Leveraging GPT-like LLMs to Automate Issue Labeling | Proceedings of the 21st International Conference on Mining Software Repositories (2024)

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Authors: Giuseppe Colavito, Filippo Lanubile, Nicole Novielli, and Luigi Quaranta

MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories

April 2024

Pages 469 - 480

Published: 02 July 2024 Publication History

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    Abstract

    Issue labeling is a crucial task for the effective management of software projects. To date, several approaches have been put forth for the automatic assignment of labels to issue reports. In particular, supervised approaches based on the fine-tuning of BERT-like language models have been proposed, achieving state-of-the-art performance. More recently, decoder-only models such as GPT have become prominent in SE research due to their surprising capabilities to achieve state-of-the-art performance even for tasks they have not been trained for. To the best of our knowledge, GPT-like models have not been applied yet to the problem of issue classification, despite the promising results achieved for many other software engineering tasks. In this paper, we investigate to what extent we can leverage GPT-like LLMs to automate the issue labeling task. Our results demonstrate the ability of GPT-like models to correctly classify issue reports in the absence of labeled data that would be required to fine-tune BERT-like LLMs.

    References

    [1]

    [n. d.]. OpenAI API Documentation, How should I set the temperature parameter? https://platform.openai.com/docs/guides/text-generation/how-should-i-set-the-temperature-parameter. Accessed: 2023-11-17.

    [2]

    W. Alhindi, A. Aleid, I. Jenhani, and M. Mkaouer. 2023. Issue-Labeler: an ALBERT-based Jira Plugin for Issue Classification. In 2023 IEEE/ACM 10th International Conference on Mobile Software Engineering and Systems (MOBILESoft). IEEE Computer Society, Los Alamitos, CA, USA, 40--43.

    [3]

    Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, Alessandro Cappelli, Ruxandra Cojocaru, Merouane Debbah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B: an open large language model with state-of-the-art performance. (2023).

    [4]

    Giuliano Antoniol, Kamel Ayari, Massimiliano Di Penta, Foutse Khomh, and Yann-Gaël Guéhéneuc. 2008. Is it a bug or an enhancement? a text-based approach to classify change requests. In Proceedings of the 2008 Conf. of the center for advanced studies on collaborative research: meeting of minds. ACM, New York, NY, USA.

    Digital Library

    [5]

    Tegawendé F. Bissyandé, David Lo, Lingxiao Jiang, Laurent Réveillère, Jacques Klein, and Yves Le Traon. 2013. Got issues? Who cares about it? A large scale investigation of issue trackers from GitHub. In 2013 IEEE 24th Int'l Symposium on Software Reliability Engineering (ISSRE).

    [6]

    Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics 5 (2017), 135--146.

    [7]

    Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models Are Few-Shot Learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS'20). Curran Associates Inc., Red Hook, NY, USA, Article 159, 25 pages.

    Digital Library

    [8]

    Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, and Yi Zhang. 2023. Sparks of Artificial General Intelligence: Early experiments with GPT-4. arXiv:2303.12712 [cs.CL]

    [9]

    Jialun Cao, Meiziniu Li, Ming Wen, and Shing chi Cheung. 2023. A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair. arXiv:2304.08191 [cs.SE]

    [10]

    Yiannis Charalambous, Norbert Tihanyi, Ridhi Jain, Youcheng Sun, Mohamed Amine Ferrag, and Lucas C. Cordeiro. 2023. A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification. arXiv:2305.14752 [cs.SE]

    [11]

    Lingjiao Chen, Matei Zaharia, and James Zou. 2023. How is ChatGPT's behavior changing over time? arXiv:2307.09009 [cs.CL]

    [12]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021. Evaluating Large Language Models Trained on Code. arXiv:2107.03374 [cs.LG]

    [13]

    Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. https://lmsys.org/blog/2023-03-30-vicuna/

    [14]

    Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022. PaLM: Scaling Language Modeling with Pathways. arXiv:2204.02311 [cs.CL]

    [15]

    Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. Deep reinforcement learning from human preferences. Advances in neural information processing systems 30 (2017).

    [16]

    Jacob Cohen. 1968. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin 70, 4 (1968), 213.

    [17]

    Giuseppe Colavito, Filippo Lanubile, and Nicole Novielli. 2023. Few-Shot Learning for Issue Report Classification. In 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE). 16--19.

    [18]

    Giuseppe Colavito, Filippo Lanubile, and Nicole Novielli. 2023. Issue Report Classification Using Pre-Trained Language Models. In Proceedings of the 1st International Workshop on Natural Language-Based Software Engineering (Pittsburgh, Pennsylvania) (NLBSE '22). Association for Computing Machinery, New York, NY, USA, 29--32.

    Digital Library

    [19]

    Javier Luis Cánovas Izquierdo, Valerio Cosentino, Belén Rolandi, Alexandre Bergel, and Jordi Cabot. 2015. GiLA: GitHub label analyzer. In 2015 IEEE 22nd Int'l Conf.on Software Analysis, Evolution, and Reengineering (SANER).

    [20]

    Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Yihan Cao, Zihao Wu, Lin Zhao, Shaochen Xu, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, Hongmin Cai, Lichao Sun, Quanzheng Li, Dinggang Shen, Tianming Liu, and Xiang Li. 2023. AugGPT: Leveraging ChatGPT for Text Data Augmentation. arXiv:2302.13007 [cs.CL]

    [21]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, Minneapolis, Minnesota, 4171--4186.

    [22]

    Shihan Dou, Junjie Shan, Haoxiang Jia, Wenhao Deng, Zhiheng Xi, Wei He, Yueming Wu, Tao Gui, Yang Liu, and Xuanjing Huang. 2023. Towards Understanding the Capability of Large Language Models on Code Clone Detection: A Survey. arXiv:2308.01191 [cs.SE]

    [23]

    Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M. Zhang. 2023. Large Language Models for Software Engineering: Survey and Open Problems. arXiv:2310.03533 [cs.SE]

    [24]

    Qiang Fan, Yue Yu, Gang Yin, Tao Wang, and Huaimin Wang. 2017. Where Is the Road for Issue Reports Classification Based on Text Mining?. In 2017 ACM/IEEE Int'l Symposium on Empirical Software Engineering and Measurement (ESEM).

    Digital Library

    [25]

    Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020. Association for Computational Linguistics, Online, 1536--1547.

    [26]

    GitHub. 2021. GitHub Copilot. https://github.com/features/copilot

    [27]

    GitHub. 2023. GitHub Copilot now has a better AI model and new capabilities. https://github.blog/2023-02-14-github-copilot-now-has-a-better-ai-model-and-new-capabilities/

    [28]

    Google. 2023. Bard. https://bard.google.com/chat

    [29]

    Haibo He and Edwardo A. Garcia. 2009. Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering 21, 9 (2009), 1263--1284.

    Digital Library

    [30]

    Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021. DeBERTa: Decoding-enhanced BERT with Disentangled Attention. arXiv:2006.03654 [cs.CL]

    [31]

    Xingwei He, Zhenghao Lin, Yeyun Gong, A-Long Jin, Hang Zhang, Chen Lin, Jian Jiao, Siu Ming Yiu, Nan Duan, and Weizhu Chen. 2023. AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators. arXiv:2303.16854 [cs.CL]

    [32]

    Kim Herzig, Sascha Just, and Andreas Zeller. 2013. It's not a bug, it's a feature: How misclassification impacts bug prediction. In 2013 35th International Conference on Software Engineering (ICSE). 392--401.

    [33]

    Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural Computation 9, 8 (11 1997), 1735--1780.

    Digital Library

    [34]

    Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2023. Large Language Models for Software Engineering: A Systematic Literature Review. arXiv:2308.10620 [cs.SE]

    [35]

    Maliheh Izadi. 2022. CatIss: An Intelligent Tool for Categorizing Issues Reports using Transformers. In (NLBSE 2022).

    Digital Library

    [36]

    Maliheh Izadi, Kiana Akbari, and Abbas Heydarnoori. 2022. Predicting the objective and priority of issue reports in software repositories. Empirical Software Engineering 2 (2022).

    Digital Library

    [37]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023. Mistral 7B. arXiv:2310.06825 [cs.CL]

    [38]

    Rafael Kallis, Oscar Chaparro, Andrea Di Sorbo, and Sebastiano Panichella. 2022. NLBSE'22 Tool Competition. In Proceedings of The 1st Intl. Workshop on Natural Language-based Software Engineering (NLBSE'22).

    Digital Library

    [39]

    Rafael Kallis, Andrea Di Sorbo, Gerardo Canfora, and Sebastiano Panichella. 2019. Ticket Tagger: Machine Learning Driven Issue Classification. In 2019 IEEE Intl. Conf. on Software Maintenance and Evolution, ICSME 2019, Cleveland, OH, USA, September 29 - October 4, 2019. IEEE.

    [40]

    Rafael Kallis, Andrea Di Sorbo, Gerardo Canfora, and Sebastiano Panichella. 2021. Predicting issue types on GitHub. Science of Computer Programming (2021).

    [41]

    Rafael Kallis, Maliheh Izadi, Luca Pascarella, Oscar Chaparro, and Pooja Rani. 2023. The NLBSE'23 Tool Competition. In Proceedings of The 2nd Intl. Workshop on Natural Language-based Software Engineering (NLBSE'23).

    [42]

    Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling Laws for Neural Language Models. arXiv:2001.08361 [cs.LG]

    [43]

    Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large Language Models are Zero-Shot Reasoners. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 22199--22213. https://proceedings.neurips.cc/paper_files/paper/2022/file/8bb0d291acd4acf06ef112099c16f326-Paper-Conference.pdf

    [44]

    Márk Lajkó, Viktor Csuvik, and László Vidács. 2022. Towards JavaScript program repair with Generative Pre-trained Transformer (GPT-2). In 2022 IEEE/ACM International Workshop on Automated Program Repair (APR). 61--68.

    Digital Library

    [45]

    Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. arXiv:1909.11942 [cs.CL]

    [46]

    Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 7871--7880.

    [47]

    Zhifang Liao, Dayu He, Zhijie Chen, Xiaoping Fan, Yan Zhang, and Shengzong Liu. 2018. Exploring the characteristics of issue-related behaviors in GitHub using visualization techniques. IEEE Access (2018).

    [48]

    Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023. Lost in the Middle: How Language Models Use Long Contexts. arXiv:2307.03172 [cs.CL]

    [49]

    Anders Giovanni Møller, Jacob Aarup Dalsgaard, Arianna Pera, and Luca Maria Aiello. 2023. Is a prompt and a few samples all you need? Using GPT-4 for data augmentation in low-resource classification tasks. arXiv:2304.13861 [cs.CL]

    [50]

    OpenAI. 2022. ChatGPT: Optimizing Language Models for Dialogue.

    [51]

    OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774 [cs.CL]

    [52]

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35 (2022), 27730--27744.

    [53]

    Shuyin Ouyang, Jie M. Zhang, Mark Harman, and Meng Wang. 2023. LLM is Like a Box of Chocolates: the Non-determinism of ChatGPT in Code Generation. arXiv:2308.02828 [cs.SE]

    [54]

    Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2023. Unifying Large Language Models and Knowledge Graphs: A Roadmap. arXiv:2306.08302 [cs.CL]

    [55]

    Nitish Pandey, Debarshi Sanyal, Abir Hudait, and Amitava Sen. 2017. Automated classification of software issue reports using machine learning techniques: an empirical study. Innovations in Systems and Software Engineering (12 2017).

    Digital Library

    [56]

    Sebastiano Panichella, Gabriele Bavota, Massimiliano Di Penta, Gerardo Canfora, and Giuliano Antoniol. 2014. How Developers' Collaborations Identified from Different Sources Tell Us about Code Changes. In 2014 IEEE International Conference on Software Maintenance and Evolution.

    Digital Library

    [57]

    Alec Radford and Karthik Narasimhan. 2018. Improving Language Understanding by Generative Pre-Training. https://api.semanticscholar.org/CorpusID:49313245, LastaccessedNov.2023

    [58]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. J. Mach. Learn. Res. 21, 1, Article 140 (jan 2020), 67 pages.

    [59]

    Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, Hong Kong, China, 3982--3992.

    [60]

    David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. 1986. Learning representations by back-propagating errors. Nature 323, 6088 (1986), 533--536.

    [61]

    Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2020. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108 [cs.CL]

    [62]

    Fabrizio Sebastiani. 2002. Machine learning in automated text categorization. Comput. Surveys (2002).

    Digital Library

    [63]

    D. Sobania, M. Briesch, C. Hanna, and J. Petke. 2023. An Analysis of the Automatic Bug Fixing Performance of ChatGPT. In 2023 IEEE/ACM International Workshop on Automated Program Repair (APR). IEEE Computer Society, Los Alamitos, CA, USA, 23--30.

    [64]

    Jeniya Tabassum, Mounica Maddela, Wei Xu, and Alan Ritter. 2020. Code and Named Entity Recognition in StackOverflow. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL). https://www.aclweb.org/anthology/2020.acl-main.443/

    [65]

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. LLaMA: Open and Efficient Foundation Language Models. arXiv:2302.13971 [cs.CL]

    [66]

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023. Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv:2307.09288 [cs.CL]

    [67]

    Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf. 2023. Zephyr: Direct Distillation of LM Alignment. arXiv:2310.16944 [cs.LG]

    [68]

    Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg. 2022. Efficient Few-Shot Learning Without Prompts.

    [69]

    Joseph Vargovich, Fabio Santos, Jacob Penney, Marco A. Gerosa, and Igor Steinmacher. 2023. GiveMeLabeledIssues: An Open Source Issue Recommendation System. In 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR). 402--406.

    [70]

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).

    [71]

    Anthony Viera and Joanne Garrett. 2005. Understanding Interobserver Agreement: The Kappa Statistic. Family medicine 37 (06 2005), 360--3.

    [72]

    Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu, Song Wang, and Qing Wang. 2023. Software Testing with Large Language Model: Survey, Landscape, and Vision. arXiv:2307.07221 [cs.SE]

    [73]

    Jun Wang, Xiaofang Zhang, and Lin Chen. 2021. How well do pre-trained contextual language representations recommend labels for GitHub issues? Knowledge-Based Systems (2021).

    Digital Library

    [74]

    Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021. Want To Reduce Labeling Cost? GPT-3 Can Help. In Findings of the Association for Computational Linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, 4195--4205.

    [75]

    Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi. 2021. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8696--8708.

    [76]

    Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022. Emergent Abilities of Large Language Models. arXiv:2206.07682 [cs.CL]

    [77]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 24824--24837. https://proceedings.neurips.cc/paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf

    [78]

    Frank F. Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022. A Systematic Evaluation of Large Language Models of Code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (San Diego, CA, USA) (MAPS 2022). Association for Computing Machinery, New York, NY, USA, 1--10.

    Digital Library

    [79]

    Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 2023. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond. arXiv:2304.13712 [cs.CL]

    [80]

    Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32 (2019).

    [81]

    Zhiqiang Yuan, Junwei Liu, Qiancheng Zi, Mingwei Liu, Xin Peng, and Yiling Lou. 2023. Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation. arXiv:2308.01240 [cs.CL]

    [82]

    Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, and Jian-Guang Lou. 2023. Large Language Models Meet NL2Code: A Survey. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Toronto, Canada, 7443--7464.

    [83]

    Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu. 2020. Retrieval-Based Neural Source Code Summarization. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE '20). Association for Computing Machinery, New York, NY, USA, 1385--1397.

    Digital Library

    [84]

    Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, and David Lo. 2023. Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models. arXiv:2310.11113 [cs.SE]

    [85]

    Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023. A Survey of Large Language Models. arXiv:2303.18223 [cs.CL]

    [86]

    Yu Zhou, Yanxiang Tong, Ruihang Gu, and Harald C. Gall. 2014. Combining Text Mining and Data Mining for Bug Report Classification. 2014 IEEE International Conference on Software Maintenance and Evolution (2014).

    [87]

    Yiming Zhu, Peixian Zhang, Ehsan-Ul Haq, Pan Hui, and Gareth Tyson. 2023. Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks. arXiv:2304.10145 [cs.AI]

    [88]

    Liu Zhuang, Lin Wayne, Shi Ya, and Zhao Jun. 2021. A Robustly Optimized BERT Pre-training Approach with Post-training. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, Sheng Li, Maosong Sun, Yang Liu, Hua Wu, Kang Liu, Wanxiang Che, Shizhu He, and Gaoqi Rao (Eds.). Chinese Information Processing Society of China, Huhhot, China, 1218--1227. https://aclanthology.org/2021.ccl-1.108

    [89]

    Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2020. Fine-Tuning Language Models from Human Preferences. arXiv:1909.08593 [cs.CL]

    Index Terms

    1. Leveraging GPT-like LLMs to Automate Issue Labeling

      1. Information systems

        1. Information retrieval

          1. Retrieval tasks and goals

            1. Clustering and classification

        2. Software and its engineering

          1. Software creation and management

            1. Software post-development issues

              1. Documentation

                1. Maintaining software

                  1. Software evolution

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            Leveraging GPT-like LLMs to Automate Issue Labeling | Proceedings of the 21st International Conference on Mining Software Repositories (5)

            MSR '24: Proceedings of the 21st International Conference on Mining Software Repositories

            April 2024

            788 pages

            ISBN:9798400705878

            DOI:10.1145/3643991

            • Chair:
            • Diomidis Spinellis,
            • Program Chair:
            • Alberto Bacchelli,
            • Program Co-chair:
            • Eleni Constantinou

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            Published: 02 July 2024

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            Author Tags

            1. LLM
            2. issue labeling
            3. GPT
            4. software maintenance and evolution
            5. labeling unstructured data

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            2025 IEEE/ACM 46th International Conference on Software Engineering

            April 26 - May 3, 2025

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