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This program is tentative and subject to change.

Fri 2 May 2025 11:45 - 12:00 at Canada Hall 1 and 2 - AI for SE 3

Deep learning-based code generation has completely transformed the way developers write programs today. Existing approaches to code generation have focused either on the Sequence-to-Sequence paradigm, which generates target code as a sequence of tokens, or the Sequence-to-Tree paradigm, which outputs code as a sequence of actions. While these two paradigms are intuitively complementary, their combination has not been previously explored. By comparing the code generated under these two paradigms, we find that integrating them holds significant potential. In this paper, we propose UniGenCoder for code-related generation tasks, which consists of a shared encoder, a shared decoder with a minimal set of additional parameters to unify two paradigms, and a selector that dynamically chooses optimal paradigm for each instance. Also, during the model training, we first perform the multi-task learning and distillation strategies to facilitate knowledge transfer between two paradigms, and then leverage contrastive learning to train the selector. Experimental results on the text-to-code and code-to-code generation tasks demonstrate the effectiveness of our proposed model. We will release our code upon acceptance.

This program is tentative and subject to change.

Fri 2 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
11:00
15m
Talk
A First Look at Conventional Commits Classification
Research Track
Qunhong Zeng Beijing Institute of Technology, Yuxia Zhang Beijing Institute of Technology, Zhiqing Qiu Beijing Institute of Technology, Hui Liu Beijing Institute of Technology
11:15
15m
Talk
ChatGPT-Based Test Generation for Refactoring Engines Enhanced by Feature Analysis on Examples
Research Track
Chunhao Dong Beijing Institute of Technology, Yanjie Jiang Peking University, Yuxia Zhang Beijing Institute of Technology, Yang Zhang Hebei University of Science and Technology, Hui Liu Beijing Institute of Technology
11:30
15m
Talk
SECRET: Towards Scalable and Efficient Code Retrieval via Segmented Deep Hashing
Research Track
Wenchao Gu The Chinese University of Hong Kong, Ensheng Shi Xi’an Jiaotong University, Yanlin Wang Sun Yat-sen University, Lun Du Microsoft Research, Shi Han Microsoft Research, Hongyu Zhang Chongqing University, Dongmei Zhang Microsoft Research, Michael Lyu The Chinese University of Hong Kong
11:45
15m
Talk
UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation
New Ideas and Emerging Results (NIER)
Liangying Shao School of Informatics, Xiamen University, China, Yanfu Yan William & Mary, Denys Poshyvanyk William & Mary, Jinsong Su School of Informatics, Xiamen University, China
12:00
15m
Talk
How is Google using AI for internal code migrations?
SE In Practice (SEIP)
Stoyan Nikolov Google, Inc., Daniele Codecasa Google, Inc., Anna Sjovall Google, Inc., Maxim Tabachnyk Google, Siddharth Taneja Google, Inc., Celal Ziftci Google, Satish Chandra Google, Inc
12:15
7m
Talk
LLM-Based Test-Driven Interactive Code Generation: User Study and Empirical Evaluation
Journal-first Papers
Sarah Fakhoury Microsoft Research, Aaditya Naik University of Pennsylvania, Georgios Sakkas University of California at San Diego, Saikat Chakraborty Microsoft Research, Shuvendu K. Lahiri Microsoft Research
12:22
7m
Talk
The impact of Concept drift and Data leakage on Log Level Prediction Models
Journal-first Papers
Youssef Esseddiq Ouatiti Queen's university, Mohammed Sayagh ETS Montreal, University of Quebec, Noureddine Kerzazi Ensias-Rabat, Bram Adams Queen's University, Ahmed E. Hassan Queen’s University, Youssef Esseddiq Ouatiti Queen's university
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