Sifting Truth from Coincidences: A Two-Stage Positive and Unlabeled Learning Model for Coincidental Correctness Detection
This program is tentative and subject to change.
Fault localization (FL) can identify the fault’s location by analyzing the execution information from test cases in the program. This execution information serves as the foundation for FL to infer latent causal relationships between fault entities and failed results. However, this execution information contains coincidental correctness (CC), which reduces the accuracy of FL. CC arises when a test case executes faulty program entities but still produces the correct output, leading to misleading FL inferences. In widely used datasets, the presence of CC compromises the reliability of passed test cases (i.e., negative labels). In contrast, failed test cases (i.e., positive labels) remain definitive. In FL scenarios, unlabeled data is typically abundant and primarily consists of passed test cases. Therefore, systematically leveraging positive and unlabeled data for accurate CC detection is essential, which is beneficial to FL. To tackle the problem, we propose a two-stagE positiVe and unlAbeled learning model for coiNcidental correctneSs detection, EVANS. EVANS defines failed test cases as positive samples and treats the remaining ones as unlabeled data. It comprises two core modules: (1) A module for selecting high-quality pseudo-negative samples. This module leverages vector distance metrics to identify high-quality pseudo-negative test cases, using inter-class distances computed via a pre-trained model. (2) A weakly supervised contrastive learning module. This module utilizes the labeled samples from Stage (1) to train a contrastive learning model, which then detects CC in unlabeled test cases. Experimental results demonstrate that EVANS significantly outperforms current CC detection methods.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | 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 10mTalk | 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 10mTalk | 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 10mTalk | 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 10mTalk | 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 10mTalk | 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 10mTalk | 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 10mTalk | 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 10mTalk | 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 | ||