Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing supervised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose DivLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance.
Wed 17 AprDisplayed time zone: Lisbon change
14:00 - 15:30 | Analytics & AIResearch Track / Journal-first Papers at Sophia de Mello Breyner Andresen Chair(s): Lingming Zhang University of Illinois at Urbana-Champaign | ||
14:00 15mTalk | DeepLSH: Deep Locality-Sensitive Hash Learning for Fast and Efficient Near-Duplicate Crash Report Detection Research Track Youcef REMIL INSA Lyon, INFOLOGIC, Anes Bendimerad Infologic, Romain Mathonat Infologic, Chedy raissi Ubisoft, Mehdi Kaytoue Infologic | ||
14:15 15mTalk | DivLog: Log Parsing with Prompt Enhanced In-Context Learning Research Track Junjielong Xu The Chinese University of Hong Kong, Shenzhen, Ruichun Yang The Chinese University of Hong Kong, Shenzhen, Yintong Huo The Chinese University of Hong Kong, Chengyu Zhang ETH Zurich, Pinjia He Chinese University of Hong Kong, Shenzhen | ||
14:30 15mTalk | Where is it? Tracing the Vulnerability-relevant Files from Vulnerability Reports Research Track Jiamou Sun CSIRO's Data61, Jieshan Chen CSIRO's Data61, Zhenchang Xing CSIRO's Data61, Qinghua Lu Data61, CSIRO, Xiwei (Sherry) Xu Data61, CSIRO, Liming Zhu CSIRO’s Data61 | ||
14:45 15mTalk | Demystifying and Detecting Misuses of Deep Learning APIs Research Track Moshi Wei York University, Nima Shiri Harzevili York University, Yuekai Huang Institute of Software, Chinese Academy of Sciences, Jinqiu Yang Concordia University, Junjie Wang Institute of Software, Chinese Academy of Sciences, Song Wang York University | ||
15:00 7mTalk | Toward Understanding Deep Learning Framework Bugs Journal-first Papers Junjie Chen Tianjin University, Yihua Liang College of Intelligence and Computing, Tianjin University, Qingchao Shen Tianjin University, Jiajun Jiang Tianjin University, Shuochuan Li College of Intelligence and Computing, Tianjin University | ||
15:07 7mTalk | Fair Enough: Searching for Sufficient Measures of Fairness Journal-first Papers Suvodeep Majumder North Carolina State University, Joymallya Chakraborty Amazon.com, Gina Bai North Carolina State University, Kathryn Stolee North Carolina State University, Tim Menzies North Carolina State University DOI Pre-print | ||
15:14 7mTalk | Representation Learning for Stack Overflow Posts: How Far are We? Journal-first Papers Junda He Singapore Management University, Xin Zhou Singapore Management University, Singapore, Bowen Xu North Carolina State University, Ting Zhang Singapore Management University, Kisub Kim Singapore Management University, Singapore, Zhou Yang Singapore Management University, Ferdian Thung Singapore Management University, Ivana Clairine Irsan Singapore Management University, David Lo Singapore Management University | ||
15:21 7mTalk | Journal First: Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health) Journal-first Papers DOI Pre-print |