AUCAD: Automated Construction of Alignment Dataset from Log-Related Issues for Enhancing LLM-based Log Generation
Log statements have become an integral part of modern software systems. Prior research efforts have focused on supporting the decisions of placing log statements, such as where/what to log. With the increasing adoption of Large Language Models (LLMs) for code-related tasks such as code completion or generation, automated approaches for generating log statements have gained much momentum. However, the performance of these approaches still has a long way to go. This paper explores enhancing the performance of LLM-based solutions for automated log statement generation by post-training LLMs with a purpose-built dataset. Thus the primary contribution is a novel approach called AUCAD, which automatically constructs such a dataset with information extracting from log-related issues. Researchers have long noticed that a significant portion of the issues in the open-source community are related to log statements. However, distilling this portion of data requires manual efforts, which is labor-intensive and costly, rendering it impractical. Utilizing our approach, we automatically extract log-related issues from 1,537 entries of log data across 88 projects and identify 808 code snippets (i.e., methods) with retrievable source code both before and after modification of each issue (including log statements) to construct a dataset. Each entry in the dataset consists of a data pair representing high-quality and problematic log statements, respectively. With this dataset, we proceed to post-train multiple LLMs (primarily from the Llama series) for automated log statement generation. Both human and experimental evaluations indicate that these models significantly outperform existing LLM-based solutions, thereby validating the efficacy of our method for constructing a post-training dataset to enhance LLM-based log statement generation.
Sat 21 JunDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
11:00 - 13:00 | Session7: AI for Software Engineering IIIResearch Track at Cosmos 3C Chair(s): Lina Gong Nanjing University of Aeronautics and Astronautic | ||
11:00 15mTalk | Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation Research Track Hongyu Kuang Nanjing University, Ning Zhang Nanjing University, Hui Gao Nanjing University, Xin Zhou Nanjing University, Wesley Assunção North Carolina State University, Xiaoxing Ma Nanjing University, Dong Shao Nanjing University, Guoping Rong Nanjing University, He Zhang Nanjing University | ||
11:15 15mTalk | DesDD: A Design-Enabled Framework with Dual-Layer Debugging for LLM-based Iterative API Orchestrating Research Track Zhuo Cheng Jiangxi normal University, Zhou Zou Jiangxi Normal University, Qing Huang School of Computer Information Engineering, Jiangxi Normal University, Zhenchang Xing CSIRO's Data61, Wei Zhang Jiangxi Meteorological Disaster Emergency Early Warning Center, Jiangxi Meteorological Bureau, Shaochen Wang Jiangxi Normal Univesity, Xueting Yi Jiangxi Meteorological Disaster Emergency Early Warning Center, Jiangxi Meteorological Bureau, Huan Jin School of Information Engineering, Jiangxi University of Technology, Zhiping Liu College of Information Engineering, Gandong University, Zhaojin Lu Jiangxi Tellhow Animation College, Tellhow Group Co.,LTD | ||
11:30 15mTalk | AUCAD: Automated Construction of Alignment Dataset from Log-Related Issues for Enhancing LLM-based Log Generation Research Track Hao Zhang Nanjing University, Dongjun Yu Nanjing University, Lei Zhang Nanjing University, Guoping Rong Nanjing University, YongdaYu Nanjing University, Haifeng Shen Southern Cross University, He Zhang Nanjing University, Dong Shao Nanjing University, Hongyu Kuang Nanjing University | ||
11:45 15mTalk | Enhancement Report Approval Prediction: A Comparative Study of Large Language Models Research Track | ||
12:00 15mTalk | MetaCoder: Generating Code from Multiple Perspectives Research Track chen xin , Zhijie Jiang National University of Defense Technology, Yong Guo National University of Defense Technology, Zhouyang Jia National University of Defense Technology, Si Zheng National University of Defense Technology, Yuanliang Zhang National University of Defense Technology, Shanshan Li National University of Defense Technology | ||
12:15 15mTalk | API-Repo: API-centric Repository-level Code Completion Research Track Zhihao Li State Key Laboratory for Novel Software and Technology, Nanjing University, Chuanyi Li Nanjing University, Changan Niu Software Institute, Nanjing University, Ying Yan State Key Laboratory for Novel Software and Technology, Nanjing University, Jidong Ge Nanjing University, Bin Luo Nanjing University | ||
12:30 15mTalk | AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length Research Track Junhang Cheng Beihang University, Fang Liu Beihang University, Chengru Wu Beihang University, Li Zhang Beihang University Pre-print Media Attached File Attached | ||
12:45 15mTalk | Lightweight Probabilistic Coverage Metrics for Efficient Testing of Deep Neural Networks Research Track Yining Yin Nanjing University, Yang Feng Nanjing University, Shihao Weng Nanjing University, Xinyu Gao , Jia Liu Nanjing University, Zhihong Zhao Nanjing University | ||
Cosmos 3C is the third room in the Cosmos 3 wing.
When facing the main Cosmos Hall, access to the Cosmos 3 wing is on the left, close to the stairs. The area is accessed through a large door with the number “3”, which will stay open during the event.