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Fri 2 May 2025 11:30 - 11:45 at Canada Hall 1 and 2 - AI for SE 3 Chair(s): Ying Zou

Code retrieval, which retrieves code snippets based on users’ natural language descriptions, is widely used by developers and plays a pivotal role in real-world software development. The advent of deep learning has shifted the retrieval paradigm from lexical-based matching towards leveraging deep learning models to encode source code and queries into vector representations, facilitating code retrieval according to vector similarity. Despite the effectiveness of these models, managing large-scale code bases presents significant challenges. Previous research propose deep hashing-based methods, which generate hash codes for queries and code snippets and use Hamming distance for rapid recall of code candidates. However, this approach’s reliance on linear scanning of the entire code base limits its scalability. To further improve the efficiency of large scale code retrieval, we propose a novel approach SECRET (Scalable and Efficient Code Retrieval via SegmEnTed deep hashing). SECRET converts long hash codes calculated by existing deep hashing approaches into several short hash code segments through an iterative training strategy. After training, SECRET recalls code candidates by looking up the hash tables for each segment, the time complexity of recall can thus be greatly reduced. Extensive experimental results demonstrate that SECRET can drastically reduce the retrieval time by at least 95% while achieving comparable or even higher performance of existing deep hashing approaches. Besides, SECRET also exhibits superior performance and efficiency compared to the classical hash table-based approach known as LSH under the same number of hash tables.

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
Link to publication
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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