{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:05:56Z","timestamp":1784300756628,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T00:00:00Z","timestamp":1783209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100013290","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2024YFB4505903"],"award-info":[{"award-number":["2024YFB4505903"]}],"id":[{"id":"10.13039\/501100013290","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing National Research Center for Information Science and Technology"},{"name":"National Natural Science Foundation of China","award":["62202445"],"award-info":[{"award-number":["62202445"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,7,5]]},"DOI":"10.1145\/3803437.3805214","type":"proceedings-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:27:39Z","timestamp":1784298459000},"page":"404-414","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Eagle: Leveraging Operations Documents for Comprehensive Benchmark Question Generation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0324-7749","authenticated-orcid":false,"given":"Yuhe","family":"Liu","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, Beijing, China"},{"name":"Beijing National Research Center for Information Science and Technology, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9288-4787","authenticated-orcid":false,"given":"Changhua","family":"Pei","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7469-6511","authenticated-orcid":false,"given":"Hang","family":"Wang","sequence":"additional","affiliation":[{"name":"Computer Network Information Center, Chinese Academy of Sciences, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7364-1307","authenticated-orcid":false,"given":"Longlong","family":"Xu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, Beijing, China"},{"name":"Beijing National Research Center for Information Science and Technology, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1558-2843","authenticated-orcid":false,"given":"Xiaogang","family":"Dong","sequence":"additional","affiliation":[{"name":"Huawei, Xian'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3345-199X","authenticated-orcid":false,"given":"Zhen","family":"Feng","sequence":"additional","affiliation":[{"name":"Huawei, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0562-4786","authenticated-orcid":false,"given":"Li","family":"Zheng","sequence":"additional","affiliation":[{"name":"China Academy of Information and Communications Technology, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2400-7340","authenticated-orcid":false,"given":"Kehang","family":"Ji","sequence":"additional","affiliation":[{"name":"China Academy of Information and Communications Technology, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5113-838X","authenticated-orcid":false,"given":"Dan","family":"Pei","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, Beijing, China"},{"name":"Beijing National Research Center for Information Science and Technology, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Anthropic. 2025. Claude 3.7 Sonnet and Claude Code. https:\/\/www.anthropic.com\/news\/claude-3-7-sonnet. Accessed: 2025-05-12."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.2"},{"key":"e_1_3_2_1_3_1","volume-title":"Logeval: A comprehensive benchmark suite for large language models in log analysis. arXiv preprint arXiv:2407.01896","author":"Cui Tianyu","year":"2024","unstructured":"Tianyu Cui, Shiyu Ma, Ziang Chen, Tong Xiao, Shimin Tao, Yilun Liu, Shenglin Zhang, Duoming Lin, Changchang Liu, Yuzhe Cai, and others. 2024. Logeval: A comprehensive benchmark suite for large language models in log analysis. arXiv preprint arXiv:2407.01896 (2024)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-11222-2"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1090"},{"key":"e_1_3_2_1_6_1","volume-title":"Redis Development and Operations","author":"Fu Lei","unstructured":"Lei Fu and Yijun Zhang. 2017. Redis Development and Operations. China Machine Press."},{"key":"e_1_3_2_1_7_1","unstructured":"Google DeepMind. 2025. Gemini 2.5: Our Most Intelligent AI Model. https:\/\/blog.google\/technology\/google-deepmind\/gemini-model-thinking-updates-march-2025\/. Accessed: 2025-05-12."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.177"},{"key":"e_1_3_2_1_9_1","volume-title":"OWL: A Large Language Model for IT Operations. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SZOQ9RKYJu","author":"Guo Hongcheng","year":"2024","unstructured":"Hongcheng Guo, Jian Yang, Jiaheng Liu, Liqun Yang, Linzheng Chai, Jiaqi Bai, Junran Peng, Xiaorong Hu, Chao Chen, Dongfeng Zhang, xu Shi, Tieqiao Zheng, liangfan zheng, Bo Zhang, Ke Xu, and Zhoujun Li. 2024. OWL: A Large Language Model for IT Operations. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SZOQ9RKYJu"},{"key":"e_1_3_2_1_10_1","volume-title":"UNIX and Linux System Administration Handbook","author":"Hein Trent R.","unstructured":"Trent R. Hein, Evi Nemeth, Garth Snyder, Ben Whaley, and Dan Mackin. 2017. UNIX and Linux System Administration Handbook (5th ed.). Addison-Wesley Professional.","edition":"5"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2024.3516055"},{"key":"e_1_3_2_1_12_1","unstructured":"Andrew Lerner. 2017. AIOps Platforms\u2014Gartner."},{"key":"e_1_3_2_1_13_1","unstructured":"Yuhe Liu Changhua Pei Longlong Xu Bohan Chen Mingze Sun Zhirui Zhang Yongqian Sun Shenglin Zhang Kun Wang Haiming Zhang Jianhui Li Gaogang Xie Xidao Wen Xiaohui Nie Minghua Ma and Dan Pei. 2024. OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models. http:\/\/arxiv.org\/abs\/2310.07637 arXiv:2310.07637."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2309.05557"},{"key":"e_1_3_2_1_15_1","volume-title":"Bach","author":"Nayak Nihal V.","year":"2024","unstructured":"Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach. 2024. Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation. In Findings of the Association for Computational Linguistics: ACL 2024."},{"key":"e_1_3_2_1_16_1","unstructured":"OpenAI. 2023. GPT-4 Technical Report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_1_17_1","unstructured":"QwenLM. 2023. QwenLM\/Qwen-7B. https:\/\/github.com\/QwenLM\/Qwen-7B"},{"key":"e_1_3_2_1_18_1","unstructured":"Ke Wang Jiahui Zhu Minjie Ren Zeming Liu Shiwei Li Zongye Zhang Chenkai Zhang Xiaoyu Wu Qiqi Zhan Qingjie Liu and Yunhong Wang. 2024. A Survey on Data Synthesis and Augmentation for Large Language Models. http:\/\/arxiv.org\/abs\/2410.12896 arXiv:2410.12896."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.754"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 4966\u20134974","author":"Wang Zefan","year":"2023","unstructured":"Zefan Wang, Zichuan Liu, Yingying Zhang, Aoxiao Zhong, Jihong Wang, Fengbin Yin, Lunting Fan, Lingfei Wu, and Qingsong Wen. 2023. Rcagent: Cloud root cause analysis by autonomous agents with tool-augmented large language models. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 4966\u20134974."},{"key":"e_1_3_2_1_21_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Xu Can","year":"2024","unstructured":"Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Qingwei Lin, and Daxin Jiang. 2024. WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3597503.3639155"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.34"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3663529.3663826"},{"key":"e_1_3_2_1_25_1","unstructured":"Zabbix Documentation Team. 2018. Zabbix Documentation 4.0 (Chinese Version). Zabbix. https:\/\/www.zabbix.com\/documentation\/4.0\/zh\/manual Accessed: 2025-05-05."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.348"},{"key":"e_1_3_2_1_27_1","volume-title":"D-bot: Database diagnosis system using large language models. arXiv preprint arXiv:2312.01454","author":"Zhou Xuanhe","year":"2023","unstructured":"Xuanhe Zhou, Guoliang Li, Zhaoyan Sun, Zhiyuan Liu, Weize Chen, Jianming Wu, Jiesi Liu, Ruohang Feng, and Guoyang Zeng. 2023. D-bot: Database diagnosis system using large language models. arXiv preprint arXiv:2312.01454 (2023)."}],"event":{"name":"FSE Companion '26: 34th ACM International Conference on the Foundations of Software Engineering","location":"Concordia University Montreal QC Canada","acronym":"FSE Companion '26","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering"]},"container-title":["Proceedings of the 34th ACM International Conference on the Foundations of Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3803437.3805214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:30:54Z","timestamp":1784298654000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3803437.3805214"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,5]]},"references-count":27,"alternative-id":["10.1145\/3803437.3805214","10.1145\/3803437"],"URL":"https:\/\/doi.org\/10.1145\/3803437.3805214","relation":{},"subject":[],"published":{"date-parts":[[2026,7,5]]},"assertion":[{"value":"2026-07-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}