[Remote] xASTNN: Improved Code Representations for Industrial Practice
The application of deep learning techniques in software engineering becomes increasingly popular. One key problem is developing high-quality and easy-to-use source code representations for code-related tasks. The research community has acquired impressive results in recent years. However, due to the deployment difficulties and performance bottlenecks, seldom these approaches are applied to the industry. In this paper, we present xASTNN, an eXtreme Abstract Syntax Tree (AST)-based Neural Network for source code representation, aiming to push this technique to industrial practice. The proposed xASTNN has three advantages. First, xASTNN is completely based on widely-used ASTs and does not require complicated data pre-processing, making it applicable to various programming languages and practical scenarios. Second, three closely-related designs are proposed to guarantee the effectiveness of xASTNN, including statement subtree sequence for code naturalness, gated recursive unit for syntactical information, and gated recurrent unit for sequential information. Third, a dynamic batching algorithm is introduced to significantly reduce the time complexity of xASTNN. Two code comprehension downstream tasks, code classification and code clone detection, are adopted for evaluation. The results demonstrate that our xASTNN can improve the state-of-the-art while being faster than the baselines.
Tue 5 DecDisplayed time zone: Pacific Time (US & Canada) change
16:00 - 18:00 | Code Search and Text to CodeResearch Papers / Industry Papers / Journal First / Demonstrations at Golden Gate A Chair(s): Miryung Kim University of California at Los Angeles, USA | ||
16:00 15mTalk | [Remote] Self-Supervised Query Reformulation for Code Search Research Papers Yuetian Mao Shanghai Jiao Tong University, Chengcheng Wan East China Normal University, Yuze Jiang Shanghai Jiao Tong University, Xiaodong Gu Shanghai Jiao Tong University Media Attached | ||
16:15 15mTalk | [Remote] Natural Language to Code: How Far are We? Research Papers Shangwen Wang National University of Defense Technology, Mingyang Geng National University of Defense Technology, Bo Lin National University of Defense Technology, Zhensu Sun Singapore Management University, Ming Wen Huazhong University of Science and Technology, Yepang Liu Southern University of Science and Technology, Li Li Beihang University, Tegawendé F. Bissyandé University of Luxembourg, Xiaoguang Mao National University of Defense Technology DOI Pre-print Media Attached | ||
16:30 15mTalk | [Remote] xASTNN: Improved Code Representations for Industrial Practice Industry Papers Zhiwei Xu Tsinghua University, Min Zhou Tsinghua University, Xibin Zhao Tsinghua University, Yang Chen Huazhong University of Science and Technology, Xi Cheng VMware, Hongyu Zhang Chongqing University DOI Media Attached | ||
16:45 7mTalk | [Remote] On the Dual Nature of Necessity in Use of Rust Unsafe Code Industry Papers Yuchen Zhang New York University, USA, Ashish Kundu Cisco Research, Georgios Portokalidis Stevens Institute of Technology, Jun Xu The University of Utah DOI Media Attached | ||
16:53 7mTalk | On Using Information Retrieval to Recommend Machine Learning Good Practices for Software Engineers Demonstrations Laura Cabra-Acela Universidad de Los Andes, Anamaria Mojica-Hanke University of Passau, Universidad de Los Andes, Mario Linares-Vásquez Universidad de los Andes, Steffen Herbold University of Passau Media Attached | ||
17:00 15mTalk | MultiPL-E: A Scalable and Polyglot Approach to Benchmarking Neural Code Generation Journal First Federico Cassano Northeastern University, John Gouwar Northeastern University, Daniel Nguyen Hannover High School, Sydney Nguyen Wellesley College, Luna Phipps-Costin Northeastern University, Donald Pinckney Northeastern University, Ming-Ho Yee Northeastern University, Yangtian Zi Northeastern University, Carolyn Jane Anderson Wellesley College, Molly Q Feldman Oberlin College, Arjun Guha Northeastern University and Roblox, Michael Greenberg Stevens Institute of Technology, Abhinav Jangda Microsoft Research Link to publication Media Attached | ||
17:15 15mTalk | NCQ: Code reuse support for Node.js developers Journal First Brittany Reid The University of Adelaide, Marcelo d'Amorim North Carolina State University, Markus Wagner Monash University, Australia, Christoph Treude University of Melbourne Link to publication DOI Pre-print Media Attached | ||
17:30 15mTalk | Efficient Text-to-Code Retrieval with Cascaded Fast and Slow Transformer Models Research Papers Akhilesh Deepak Gotmare Salesforce Research, Junnan Li Salesforce Research, Shafiq Joty Salesforce Research, Steven C.H. Hoi Salesforce Research Asia Media Attached | ||
17:45 15mTalk | PEM: Representing Binary Program Semantics for Similarity Analysis via A Probabilistic Execution Model Research Papers Xiangzhe Xu Purdue University, Zhou Xuan , Shiwei Feng Purdue University, Siyuan Cheng Purdue University, Yapeng Ye Purdue University, Qingkai Shi The Hong Kong University of Science and Technology, Guanhong Tao Purdue University, Le Yu , Zhuo Zhang Purdue University, Xiangyu Zhang Purdue University Media Attached | ||