CMDeSum: A Cross-Modal Deliberation Network for Code Summarization
The task of Code Summarization aims to generate concise natural language descriptions for given source code snippets, thereby assisting developers in reducing the cognitive load of comprehending the code. Existing learning-based models, employing single-pass encoder-decoder frameworks, are unable to harness global information to optimize local content, while those utilizing multi-pass encoder-decoder architectures fail to consider the structural information of the code. To tackle this issue, we propose CMDeSum, a novel deliberation framework that injects cross-modal information in a staged manner, aiming to better balance the code sequence information and Abstract Syntax Tree (AST) structural information. Specifically, we first retrieve comments from code segments similar to the given one as drafts and extract method names and ASTs from the code. Then, in the First-Pass stage, we utilize code sequences and method names to generate initial comments and refine them based on the drafts. In the Second-Pass stage, building upon the results from the First-Pass stage, we utilize additional AST information to modify the comments, producing the final comments. To evaluate our approach, we conducted experiments on existing Java and Python datasets. The experimental results indicate that compared with the state-of-the-art models for code summarization generation, our model has improved by at least 6.3%, 3.0%, and 5.8% in BLEU, ROUGE-L, and METEOR.
Sun 27 AprDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | Summarisation, Natural Language GenerationResearch Track / Early Research Achievements (ERA) / Replications and Negative Results (RENE) at 205 Chair(s): Oscar Chaparro William & Mary, Coen De Roover Vrije Universiteit Brussel, Gema Rodríguez-Pérez Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Okanagan Campus | ||
16:00 10mTalk | Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough? Research Track Antonio Vitale Politecnico di Torino, University of Molise, Antonio Mastropaolo William and Mary, USA, Rocco Oliveto University of Molise, Massimiliano Di Penta University of Sannio, Italy, Simone Scalabrino University of Molise | ||
16:10 10mTalk | CMDeSum: A Cross-Modal Deliberation Network for Code Summarization Research Track Zhifang Liao Central South University, Xiaoyu Liu Central South University, Peng Lan School of Computer Science and Engineering, Central South University, Changsha, China, Song Yu Central South University, Pei Liu Monash University | ||
16:20 10mTalk | CLCoSum: Curriculum Learning-based Code Summarization for Code Language Models Research Track Hongkui He South China University of Technology, Jiexin Wang South China University of Technology, Liuwen Cao South China University of Technology, Yi Cai School of Software Engineering, South China University of Technology, Guangzhou, China | ||
16:30 10mTalk | DLCoG: A Novel Framework for Dual-Level Code Comment Generation based on Semantic Segmentation and In-Context Learning Research Track Zhang Zhiyang , Haiyang Yang School of Computer Science and Engineering, Central South University, Qingyang Yan Central South University, Hao Yan Central South University, Wei-Huan Min Central South University, Zhao Wei Tencent, Li Kuang Central South University, Yingjie Xia Hangzhou Dianzi University | ||
16:40 10mTalk | Explaining GitHub Actions Failures with Large Language Models: Challenges, Insights, and Limitations Research Track Pablo Valenzuela-Toledo University of Bern, Universidad de La Frontera, Chuyue Wu University of Bern, Sandro Hernández University of Bern, Alexander Boll University of Bern, Roman Machacek University of Bern, Sebastiano Panichella University of Bern, Timo Kehrer University of Bern | ||
16:50 10mTalk | Large Language Models are Qualified Benchmark Builders: Rebuilding Pre-Training Datasets for Advancing Code Intelligence Tasks Research Track Kang Yang National University of Defense Technology, Xinjun Mao National University of Defense Technology, Shangwen Wang National University of Defense Technology, Yanlin Wang Sun Yat-sen University, Tanghaoran Zhang National University of Defense Technology, Yihao Qin National University of Defense Technology, Bo Lin National University of Defense Technology, Zhang Zhang Key Laboratory of Software Engineering for Complex Systems, National University of Defense Technology, Yao Lu National University of Defense Technology, Kamal Al-Sabahi College of Banking and Financial Studies Pre-print | ||
17:00 10mTalk | Extracting Formal Specifications from Documents Using LLMs for Test Automation Research Track Hui Li Xiamen University, Zhen Dong Fudan University, Siao Wang Fudan University, Hui Zhang Fudan University, Liwei Shen Fudan University, Xin Peng Fudan University, Dongdong She HKUST (The Hong Kong University of Science and Technology) | ||
17:10 6mTalk | Using Large Language Models to Generate Concise and Understandable Test Case Summaries Early Research Achievements (ERA) Natanael Djajadi Delft University of Technology, Amirhossein Deljouyi Delft University of Technology, Andy Zaidman TU Delft Pre-print | ||
17:16 6mTalk | Towards Generating the Rationale for Code Changes Replications and Negative Results (RENE) Francesco Casillo Università di Salerno, Antonio Mastropaolo William and Mary, USA, Gabriele Bavota Software Institute @ Università della Svizzera Italiana, Vincenzo Deufemia University of Salerno, Carmine Gravino University of Salerno | ||
17:22 8mTalk | Session's Discussion: "Summarisation, Natural Language Generation" Research Track | ||