From Sparse to Structured: A Diffusion-Enhanced and Feature-Aligned Framework for Coincidental Correctness Detection
Coincidental correctness (CC) refers to test cases that execute faulty code but still produce excepted outputs. This phenomenon introduces noise into the data of software testing-related tasks. As demonstrated in the literature, CC has negative impact on test suite reduction, test case prioritization, fault localization, and automated program repair. Thus, it is essential to detect and mitigate the impact of CC. Although CC is commonly observed across a large number of programs, CC test cases are typically sparse within each program’s test suite. In other words, CC test cases generally make up merely a small portion of the passing test cases. The proportions vary from 3.27% to 31.74% within Defects4J V1.4. This results in a highly imbalanced distribution of CC versus non-CC test cases, posing challenges for accurate detection. To address this issue, we propose a Diffusion-Enhanced and Feature-Aligned Framework for Coincidental Correctness detection, named DEFACC, to obtain more structured representations of test cases. Specifically, DEFACC first introduces a diffusion- based generation module. This module generates new CC samples from original samples to alleviate class imbalance issue and enhance the diversity of CC samples. However, generated feature samples may deviate from the distribution of real CC samples. Such shifts can hurt model reliability and generalization. To resolve this, DEFACC integrates a feature alignment module that is founded on the Maximum Mean Discrepancy (MMD) loss. This module enforces distributional consistency between generated and original CC samples during training. Together, these components ensure that the augmented samples are from sparse to structured, which is not only quantitatively balanced but also semantically faithful. Experimental results show that the DEFACC significantly improves the performance existing CC detection methods and provides a stronger representation foundation for accurate fault localization.
Tue 18 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | Learning Project-wise Subsequent Code Edits via Interleaving Neural-based Induction and Tool-based Deduction Research Papers Chenyan Liu Shanghai Jiao Tong University; National University of Singapore, Yun Lin Shanghai Jiao Tong University, Yuhuan Huang Shanghai Jiao Tong University, Jiaxin Chang Shanghai Jiao Tong University, Binhang Qi National University of Singapore, Bo Jiang Bytedance Network Technology, Zhiyong Huang National University of Singapore, Jin Song Dong National University of Singapore | ||
11:10 10mTalk | Coding-Fuse: Efficient Fusion of Code Pre‑Trained Models for Classification Tasks Research Papers Yu Zhao , Lina Gong Nanjing University of Aeronautics and Astronautic, Zhiqiu Huang Nanjing University of Aeronautics and Astronautics, Yuchen Jin Nanjing University of Aeronautics and Astronautics, Mingqiang Wei Nanjing University of Aeronautics and Astronautics | ||
11:20 10mTalk | SE-Jury: An LLM-as-Ensemble-Judge Metric for Narrowing the Gap with Human Evaluation in SE Research Papers Xin Zhou Singapore Management University, Singapore, Kisub Kim DGIST, Ting Zhang Monash University, Martin Weyssow Singapore Management University, Luis F. Gomes Carnegie Mellon University, Guang Yang , Kui Liu Huawei, Xin Xia Zhejiang University, David Lo Singapore Management University | ||
11:30 10mTalk | iKnow: an Intent-Guided Chatbot for Cloud Operations with Retrieval-Augmented Generation Research Papers Junjie Huang The Chinese University of Hong Kong, Yuedong Zhong Sun Yat-sen University, Guangba Yu The Chinese University of Hong Kong, Zhihan Jiang The Chinese University of Hong Kong, Minzhi Yan HCC Lab, Huawei Cloud Computing Technology Co., Ltd, Wenfei Luan HCC Lab, Huawei Cloud Computing Technology Co., Ltd, Tianyu Yang HCC Lab, Huawei Cloud Computing Technology Co., Ltd, Rui Ren Computing and Networking Innovation Lab, Huawei Cloud Computing Technology Co., Ltd, Michael Lyu The Chinese University of Hong Kong | ||
11:40 10mTalk | Aligning LLMs to Fully Utilize the Cross-file Context in Repository-level Code Completion Research Papers Jia Li Tsinghua University, Hao Zhu Peking University, Huanyu Liu , Xianjie Shi Peking University, He Zong aiXcoder, Yihong Dong Peking University, Kechi Zhang Peking University, China, Siyuan Jiang , Zhi Jin Peking University, Ge Li Peking University | ||
11:50 10mTalk | From Sparse to Structured: A Diffusion-Enhanced and Feature-Aligned Framework for Coincidental Correctness Detection Research Papers Huan Xie Chongqing University, Chunyan Liu Chongqing University, Yan Lei Chongqing University, Zhenyu Wu School of Big Data & Software Engineering, Chongqing University, Jinping Wang Chonqing University | ||
12:00 10mTalk | Watson: A Cognitive Observability Framework for the Reasoning of LLM-Powered Agents Research Papers Benjamin Rombaut Centre for Software Excellence, Huawei Canada, Sogol Masoumzadeh Mcgill University, Kirill Vasilevski Huawei Canada, Dayi Lin Centre for Software Excellence, Huawei Canada, Ahmed E. Hassan Queen’s University | ||
12:10 10mTalk | Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories Research Papers Islem BOUZENIA University of Stuttgart, Michael Pradel CISPA Helmholtz Center for Information Security Pre-print | ||
12:20 10mTalk | Triangle: Empowering Incident Triage with Multi-Agent Research Papers Zhaoyang Yu Tsinghua University, Aoyang Fang Chinese University of Hong Kong, Shenzhen, Minghua Ma Microsoft, Jaskaran Singh Walia Microsoft, Chaoyun Zhang Microsoft, Shu Chi Tsinghua University, Ze Li Microsoft Azure, Murali Chintalapati Microsoft Azure, Xuchao Zhang Microsoft, Rujia Wang Microsoft, Chetan Bansal Microsoft Research, Saravan Rajmohan Microsoft, Qingwei Lin Microsoft, Shenglin Zhang Nankai University, Dan Pei Tsinghua University, Pinjia He Chinese University of Hong Kong, Shenzhen | ||