatically identify bug entities and their relations in bug reports. Results We evaluate the effectiveness of our method through four experimental questions. From the results, the bug knowledge extracted by our method can effectively represent the semantics and relations in the bug report, and obtain F1 scores of 79.32% and 63.8% in entity recognition and relation extraction, respectively. The proposed approach can efficiently extract the structured bug knowledge in the bug report, and further enhance the correlation between the bug reports and the effectiveness of the bug knowledge through the representation of these structured bug knowledge units.

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Notes

  1. https://www.bugzilla.org/.

  2. https://bugzilla.mozilla.org/show_bug.cgi?id=1500000.

  3. https://bugzilla.mozilla.org/show_bug.cgi?id=920702.

  4. https://bugzilla.mozilla.org/show_bug.cgi?id=1117438.

  5. https://bugzilla.mozilla.org/show_bug.cgi?id=1368216.

  6. https://bugzilla.mozilla.org/.

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

  8. http://nlp.stanford.edu/software/lex-parser.shtm.

  9. https://code.google.com/archive/p/word2vec/.

References

  • Alenezi, M., Banitaan, S., Zarour, M.: Using categorical features in mining bug tracking systems to assign bug reports. CoRR, arXiv:abs/1804.07803 (2018)

  • Ardimento, P., Dinapoli, A.: Knowledge extraction from on-line open source bug tracking systems to predict bug-fixing time. In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics, WIMS 2017, Amantea, Italy, 19–22 June 2017, pp. 7:1–7:9 (2017)

  • Bagheri, E., Ensan, F.: Semantic tagging and linking of software engineering social content. Autom. Softw. Eng. 23, 147–190 (2016)

    Article  Google Scholar 

  • Bettenburg, N., Premraj, R., Zimmermann, T., Kim, S.: Duplicate bug reports considered harmful … really? In: 24th IEEE International Conference on Software Maintenance (ICSM 2008), 28 Sept–4 Oct 2008, Beijing, China, pp. 337–345 (2008)

  • Bunescu, R.C., Mooney, R.J.: A shortest path dependency kernel for relation extraction. In: HLT/EMNLP 2005, Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, 6–8 Oct 2005, Vancouver, BC, Canada, pp. 724–731 (2005)

  • Cantrell, S.A.: The emergent algebraic structure of RNNS and embeddings in NLP. CoRR, arXiv:abs/1803.02839 (2018)

  • Capobianco, G., Lucia, A.D., Oliveto, R., Panichella, A., Panichella, S.: Improving IR-based traceability recovery via noun-based indexing of software artifacts. J. Softw.: Evol. Process 25, 743–762 (2013)

    Google Scholar 

  • Chen, D., Li, B., Zhou, C., Zhu, X.: Automatically identifying bug entities and relations for bug analysis. In: 2019 IEEE 1st International Workshop on Intelligent Bug Fixing (IBF), pp. 39–43 (2019). https://doi.org/10.1109/IBF.2019.8665494

  • Chen, M.: MinimalRNN: toward more interpretable and trainable recurrent neural networks. CoRR, arXiv:abs/1711.06788 (2017)

  • Cho, K., van Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. In: Proceedings of SSST@EMNLP 2014, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar, 25 Oct 2014, pp. 103–111 (2014)

  • Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20, 37–46 (1960)

    Article  Google Scholar 

  • Dietz, L., Kotov, A., Meij, E.: Utilizing knowledge graphs for text-centric information retrieval. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, 08–12 July 2018, pp. 1387–1390 (2018)

  • dos Santos, C.N., Xiang, B., Zhou, B.: Classifying relations by ranking with convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, 26–31 July 2015, Beijing, China, Volume 1: Long Papers, pp. 626–634 (2015)

  • Duchi, J.C., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)

    MathSciNet  MATH  Google Scholar 

  • Falleri, J., Huchard, M., Lafourcade, M., Nebut, C., Prince, V., Dao, M.: Automatic extraction of a wordnet-like identifier network from software. In: The 18th IEEE International Conference on Program Comprehension, ICPC 2010, Braga, Minho, Portugal, 30 June–2 July 2010, pp. 4–13 (2010)

  • Fu, T., Li, P., Ma, W.: Graphrel: Modeling text as relational graphs for joint entity and relation extraction. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28 July–2 Aug 2019, Volume 1: Long Papers, Association for Computational Linguistics. pp. 1409–1418 (2019)

  • Gao, H., Qin, X., Barroso, R.J.D., Hussain, W., Xu, Y., Yin, Y.: Collaborative learning-based industrial IoT API recommendation for software-defined devices: the implicit knowledge discovery perspective. IEEE Trans. Emerg. Top. Comput. Intell. 6, 66–76 (2020)

    Article  Google Scholar 

  • Gao, H., Xu, K., Cao, M., Xiao, J., Xu, Q., Yin, Y.: The deep features and attention mechanism based method to dish healthcare under social IoT systems: an empirical study with a hand-deep local-global net. IEEE Trans. Emerg. Top. Comput. Intell. (2021). https://doi.org/10.1109/TCSS.2021.3102591

    Article  Google Scholar 

  • Graves, A., Mohamed, A., Hinton, G.E.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2013, Vancouver, BC, Canada, 26–31 May 2013, pp. 6645–6649 (2013)

  • Haidar, M.A., Kurimo, M.: LDA-based context dependent recurrent neural network language model using document-based topic distribution of words. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, 5–9 March 2017, pp. 5730–5734 (2017)

  • Han, Z., Li, X., Liu, H., Xing, Z., Feng, Z.: Deepweak: reasoning common software weaknesses via knowledge graph embedding. In: 25th International Conference on Software Analysis, Evolution and Reengineering, SANER 2018, Campobasso, Italy, 20–23 March 2018, pp. 456–466 (2018)

  • Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  • Jiang, L., Liu, H., Jiang, H., Zhang, L., Mei, H.: Heuristic and neural network based prediction of project-specific API member access. IEEE Trans. Softw. Eng. (2020). https://doi.org/10.1109/TSE.2020.3017794

    Article  Google Scholar 

  • Jimeno-Yepes, A.: Confidence penalty, annealing gaussian noise and zoneout for biLSTM-CRF networks for named entity recognition (2018). CoRR, arXiv:abs/1808.04029

  • Kambhatla, N.: Combining lexical, syntactic, and semantic features with maximum entropy models for information extraction. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, 21–26 July 2004—Poster and Demonstration (2004)

  • Kang, N., Singh, B., Bui, Q., Afzal, Z., van Mulligen, E.M., Kors, J.A.: Knowledge-based extraction of adverse drug events from biomedical text. BMC Bioinform. 15, 64 (2014)

    Article  Google Scholar 

  • Katiyar, A., Cardie, C.: Going out on a limb: joint extraction of entity mentions and relations without dependency trees. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July–4 August, Volume 1: Long Papers, pp. 917–928 (2017)

  • Khatiwada, S., Tushev, M., Mahmoud, A.: Just enough semantics: an information theoretic approach for IR-based software bug localization. Inf. Softw. Technol. 93, 45–57 (2018)

    Article  Google Scholar 

  • Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. In: NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego California, USA, 12–17 June 2016, pp. 260–270 (2016)

  • Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)

    MATH  Google Scholar 

  • Le, X.D., Le, Q.L., Lo, D., Le Goues, C.: Enhancing automated program repair with deductive verification. In: 2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016, Raleigh, NC, USA, 2–7 Oct 2016, pp. 428–432 (2016a)

  • Le, X.D., Lo, D., Le Goues, C.: Empirical study on synthesis engines for semantics-based program repair. In: 2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016, Raleigh, NC, USA, 2–7 Oct 2016, pp. 423–427 (2016b)

  • Le, X.D., Lo, D., Le Goues, C.: History driven program repair. In: IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016, Suita, Osaka, Japan, 14–18 March 2016, Vol. 1, pp. 213–224 (2016c)

  • Le, T.B., Thung, F., Lo, D.: Will this localization tool be effective for this bug? Mitigating the impact of unreliability of information retrieval based bug localization tools. Empir. Softw. Eng. 22, 2237–2279 (2017)

    Article  Google Scholar 

  • Li, Q., Ji, H.: Incremental joint extraction of entity mentions and relations. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, 22–27 June 2014, Baltimore, MD, USA, Volume 1: Long Papers, pp. 402–412 (2014)

  • Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19–24 June, 2011, Portland, Oregon, USA, pp. 359–367 (2011)

  • Liu, C., Yang, J., Tan, L., Hafiz, M.: R2fix: automatically generating bug fixes from bug reports. In: 2013 IEEE Sixth International Conference on Software Testing, Verification and Validation, pp. 282–291 (2013)

  • Liu, H., Shen, M., Zhu, J., Niu, N., Li, G., Zhang, L.: Deep learning based program generation from requirements text: are we there yet? IEEE Trans. Softw. Eng. (2020). https://doi.org/10.1109/TSE.2020.3018481

    Article  Google Scholar 

  • Lu, J., Sun, X., Li, B., Bo, L., Zhang, T.: BEAT: considering question types for bug question answering via templates. Knowl. Based Syst. 225, 107098 (2021)

    Article  Google Scholar 

  • Mahfoodh, H., Obediat, Q.: Software risk estimation through bug reports analysis and bug-fix time predictions. In: 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT), pp. 1–6 (2020)

  • Malik, H.H., Bhardwaj, V.S., Fiorletta, H.: Accurate information extraction for quantitative financial events. In: Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, 24–28 Oct 2011, pp. 2497–2500 (2011)

  • Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, 22–27 June 2014, Baltimore, MD, USA, System Demonstrations, pp. 55–60 (2014)

  • Marcus, A., Maletic, J.I.: Recovering documentation-to-source-code traceability links using latent semantic indexing. In: Proceedings of the 25th International Conference on Software Engineering, 3–10 May 2003, Portland, Oregon, USA, pp. 125–137 (2003)

  • Miwa, M., Bansal, M.: End-to-end relation extraction using LSTMs on sequences and tree structures. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 Aug 2016, Berlin, Germany, Volume 1: Long Papers (2016)

  • Nagano, S., Ichikawa, Y., Kobayashi, T.: Recovering traceability links between code and documentation for enterprise project artifacts. In: 36th Annual IEEE Computer Software and Applications Conference, COMPSAC 2012, Izmir, Turkey, 16–20 July 2012, pp. 11–18 (2012)

  • Nguyen, T.D., Mai, K., Pham, T., Nguyen, M.T., Nguyen, T.T., Eguchi, T., Sasano, R., Sekine, S.: Extended named entity recognition API and its applications in language education. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July–4 August, System Demonstrations, pp. 37–42 (2017)

  • Nguyen, D.P.T., Matsuo, Y., Ishizuka, M.: Relation extraction from Wikipedia using subtree mining. In: Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 22–26 July 2007, Vancouver, British Columbia, Canada, pp. 1414–1420 (2007)

  • Nguyen, D.B., Theobald, M., Weikum, G.: J-NERD: joint named entity recognition and disambiguation with rich linguistic features. TACL 4, 215–229 (2016)

    Article  Google Scholar 

  • Ni, Z., Li, B., Sun, X., Chen, T., Tang, B., Shi, X.: Analyzing bug fix for automatic bug cause classification. J. Syst. Softw. 163, 110538 (2020)

    Article  Google Scholar 

  • Passos, A., Kumar, V., McCallum, A.: Lexicon infused phrase embeddings for named entity resolution. In: Proceedings of the Eighteenth Conference on Computational Natural Language Learning, CoNLL 2014, Baltimore, Maryland, USA, 26–27 June 2014, pp. 78–86 (2014)

  • Qi, C., Song, Q., Zhang, P., Yuan, H.: Cn-MAKG: China meteorology and agriculture knowledge graph construction based on semi-structured data. In: 17th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2018, Singapore, Singapore, 6–8 June 2018, pp. 692–696 (2018)

  • Ren, X., Wu, Z., He, W., Qu, M., Voss, C.R., Ji, H., Abdelzaher, T.F., Han, J.: Cotype: Joint extraction of typed entities and relations with knowledge bases. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017, Perth, Australia, 3–7 April 2017, pp. 1015–1024 (2017)

  • Ren, X., Ye, X., Xing, Z., Xia, X., Xu, X., Zhu, L., Sun, J.: API-misuse detection driven by fine-grained API-constraint knowledge graph. In: 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 461–472 (2020)

  • Rink, B., Harabagiu, S.M.: UTD: classifying semantic relations by combining lexical and semantic resources. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval@ACL 2010, Uppsala University, Uppsala, Sweden, 15–16 July 2010, pp. 256–259 (2010)

  • Sang, E.F., De Meulder, F.: Introduction to the CONLL-2002 shared task: language-independent named entity recognition. In: Proceedings of the 6th Conference on Natural Language Learning, CoNLL 2002, Held in cooperation with COLING 2002, Taipei, Taiwan, 2002 (2002)

  • Shaalan, K.: A survey of Arabic named entity recognition and classification. Comput. Linguist. 40, 469–510 (2014)

    Article  Google Scholar 

  • Sharma, A., Tian, Y., Lo, D.: NIRMAL: automatic identification of software relevant tweets leveraging language model. In: 22nd IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2015, Montreal, QC, Canada, 2–6 March 2015, pp. 449–458 (2015)

  • Shen, W., Wang, J., Luo, P., Wang, M.: LINDEN: linking named entities with knowledge base via semantic knowledge. In: Proceedings of the 21st World Wide Web Conference 2012, WWW 2012, Lyon, France, 16–20 April 2012, pp. 449–458 (2012)

  • Shen, J., Sun, X., Li, B., Yang, H., Hu, J.: On automatic summarization of what and why information in source code changes. In: 40th IEEE Annual Computer Software and Applications Conference, COMPSAC 2016, Atlanta, GA, USA, 10–14 June 2016, pp. 103–112 (2016)

  • 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 ’13, San Francisco, CA, USA, 18–19 May 2013, pp. 2–11 (2013)

  • Socher, R., Huval, B., Manning, C.D., Ng, A.Y.: Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, 12–14 July 2012, Jeju Island, Korea, pp. 1201–1211 (2012)

  • Sorbo, A.D., Panichella, S., Visaggio, C.A., Penta, M.D., Canfora, G., Gall, H.C.: Development emails content analyzer: intention mining in developer discussions (T). In: 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015, Lincoln, NE, USA, 9–13 Nov 2015, pp. 12–23 (2015)

  • Sun, A., Grishman, R., Sekine, S.: Semi-supervised relation extraction with large-scale word clustering. In: The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference, 19–24 June 2011, Portland, Oregon, USA, pp. 521–529 (2011)

  • Sun, X., Yang, H., Xia, X., Li, B.: Enhancing developer recommendation with supplementary information via mining historical commits. J. Syst. Softw. 134, 355–368 (2017a)

    Article  Google Scholar 

  • Sun, X., Zhou, T., Li, G., Hu, J., Yang, H., Li, B.: An empirical study on real bugs for machine learning programs. In: 24th Asia-Pacific Software Engineering Conference, APSEC 2017, Nanjing, China, 4–8 Dec 2017, pp. 348–357 (2017b)

  • Sun, X., Peng, X., Zhang, K., Liu, Y., Cai, Y.: How security bugs are fixed and what can be improved: an empirical study with Mozilla. Sci. China Inf. Sci. 62, 19102 (2018a). https://doi.org/10.1007/s11432-017-9459-5

    Article  Google Scholar 

  • Sun, X., Yang, H., Leung, H., Li, B., Li, H.J., Liao, L.: Effectiveness of exploring historical commits for developer recommendation: an empirical study. Front. Comput. Sci. 12, 528–544 (2018b)

    Article  Google Scholar 

  • Sun, X., Zhou, T., Wang, R., Duan, Y., Bo, L., Chang, J.: Experience report: investigating bug fixes in machine learning frameworks/libraries. Front. Comput. Sci. 15, 156212 (2021)

    Article  Google Scholar 

  • Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 8–13 Dec 2014, Montreal, QC, Canada, pp. 3104–3112 (2014)

  • Tian, Y., Lo, D.: A comparative study on the effectiveness of part-of-speech tagging techniques on bug reports. In: 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER), pp. 570–574 (2015). https://doi.org/10.1109/SANER.2015.7081879

  • von Krogh, G., Spaeth, S., Haefliger, S.: Knowledge reuse in open source software: an exploratory study of 15 open source projects. In: Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pp. 198b–198b (2005). https://doi.org/10.1109/HICSS.2005.378

  • Wang, L., Cao, Z., de Melo, G., Liu, Z.: Relation classification via multi-level attention CNNS. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 Aug 2016, Berlin, Germany, Volume 1: Long Papers (2016)

  • Wang, L., Sun, X., Wang, J., Duan, Y., Li, B.: Construct bug knowledge graph for bug resolution: poster. In: Proceedings of the 39th International Conference on Software Engineering, ICSE 2017, Buenos Aires, Argentina, 20–28 May 2017—Companion Volume, pp. 189–191 (2017)

  • Xu, Y., Mou, L., Li, G., Chen, Y., Peng, H., Jin, Z.: Classifying relations via long short term memory networks along shortest dependency paths. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17–21 Sept 2015, pp. 1785–1794 (2015)

  • Yao, L., Haghighi, A., Riedel, S., McCallum, A.: Structured relation discovery using generative models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, EMNLP 2011, 27–31 July 2011, John McIntyre Conference Centre, Edinburgh, UK, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1456–1466 (2011)

  • Ye, D., Xing, Z., Foo, C.Y., Ang, Z.Q., Li, J., Kapre, N.: Software-specific named entity recognition in software engineering social content. In: IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016, Suita, Osaka, Japan, 14–18 March 2016, Vol. 1, pp. 90–101 (2016)

  • Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: COLING 2014, 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, 23–29 Aug 2014, Dublin, Ireland, pp. 2335–2344 (2014)

  • Zhao, X., Xing, Z., Kabir, M.A., Sawada, N., Li, J., Lin, S.: HDSKG: harvesting domain specific knowledge graph from content of webpages. In: IEEE 24th International Conference on Software Analysis, Evolution and Reengineering, SANER 2017, Klagenfurt, Austria, 20–24 Feb 2017, pp. 56–67 (2017)

  • Zheng, S., Hao, Y., Lu, D., Bao, H., Xu, J., Hao, H., Xu, B.: Joint entity and relation extraction based on a hybrid neural network. Neurocomputing 257, 59–66 (2017a)

    Article  Google Scholar 

  • Zheng, S., Wang, F., Bao, H., Hao, Y., Zhou, P., Xu, B.: Joint extraction of entities and relations based on a novel tagging scheme. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July–4 Aug, Volume 1: Long Papers, pp. 1227–1236 (2017b)

  • Zhou, G., Su, J., Zhang, J., Zhang, M.: Exploring various knowledge in relation extraction. In: ACL 2005, 43rd Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, 25–30 June 2005, University of Michigan, USA, pp. 427–434 (2005)

  • Zhou, C., Li, B., Sun, X., Guo, H.: Recognizing software bug-specific named entity in software bug repository. In: Proceedings of the 26th Conference on Program Comprehension, ICPC 2018, Gothenburg, Sweden, 27–28 May 2018, pp. 108–119 (2018)

  • Zhou, C., Li, B., Sun, X.: Improving software bug-specific named entity recognition with deep neural network. J. Syst. Softw. 165, 110572 (2020)

    Article  Google Scholar 

  • Zhou, C., Li, B., Sun, X., Bo, L.: Why and what happened? Aiding bug comprehension with automated category and causal link identification. Empir. Softw. Eng. 26, 1–36 (2021)

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 61972335, 61872312, 62002309); the Six Talent Peaks Project in Jiangsu Province (No. RJFW-053), the Jiangsu “333” Project, the Open Funds of State Key Laboratory for Novel Software Technology of Nanjing University (Nos. KFKT2020B15, KFKT2020B16), the Yangzhou city-Yangzhou University Science and Technology Cooperation Fund Project (YZ2021157), the Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology (No. NJ2020022), the Natural Science Research Project of Universities in Jiangsu Province (No. 20KJB520024), Yangzhou University Interdisciplinary Research Foundation for Animal Husbandry Discipline of Targeted Support (yzuxk202015) and Yangzhou University Top-level Talents Support Program (2019).

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  1. School of Information Engineering, Yangzhou University, Yangzhou, China

    Bin Li, Ying Wei, Xiaobing Sun, Lili Bo & Dingshan Chen

  2. College of Computer Science and Technology/College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Chuanqi Tao

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Li, B., Wei, Y., Sun, X. et al. Towards the identification of bug entities and relations in bug reports. Autom Softw Eng 29, 24 (2022). https://doi.org/10.1007/s10515-022-00325-1

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