ASE 2025
Sun 16 - Thu 20 November 2025 Seoul, South Korea

This program is tentative and subject to change.

Tue 18 Nov 2025 12:00 - 12:10 at Grand Hall 2 - Fault Localization

Issue solving aims to generate patches to fix reported issues in real-world code repositories according to issue descriptions. Issue localization forms the basis for accurate issue solving. Recently, large language model (LLM) based issue localization methods have demonstrated state-of-the-art performance. However, these methods either search from files mentioned in issue descriptions or in the whole repository and struggle to balance the breadth and depth of the search space to converge on the target efficiently. Moreover, they allow LLM to explore whole repositories freely, making it challenging to control the search direction to prevent the LLM from searching for incorrect targets. Meanwhile, because LLMs may not correctly produce the required interaction formats with the environment, they suffer from search failures.

This paper introduces IGSIL, an LLM-driven, powerful function-level issue localization method without training or indexing. To balance search breadth and depth, IGSIL employs a two-phase code graph search strategy. It first conducts broad exploration at the file level using dynamically constructed module call graphs, and then performs in-depth analysis at the function level by expanding the module call graph into a function call graph and executing iterative searches. To precisely control the search direction, IGSIL designs a pruner to filter unrelated directions and irrelevant contexts. To avoid incorrect interaction formats in long contexts, IGSIL introduces a reflection mechanism that uses additional independent queries in short contexts to enhance formatted abilities. Experiment results demonstrate that IGSIL achieves a Top-1 localization accuracy of 43.3% and 44.6% on SWE-bench Lite and SWE-bench Verified, respectively, with Qwen2.5-Coder-32B, average outperforming the state-of-the-art methods by 96.04%. When IGSIL is integrated into an issue-solving method, Agentless, the issue resolution rate improves by 2.98%–30.5%.

This program is tentative and subject to change.

Tue 18 Nov

Displayed time zone: Seoul change

11:00 - 12:30
11:00
10m
Talk
FaultSeeker: LLM-Empowered Framework for Blockchain Transaction Fault Localization
Research Papers
Kairan Sun Nanyang Technological University, Zhengzi Xu Imperial Global Singapore, Kaixuan Li Nanyang Technological University, Lyuye Zhang Nanyang Technological University, Yuqiang Sun Nanyang Technological University, Liwei Tan MetaTrust Labs, Yang Liu Nanyang Technological University
11:10
10m
Talk
FlexFL: Flexible and Effective Fault Localization With Open-Source Large Language Models
Journal-First Track
Chuyang Xu Zhejiang University, Zhongxin Liu Zhejiang University, Xiaoxue Ren Zhejiang University, Gehao Zhang Ant Group, Ming Liang Ant Group, David Lo Singapore Management University
11:20
10m
Talk
LLM-Based Identification of Null Pointer Exception Patches
Research Papers
Tahir Ullah Beijing Institute of Technology, Waseem Akram Beijing Institute of Technology, Fiza Khaliq Beijing Institute of Technology, Hui Liu Beijing Institute of Technology
11:30
10m
Talk
SpectAcle: Fault Localisation of AI-Enabled CPS by Exploiting Sequences of DNN Controller Inferences
Journal-First Track
Deyun Lyu National Institute of Informatics, Zhenya Zhang Kyushu University, Japan, Paolo Arcaini National Institute of Informatics , Xiao-Yi Zhang University of Science and Technology Beijing, Fuyuki Ishikawa National Institute of Informatics, Jianjun Zhao Kyushu University
11:40
10m
Talk
Sifting Truth from Coincidences: A Two-Stage Positive and Unlabeled Learning Model for Coincidental Correctness Detection
Research Papers
Chunyan Liu Chongqing University, Huan Xie Chongqing University, Yan Lei Chongqing University, Zhenyu Wu School of Big Data & Software Engineering, Chongqing University, Jinping Wang Chonqing University
11:50
10m
Talk
Let the Code Speak: Incorporating Program Dynamic State for Better Method-Level Fault Localization
Research Papers
Yihao Qin , Shangwen Wang National University of Defense Technology, Bo Lin National University of Defense Technology, Xin Peng , Sheng Ouyang National University of Defense Technology, Liqian Chen National University of Defense Technology, Xiaoguang Mao National University of Defense Technology
12:00
10m
Talk
Issue Localization via LLM-Driven Iterative Code Graph Searching
Research Papers
Zhonghao Jiang Zhejiang University, Xiaoxue Ren Zhejiang University, Meng Yan Chongqing University, Wei Jiang Ant Group, Yong Li Ant Group, Zhongxin Liu Zhejiang University
12:10
10m
Talk
Hypergraph Neural Network-based Multi-Granular Root Cause Localization for Microservice Systems
Research Papers
Yaxiao Li Xidian University, Lu Wang Xidian University, Chenxi Zhang Xidian University, Qingshan Li Xidian University, Siming Rong Xidian University, Baiyang Wen Xidian University, Xuyang Li Purdue University, Kun Ma Xidian University, Quanwei Du Xidian University, KeYang Li Xidian University, Lingfeng Pan Xidian University, Xinyue Li Peking University, MingXuan Hui Xidian University
12:20
10m
Talk
Explainable Fault Localization for Programming Assignments via LLM-Guided Annotation
Research Papers
Fang Liu Beihang University, Tianze Wang Beihang University, Li Zhang Beihang University, Zheyu Yang Beihang University, Jing Jiang Beihang University, Zian Sun Beihang University
Pre-print