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Fri 2 May 2025 11:00 - 11:15 at Canada Hall 1 and 2 - AI for SE 3 Chair(s): Ying Zou

Modern distributed software development relies on commits to control system versions. Commit classification plays a vital role in both industry and academia. The widely-used commit classification framework was proposed in 1976 by Swanson and includes three base classes: perfective, corrective, and adaptive. With the increasing complexity of software development, the industry has shifted towards a more fine-grained commit category, i.e., adopting Conventional Commits Specification (CCS) for delicacy management. The new commit framework requires developers to classify commits into ten distinct categories, such as feat'',fix'', and ``docs''. However, existing studies mainly focus on the three-category classification, leaving the definition and application of the fine-grained commit categories as knowledge gaps. This paper reports a preliminary study on this mechanism from its application status and problems. We also explore ways to address these identified problems. We find that a growing number of projects on GitHub are adopting CCS. By analyzing 194 issues from GitHub and 100 questions from Stack Overflow about the CCS application, we qualitatively categorized 52 challenges developers encountered. The most common one is CCS-type confusion. To address these challenges, we propose a clear definition of CCS types based on existing variants. Further, we designed an approach to automatically classify commits into CCS types, and the evaluation results demonstrate a promising performance. Our work facilitates a deeper comprehension of the present fine-grained commit categorization and holds the potential to alleviate application challenges significantly.

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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