{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T04:21:47Z","timestamp":1769746907510,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,14]],"date-time":"2024-04-14T00:00:00Z","timestamp":1713052800000},"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":[[2024,4,14]]},"DOI":"10.1145\/3639477.3639754","type":"proceedings-article","created":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T13:27:26Z","timestamp":1717162046000},"page":"392-404","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["FaultProfIT: Hierarchical Fault Profiling of Incident Tickets in Large-scale Cloud Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6962-5292","authenticated-orcid":false,"given":"Junjie","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0037-1912","authenticated-orcid":false,"given":"Jinyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, HongKong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5158-6716","authenticated-orcid":false,"given":"Zhuangbin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1988-6219","authenticated-orcid":false,"given":"Zhihan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8370-644X","authenticated-orcid":false,"given":"Yichen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5831-9474","authenticated-orcid":false,"given":"Jiazhen","family":"Gu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5556-4004","authenticated-orcid":false,"given":"Cong","family":"Feng","sequence":"additional","affiliation":[{"name":"Computing and Networking Innovation Lab, Huawei Cloud Computing Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6307-7310","authenticated-orcid":false,"given":"Zengyin","family":"Yang","sequence":"additional","affiliation":[{"name":"Computing and Networking Innovation Lab, Huawei Cloud Computing Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9733-4346","authenticated-orcid":false,"given":"Yongqiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Computing and Networking Innovation Lab, Huawei Cloud Computing Technology, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3666-5798","authenticated-orcid":false,"given":"Michael R.","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,31]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"2021 Facebook outage. https:\/\/en.wikipedia.org\/wiki\/2021_Facebook_outage. [Online","year":"2023","unstructured":"2021. 2021 Facebook outage. https:\/\/en.wikipedia.org\/wiki\/2021_Facebook_outage. [Online; accessed 31 July 2023]."},{"key":"e_1_3_2_1_2_1","volume-title":"AWS Post-Event Summaries. https:\/\/aws.amazon.com\/cn\/premiumsupport\/technology\/pes\/. [Online","year":"2023","unstructured":"2023. AWS Post-Event Summaries. https:\/\/aws.amazon.com\/cn\/premiumsupport\/technology\/pes\/. [Online; accessed 31 July 2023]."},{"key":"e_1_3_2_1_3_1","volume-title":"Azure status history. https:\/\/azure.status.microsoft\/en-us\/status\/history\/. [Online","year":"2023","unstructured":"2023. Azure status history. https:\/\/azure.status.microsoft\/en-us\/status\/history\/. [Online; accessed 31 July 2023]."},{"key":"e_1_3_2_1_4_1","volume-title":"Google Cloud Status Dashboard. https:\/\/status.cloud.google.com\/summary. [Online","year":"2023","unstructured":"2023. Google Cloud Status Dashboard. https:\/\/status.cloud.google.com\/summary. [Online; accessed 31 July 2023]."},{"key":"e_1_3_2_1_5_1","volume-title":"2023 IEEE\/ACM International Workshop on Cloud Intelligence & AIOps (AIOps). IEEE, 1--7.","author":"Ahmed Salman","year":"2023","unstructured":"Salman Ahmed, Muskaan Singh, Brendan Doherty, Effirul Ramlan, Kathryn Harkin, Magda Bucholc, and Damien Coyle. 2023. Knowledge-based intelligent system for IT incident DevOps. In 2023 IEEE\/ACM International Workshop on Cloud Intelligence & AIOps (AIOps). IEEE, 1--7."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE48619.2023.00149"},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2890--2896","author":"Alzantot Moustafa","year":"2018","unstructured":"Moustafa Alzantot, Yash Sharma, Ahmed Elgohary, Bo-Jhang Ho, Mani Srivastava, and Kai-Wei Chang. 2018. Generating Natural Language Adversarial Examples. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2890--2896."},{"key":"e_1_3_2_1_8_1","volume-title":"2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 255--267","author":"Chakraborty Sarthak","year":"2023","unstructured":"Sarthak Chakraborty, Shubham Agarwal, Shaddy Garg, Abhimanyu Sethia, Udit Narayan Pandey, Videh Aggarwal, and Shiv Saini. 2023. ESRO: Experience Assisted Service Reliability against Outages. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 255--267."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-SEIP.2019.00020"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00042"},{"key":"e_1_3_2_1_11_1","volume-title":"International conference on machine learning. PMLR, 1597--1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Yinfang Chen Huaibing Xie Minghua Ma Yu Kang Xin Gao Liu Shi Yunjie Cao Xuedong Gao Hao Fan Ming Wen et al. 2023. Empowering Practical Root Cause Analysis by Large Language Models for Cloud Incidents. arXiv preprint arXiv:2305.15778 (2023).","DOI":"10.1145\/3627703.3629553"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409768"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417055"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678746"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.58"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-NLT). 4171--4186","author":"Devlin Jacob","year":"2019","unstructured":"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 (NAACL-NLT). 4171--4186."},{"key":"e_1_3_2_1_18_1","volume-title":"Insights and Tools for Root Cause Labelling of Incidents in Microsoft Azure. In 2023 USENIX Annual Technical Conference (USENIX ATC 23)","author":"Dogga Pradeep","year":"2023","unstructured":"Pradeep Dogga, Chetan Bansal, Richard Costleigh, Gopinath Jayagopal, Suman Nath, and Xuchao Zhang. 2023. AutoARTS: Taxonomy, Insights and Tools for Root Cause Labelling of Incidents in Microsoft Azure. In 2023 USENIX Annual Technical Conference (USENIX ATC 23). 359--372."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.26"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387514.3405867"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3542929.3563482"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409741"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3417061"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2987550.2987583"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.442"},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of the 16th Workshop on Hot Topics in Operating Systems (HotOS). 150--155","author":"Huang Peng","year":"2017","unstructured":"Peng Huang, Chuanxiong Guo, Lidong Zhou, Jacob R Lorch, Yingnong Dang, Murali Chintalapati, and Randolph Yao. 2017. Gray failure: The achilles' heel of cloud-scale systems. In Proceedings of the 16th Workshop on Hot Topics in Operating Systems (HotOS). 150--155."},{"key":"e_1_3_2_1_27_1","volume-title":"Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations.","author":"Jang Eric","year":"2016","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical Reparameterization with Gumbel-Softmax. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_28_1","unstructured":"Pengxiang Jin Shenglin Zhang Minghua Ma Haozhe Li Yu Kang Liqun Li Yudong Liu Bo Qiao Chaoyun Zhang Pu Zhao et al. 2023. Assess and Summarize: Improve Outage Understanding with Large Language Models. arXiv preprint arXiv:2305.18084 (2023)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.197"},{"key":"e_1_3_2_1_31_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_32_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_33_1","volume-title":"Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 116--128","author":"Lee Cheryl","year":"2023","unstructured":"Cheryl Lee, Tianyi Yang, Zhuangbin Chen, Yuxin Su, and Michael R Lyu. 2023. Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 116--128."},{"key":"e_1_3_2_1_34_1","volume-title":"Fighting the Fog of War: Automated Incident Detection for Cloud Systems. In 2021 USENIX Annual Technical Conference (USENIX ATC). USENIX Association, 131--146","author":"Li Liqun","year":"2021","unstructured":"Liqun Li, Xu Zhang, Xin Zhao, Hongyu Zhang, Yu Kang, Pu Zhao, Bo Qiao, Shilin He, Pochian Lee, Jeffrey Sun, Feng Gao, Li Yang, Qingwei Lin, Saravanakumar Rajmohan, Zhangwei Xu, and Dongmei Zhang. 2021. Fighting the Fog of War: Automated Incident Detection for Cloud Systems. In 2021 USENIX Annual Technical Conference (USENIX ATC). USENIX Association, 131--146. https:\/\/www.usenix.org\/conference\/atc21\/presentation\/li-liqun"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3544497.3544499"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3540250.3549092"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3317550.3321438"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 45th International Conference on Software Engineering","author":"Liu Jinyang","unstructured":"Jinyang Liu, Shilin He, Zhuangbin Chen, Liqun Li, Yu Kang, Xu Zhang, Pinjia He, Hongyu Zhang, Qingwei Lin, Zhangwei Xu, Saravan Rajmohan, Dongmei Zhang, and Michael R. Lyu. 2023. Incident-Aware Duplicate Ticket Aggregation for Cloud Systems. In Proceedings of the 45th International Conference on Software Engineering (Melbourne, Victoria, Australia) (ICSE '23). 2299--2311."},{"key":"e_1_3_2_1_39_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_40_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-short.8"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2013.6693105"},{"key":"e_1_3_2_1_42_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems (NeurIPS) 32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems (NeurIPS) 32 (2019)."},{"key":"e_1_3_2_1_43_1","volume-title":"LogEncoder: Log-based Contrastive Representation Learning for anomaly detection","author":"Qi Jiaxing","year":"2023","unstructured":"Jiaxing Qi, Zhongzhi Luan, Shaohan Huang, Carol Fung, Hailong Yang, Hanlu Li, Danfeng Zhu, and Depei Qian. 2023. LogEncoder: Log-based Contrastive Representation Learning for anomaly detection. IEEE Transactions on Network and Service Management (2023)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3561970"},{"key":"e_1_3_2_1_45_1","volume-title":"Contrastive Learning for API Aspect Analysis. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 637--648","author":"Shahariar GM","year":"2023","unstructured":"GM Shahariar, Tahmid Hasan, Anindya Iqbal, and Gias Uddin. 2023. Contrastive Learning for API Aspect Analysis. In 2023 38th IEEE\/ACM International Conference on Automated Software Engineering (ASE). IEEE, 637--648."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s10664-022-10159-w","article-title":"SoftNER: Mining knowledge graphs from cloud incidents","volume":"27","author":"Shetty Manish","year":"2022","unstructured":"Manish Shetty, Chetan Bansal, Sumit Kumar, Nikitha Rao, and Nachiappan Nagappan. 2022. SoftNER: Mining knowledge graphs from cloud incidents. Empirical Software Engineering 27, 4 (2022), 93.","journal-title":"Empirical Software Engineering"},{"key":"e_1_3_2_1_47_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_48_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_49_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_50_1","volume-title":"2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE). IEEE, 36--46","author":"Wang Weijing","year":"2021","unstructured":"Weijing Wang, Junjie Chen, Lin Yang, Hongyu Zhang, Pu Zhao, Bo Qiao, Yu Kang, Qingwei Lin, Saravanakumar Rajmohan, Feng Gao, et al. 2021. How long will it take to mitigate this incident for online service systems?. In 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE). IEEE, 36--46."},{"key":"e_1_3_2_1_51_1","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7109--7119","author":"Wang Zihan","year":"2022","unstructured":"Zihan Wang, Peiyi Wang, Lianzhe Huang, Xin Sun, and Houfeng Wang. 2022. Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 7109--7119."},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of the 44th International Conference on Software Engineering. 376--387","author":"Wei Moshi","year":"2022","unstructured":"Moshi Wei, Nima Shiri Harzevili, Yuchao Huang, Junjie Wang, and Song Wang. 2022. Clear: contrastive learning for api recommendation. In Proceedings of the 44th International Conference on Software Engineering. 376--387."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Thomas Wolf Lysandre Debut Victor Sanh Julien Chaumond Clement Delangue Anthony Moi Pierric Cistac Tim Rault R\u00e9mi Louf Morgan Funtowicz et al. 2019. Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019).","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_2_1_54_1","volume-title":"Clear: Contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466","author":"Wu Zhuofeng","year":"2020","unstructured":"Zhuofeng Wu, Sinong Wang, Jiatao Gu, Madian Khabsa, Fei Sun, and Hao Ma. 2020. Clear: Contrastive learning for sentence representation. arXiv preprint arXiv:2012.15466 (2020)."},{"key":"e_1_3_2_1_55_1","first-page":"28877","article-title":"Do transformers really perform badly for graph representation","volume":"34","author":"Ying Chengxuan","year":"2021","unstructured":"Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems 34 (2021), 28877--28888.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_56_1","unstructured":"Alessandro Zangari Matteo Marcuzzo Michele Schiavinato Matteo Rizzo Andrea Gasparetto Andrea Albarelli et al. 2023. Hierarchical Text Classification: a review of current research. EXPERT SYSTEMS WITH APPLICATIONS 224 (2023)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467190"},{"key":"e_1_3_2_1_58_1","volume-title":"Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE). 1253--1263","author":"Zhang Xu","year":"2021","unstructured":"Xu Zhang, Yong Xu, Si Qin, Shilin He, Bo Qiao, Ze Li, Hongyu Zhang, Xukun Li, Yingnong Dang, Qingwei Lin, et al. 2021. Onion: identifying incident-indicating logs for cloud systems. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC\/FSE). 1253--1263."},{"key":"e_1_3_2_1_59_1","volume-title":"2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 264--274","author":"Zhao Yujin","year":"2023","unstructured":"Yujin Zhao, Ling Jiang, Ye Tao, Songlin Zhang, Changlong Wu, Yifan Wu, Tong Jia, Ying Li, and Zhonghai Wu. 2023. How to Manage Change-Induced Incidents? Lessons from the Study of Incident Life Cycle. In 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE). IEEE, 264--274."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.104"}],"event":{"name":"ICSE-SEIP '24: 46th International Conference on Software Engineering: Software Engineering in Practice","location":"Lisbon Portugal","acronym":"ICSE-SEIP '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS","Faculty of Engineering of University of Porto"]},"container-title":["Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639477.3639754","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3639477.3639754","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T23:44:32Z","timestamp":1750290272000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3639477.3639754"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,14]]},"references-count":60,"alternative-id":["10.1145\/3639477.3639754","10.1145\/3639477"],"URL":"https:\/\/doi.org\/10.1145\/3639477.3639754","relation":{},"subject":[],"published":{"date-parts":[[2024,4,14]]},"assertion":[{"value":"2024-05-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}