{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T12:56:17Z","timestamp":1782996977187,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,12]]},"DOI":"10.1145\/3597503.3639155","type":"proceedings-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T16:43:26Z","timestamp":1712940206000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":63,"title":["DivLog: Log Parsing with Prompt Enhanced In-Context Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1516-103X","authenticated-orcid":false,"given":"Junjielong","family":"Xu","sequence":"first","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9452-9762","authenticated-orcid":false,"given":"Ruichun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8798-5667","authenticated-orcid":false,"given":"Yintong","family":"Huo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-289X","authenticated-orcid":false,"given":"Chengyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Zurich, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3377-8129","authenticated-orcid":false,"given":"Pinjia","family":"He","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models. ICSE","author":"Ahmed Toufique","year":"2023","unstructured":"Toufique Ahmed, Supriyo Ghosh, Chetan Bansal, Thomas Zimmermann, Xuchao Zhang, and Saravan Rajmohan. 2023. Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models. ICSE (2023)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00031"},{"key":"e_1_3_2_1_3_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901."},{"key":"e_1_3_2_1_4_1","volume-title":"Fast greedy map inference for determinantal point process to improve recommendation diversity. Advances in Neural Information Processing Systems 31","author":"Chen Laming","year":"2018","unstructured":"Laming Chen, Guoxin Zhang, and Eric Zhou. 2018. Fast greedy map inference for determinantal point process to improve recommendation diversity. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_5_1","volume-title":"Weiyi Shang, and Tse-Hsun Chen.","author":"Dai Hetong","year":"2020","unstructured":"Hetong Dai, Heng Li, Che Shao Chen, Weiyi Shang, and Tse-Hsun Chen. 2020. Logram: Efficient log parsing using n-gram dictionaries. IEEE Transactions on Software Engineering (2020)."},{"key":"e_1_3_2_1_6_1","volume-title":"Haoran Peng, Chenyuan Yang, and Lingming Zhang.","author":"Deng Yinlin","year":"2022","unstructured":"Yinlin Deng, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang, and Lingming Zhang. 2022. Fuzzing Deep-Learning Libraries via Large Language Models. arXiv preprint arXiv:2212.14834 (2022)."},{"key":"e_1_3_2_1_7_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_8_1","unstructured":"Rui Ding Hucheng Zhou Jian-Guang Lou Hongyu Zhang Qingwei Lin Qiang Fu Dongmei Zhang and Tao Xie. 2015. Log2: A cost-aware logging mechanism for performance diagnosis. In 2015 {USENIX} Annual Technical Conference ({USENIX}{ATC} 15). 139--150."},{"key":"e_1_3_2_1_9_1","volume-title":"A Survey for In-context Learning. arXiv preprint arXiv:2301.00234","author":"Dong Qingxiu","year":"2022","unstructured":"Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022. A Survey for In-context Learning. arXiv preprint arXiv:2301.00234 (2022)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0103"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134015"},{"key":"e_1_3_2_1_12_1","volume-title":"Execution anomaly detection in distributed systems through unstructured log analysis. In 2009 ninth IEEE international conference on data mining","author":"Fu Qiang","unstructured":"Qiang Fu, Jian-Guang Lou, Yi Wang, and Jiang Li. 2009. Execution anomaly detection in distributed systems through unstructured log analysis. In 2009 ninth IEEE international conference on data mining. IEEE, 149--158."},{"key":"e_1_3_2_1_13_1","volume-title":"Execution anomaly detection in distributed systems through unstructured log analysis. In 2009 ninth IEEE international conference on data mining","author":"Fu Qiang","unstructured":"Qiang Fu, Jian-Guang Lou, Yi Wang, and Jiang Li. 2009. Execution anomaly detection in distributed systems through unstructured log analysis. In 2009 ninth IEEE international conference on data mining. IEEE, 149--158."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983358"},{"key":"e_1_3_2_1_16_1","volume-title":"An evaluation study on log parsing and its use in log mining. In 2016 46th annual IEEE\/IFIP international conference on dependable systems and networks (DSN)","author":"He Pinjia","unstructured":"Pinjia He, Jieming Zhu, Shilin He, Jian Li, and Michael R Lyu. 2016. An evaluation study on log parsing and its use in log mining. In 2016 46th annual IEEE\/IFIP international conference on dependable systems and networks (DSN). IEEE, 654--661."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2017.2762673"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2017.13"},{"key":"e_1_3_2_1_19_1","volume-title":"A survey on automated log analysis for reliability engineering. ACM computing surveys (CSUR) 54, 6","author":"He Shilin","year":"2021","unstructured":"Shilin He, Pinjia He, Zhuangbin Chen, Tianyi Yang, Yuxin Su, and Michael R Lyu. 2021. A survey on automated log analysis for reliability engineering. ACM computing surveys (CSUR) 54, 6 (2021), 1--37."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3236083"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2016.21"},{"key":"e_1_3_2_1_22_1","volume-title":"SemParser: A Semantic Parser for Log Analysis. arXiv preprint arXiv:2112.12636","author":"Huo Yintong","year":"2021","unstructured":"Yintong Huo, Yuxin Su, Baitong Li, and Michael R Lyu. 2021. SemParser: A Semantic Parser for Log Analysis. arXiv preprint arXiv:2112.12636 (2021)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510101"},{"key":"e_1_3_2_1_24_1","volume-title":"Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054","author":"Kumar Ananya","year":"2022","unstructured":"Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. 2022. Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054 (2022)."},{"key":"e_1_3_2_1_25_1","volume-title":"Log Parsing with Prompt-based Few-shot Learning. arXiv preprint arXiv:2302.07435","author":"Le Van-Hoang","year":"2023","unstructured":"Van-Hoang Le and Hongyu Zhang. 2023. Log Parsing with Prompt-based Few-shot Learning. arXiv preprint arXiv:2302.07435 (2023)."},{"key":"e_1_3_2_1_26_1","volume-title":"Repository of LogPPT. https:\/\/github.com\/LogIntelligence\/LogPPT. [Online","author":"Le Van-Hoang","year":"2023","unstructured":"Van-Hoang Le and Hongyu Zhang. 2023. Repository of LogPPT. https:\/\/github.com\/LogIntelligence\/LogPPT. [Online; accessed 19-March-2023]."},{"key":"e_1_3_2_1_27_1","volume-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","author":"Lewis Mike","year":"2019","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549099"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2889160.2889232"},{"key":"e_1_3_2_1_30_1","volume-title":"What Makes Good In-Context Examples for GPT-3? arXiv preprint arXiv:2101.06804","author":"Liu Jiachang","year":"2021","unstructured":"Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021. What Makes Good In-Context Examples for GPT-3? arXiv preprint arXiv:2101.06804 (2021)."},{"key":"e_1_3_2_1_31_1","first-page":"1","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys 55, 9 (2023), 1--35.","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_1_32_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511993"},{"key":"e_1_3_2_1_34_1","volume-title":"2010 USENIX Annual Technical Conference (USENIX ATC 10)","author":"Lou Jian-Guang","year":"2010","unstructured":"Jian-Guang Lou, Qiang Fu, Shenqi Yang, Ye Xu, and Jiang Li. 2010. Mining invariants from console logs for system problem detection. In 2010 USENIX Annual Technical Conference (USENIX ATC 10)."},{"key":"e_1_3_2_1_35_1","volume-title":"Template-free prompt tuning for few-shot NER. arXiv preprint arXiv:2109.13532","author":"Ma Ruotian","year":"2021","unstructured":"Ruotian Ma, Xin Zhou, Tao Gui, Yiding Tan, Linyang Li, Qi Zhang, and Xuanjing Huang. 2021. Template-free prompt tuning for few-shot NER. arXiv preprint arXiv:2109.13532 (2021)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557154"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/SCC.2013.73"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSR.2010.5463281"},{"key":"e_1_3_2_1_39_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever et al. 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_3_2_1_40_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1 8 (2019) 9."},{"key":"e_1_3_2_1_41_1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"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.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_1_42_1","volume-title":"Learning to retrieve prompts for in-context learning. arXiv preprint arXiv:2112.08633","author":"Rubin Ohad","year":"2021","unstructured":"Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021. Learning to retrieve prompts for in-context learning. arXiv preprint arXiv:2112.08633 (2021)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2013.6606586"},{"key":"e_1_3_2_1_44_1","volume-title":"Length matters: Clustering system log messages using length of words. arXiv preprint arXiv:1611.03213","author":"Shima Keiichi","year":"2016","unstructured":"Keiichi Shima. 2016. Length matters: Clustering system log messages using length of words. arXiv preprint arXiv:1611.03213 (2016)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063690"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPOM.2003.1251233"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNSM.2015.7367331"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549113"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549176"},{"key":"e_1_3_2_1_50_1","volume-title":"Chi, Quoc Le, and Denny Zhou","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022. Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201.11903 (2022)."},{"key":"e_1_3_2_1_51_1","volume-title":"Revisiting the Plastic Surgery Hypothesis via Large Language Models. arXiv preprint arXiv:2303.10494","author":"Xia Chunqiu Steven","year":"2023","unstructured":"Chunqiu Steven Xia, Yifeng Ding, and Lingming Zhang. 2023. Revisiting the Plastic Surgery Hypothesis via Large Language Models. arXiv preprint arXiv:2303.10494 (2023)."},{"key":"e_1_3_2_1_52_1","volume-title":"Conversational automated program repair. arXiv preprint arXiv:2301.13246","author":"Xia Chunqiu Steven","year":"2023","unstructured":"Chunqiu Steven Xia and Lingming Zhang. 2023. Conversational automated program repair. arXiv preprint arXiv:2301.13246 (2023)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338931"},{"key":"e_1_3_2_1_54_1","volume-title":"International Conference on Machine Learning. PMLR, 12697--12706","author":"Zhao Zihao","year":"2021","unstructured":"Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. Calibrate before use: Improving few-shot performance of language models. In International Conference on Machine Learning. PMLR, 12697--12706."},{"key":"e_1_3_2_1_55_1","unstructured":"Jieming Zhu Shilin He Jinyang Liu Pinjia He Qi Xie Zibin Zheng and Michael R Lyu. [n. d.]. Repository of LogPAI. https:\/\/github.com\/logpai\/loghub."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP.2019.00021"}],"event":{"name":"ICSE '24: IEEE\/ACM 46th International Conference on Software Engineering","location":"Lisbon Portugal","acronym":"ICSE '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS","Faculty of Engineering of University of Porto"]},"container-title":["Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639155","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597503.3639155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:12Z","timestamp":1750286952000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":56,"alternative-id":["10.1145\/3597503.3639155","10.1145\/3597503"],"URL":"https:\/\/doi.org\/10.1145\/3597503.3639155","relation":{},"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"2024-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}