ICPC 2024
Sun 14 - Sat 20 April 2024 Lisbon, Portugal
co-located with ICSE 2024

Code completion is an important feature in Integrated Development Environments (IDEs). These years, researchers have been making efforts for intelligent code completion. However, existing work on intelligent code completion either only considered production code, or did not distinguish between production code and test code. It is unclear how effective existing completion models are for test code completion, nor whether we can further improve it. In this work, we focus on the completion of test code. We first find through experiments that completion models for production code are sub-optimal for test code completion. Then we analyze the specific characteristics of test code, and observe that test code has inter- and intra-project similarities, and a strong relationship with its focal class and other production classes depending on the focal class (i.e., focal-related code). By incorporating test code from other projects to fine-tune existing models, we leverage the inter-project similarity of test code to improve the completion of tokens specific to test code. By introducing a local component and constructing existing test code as well as the focal-related code in the project as references, we enhance existing code completion models with the intra-project similarity and the focal-related code of test code. Experiments show that each characteristic of test code we exploit can bring substantial improvement to test code completion and our integrated framework outperforms other baseline frameworks. Compared to the base completion model, on token-level completion, our optimal model for test code completion relatively improves all-token and identifier completion accuracy by 7.68% and 19.96%, respectively; on line-level completion, it relatively improves editdistance similarity and exact-match metrics by 8.89% and 22.82%, respectively. Moreover, we perform error analysis and point out potential directions for future work.

Mon 15 Apr

Displayed time zone: Lisbon change

14:00 - 15:30
14:00
10m
Talk
MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment GenerationICPCICPC Full paper
Research Track
Xinglu Pan Peking University, Chenxiao Liu Peking University, Yanzhen Zou Peking University, Tao Xie Peking University, Bing Xie Peking University
Pre-print
14:10
10m
Talk
Compositional API Recommendation for Library-Oriented Code GenerationICPCICPC Full paper
Research Track
Zexiong Ma Peking University, Shengnan An Xi’an Jiaotong University, Bing Xie Peking University, Zeqi Lin Microsoft Research, China
Pre-print
14:20
10m
Talk
On the Generalizability of Deep Learning-based Code Completion Across Programming Language VersionsICPCICPC Full paper
Research Track
Matteo Ciniselli Università della Svizzera Italiana, Alberto Martin-Lopez Software Institute - USI, Lugano, Gabriele Bavota Software Institute @ Università della Svizzera Italiana
14:30
10m
Talk
ESGen: Commit Message Generation Based on Edit Sequence of Code ChangeICPCICPC Full paperVirtual-Talk
Research Track
Xiangping Chen Sun Yat-sen University, Yangzi Li SUN YAT-SEN UNIVERSITY, Zhicao Tang SUN YAT-SEN UNIVERSITY, Yuan Huang School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China, Haojie Zhou School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China, Mingdong Tang Guangdong University of Foreign Studies, Zibin Zheng Sun Yat-sen University
14:40
10m
Talk
Improving AST-Level Code Completion with Graph Retrieval and Multi-Field AttentionICPCICPC Full paperVirtual-Talk
Research Track
Yu Xia Central South University, Tian Liang Central South University, Wei-Huan Min Central South University, Li Kuang School of Computer Science and Engineering, Central South University
14:50
10m
Talk
Exploring and Improving Code Completion for Test CodeICPCICPC Full paper
Research Track
Tingwei Zhu Nanjing University, Zhongxin Liu Zhejiang University, Tongtong Xu Huawei, Ze Tang Software Institute, Nanjing University, Tian Zhang Nanjing University, Minxue Pan Nanjing University, Xin Xia Huawei Technologies
15:00
10m
Talk
Understanding the Impact of Branch Edit Features for the Automatic Prediction of Merge Conflict ResolutionsICPCICPC RENE Paper
Replications and Negative Results (RENE)
Waad riadh aldndni Virginia Tech, Francisco Servant ITIS Software, University of Malaga, Na Meng Virginia Tech
15:10
4m
Talk
Investigating the Efficacy of Large Language Models for Code Clone DetectionICPCICPC ERA Paper
Early Research Achievements (ERA)
Mohamad Khajezade University of British Columbia Okanagan, Jie JW Wu University of British Columbia (UBC), Fatemeh Hendijani Fard University of British Columbia, Gema Rodríguez-Pérez University of British Columbia (UBC), Mohamed S Shehata University of British Columbia
15:14
16m
Talk
Code + Documentation Generation: Panel with SpeakersICPC
Discussion

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