Coding-Fuse: Efficient Fusion of Code Pre‑Trained Models for Classification Tasks
This program is tentative and subject to change.
Software engineering (SE) classification tasks play a vital role in improving software quality. Nevertheless, SE researchers and practitioners tend to rely on a single code pre-trained model (PTM) for downstream classification tasks. Previous studies have found that different code PTMs yield different performance in SE classification tasks, which triggers our thinking of whether the integration of multiple code PTMs improves the performance of classification tasks. Therefore, we first conduct preliminary exploratory research to analyze the impact of fusing multiple PTMs on code classification tasks. The result shows that compared to the single code PTM, the fusion of multiple code PTMs can significantly improve the performance of SE classification tasks. However, the performance improvement also brings about the problem of increased finetuning resources and reduced reasoning efficiency, which does not meet the greenness requirements. In order to address these issues, we propose Coding-Fuse, a framework of efficient fusion of code PTMs for SE classification tasks. Coding-Fuse first introduces evidence theory to evaluate the adaptability of the output features of each layer of code PTMs and data labels, and locates the potential best performance layer of different code PTMs. Then, Coding-Fuse uses a soft voting strategy to fuse the outputs of these layers to obtain a new model. We conduct experiments for effectiveness by comparing Coding-Fuse with the full PTM fusion method and the original single PTM using five different code PTMs on three different SE classification tasks and two task scenarios. The results show that Coding-Fuse can achieve better performance than the full PTM fusion method with higher efficiency and fewer hardware resources, and can achieve better performance than the original single PTM at the same efficiency and hardware resource level. We encourage SE practitioners to use our Coding-Fuse method in practice to fully utilize the advantages of each code PTM in the PTM repository according to task requirements to easily create new SE intelligent PTMs to achieve performance and greenness improvements.
This program is tentative and subject to change.
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 | ||
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 | ||