ISSTA 2024
Mon 16 - Fri 20 September 2024 Vienna, Austria
co-located with ISSTA/ECOOP 2024
Thu 19 Sep 2024 15:50 - 16:10 at EI 7 - Code Transformation Chair(s): Xiaoning Du

Recent years have seen the development of LLM-based code generation. Compared to generating code in a software project, incremental code edits are empirically observed to be more frequent. The emerging code editing approaches usually formulate the problem as generating an edit based on known relevant prior edits and context. However, practical code edits can be more complicated. First, an editing session can include multiple (ir)relevant edits to the code under edit. Second, the inference of the subsequent edits is non-trivial as the scope of its ripple effect can be the whole project.

In this work, we propose CoEdPilot, an LLM-driven solution to recommend code edits by discriminating the relevant edits, exploring their interactive natures, and estimating its ripple effect in the project. Specifically, CoEdPilot orchestrates multiple neural transformers to identify what and how to edit in the project regarding both edit location and edit content. When a user accomplishes an edit with an optional editing description, an Subsequent Edit Analysis first reports the most relevant files in the project with what types of edits (e.g., keep, insert, and replace) can happen for each line of their code. Next, an Edit-content Generator generates concrete edit options for the lines of code, regarding its relevant prior changes reported by an Edit-dependency Analyzer. Last, both the Subsequent Edit Analysis and the Edit-content Generator capture relevant prior edits as feedback to readjust their recommendations. We train our models by collecting over 180K commits from 471 open-source projects in 5 programming languages. Our extensive experiments show that (1) CoEdPilot can well predict the edits (i.e., predicting edit location with accuracy of 70.8%-85.3%, and the edit content with exact match rate of 41.8% and BLEU4 score of 60.7); (2) CoEdPilot can well boost existing edit generators such as GRACE and CCT5 on exact match rate by 8.57% points and BLEU4 score by 18.08. Last, our user study on 18 participants with 3 editing tasks (1) shows that CoEdPilot can be effective in assisting users to edit code in comparison with Copilot, and (2) sheds light on the future improvement of the tool design. The video demonstration of our tool is available at https://sites.google.com/view/coedpilot/home.

Thu 19 Sep

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

15:30 - 17:10
Code TransformationTechnical Papers at EI 7
Chair(s): Xiaoning Du Monash University
15:30
20m
Talk
One-to-One or One-to-Many? Suggesting Extract Class Refactoring Opportunities with Intra-class Dependency Hypergraph Neural Network
Technical Papers
Di Cui Xidian University, Qiangqiang Wang Xidian University, Yutong Zhao University of Central Missouri, Jiaqi Wang Xidian University, Minjie Wei Xidian University, Jingzhao Hu Xidian University, Luqiao Wang Xidian University, Qingshan Li Xidian University
DOI
15:50
20m
Talk
CoEdPilot: Recommending Code Edits with Learned Prior Edit Relevance, Project-wise Awareness, and Interactive Nature
Technical Papers
Chenyan Liu Shanghai Jiao Tong University; National University of Singapore, Yufan Cai Shanghai Jiao Tong University; National University of Singapore, Yun Lin Shanghai Jiao Tong University, Yuhuan Huang Shanghai Jiao Tong University, Yunrui Pei Shanghai Jiao Tong University, Bo Jiang Bytedance Network Technology, Ping Yang Bytedance Network Technology, Jin Song Dong National University of Singapore, Hong Mei Shanghai Jiao Tong University
DOI
16:10
20m
Talk
Arfa: An Agile Regime-Based Floating-Point Optimization Approach for Rounding Errors
Technical Papers
Jinchen Xu Information Engineering University, Mengqi Cui Information Engineering University, Fei Li Information Engineering University, Zuoyan Zhang Hunan University, Hongru Yang Information Engineering University, Bei Zhou Information Engineering University, Jie Zhao Hunan University
DOI
16:30
20m
Talk
Automated Deep Learning Optimization via DSL-Based Source Code Transformation
Technical Papers
Ruixin Wang Purdue University, Minghai Lu Purdue University, Cody Hao Yu BosonAI, Yi-Hsiang Lai Amazon Web Services, Tianyi Zhang Purdue University
DOI

Information for Participants
Thu 19 Sep 2024 15:30 - 17:10 at EI 7 - Code Transformation Chair(s): Xiaoning Du
Info for room EI 7:

Map: https://tuw-maps.tuwien.ac.at/?q=CDEG13

Room tech: https://raumkatalog.tiss.tuwien.ac.at/room/15417