26 citations · 107 across the 14 of their papers we have counts for
8 papers · 1 filter
PassSum: Leveraging Paths of Abstract Syntax Trees and Self-Supervision for Code Summarization
Changan Niu, Chuanyi Li, Vincent Ng +3
Code summarization is to provide a high-level comment for a code snippet that typically describes the function and intent of the given code. Recent years have seen the successful a…
An Extensive Replication Study of the ABLoTS Approach for Bug Localization
Feifei Niu, Enshuo Zhang, Christoph Mayr-Dorn +5
Bug localization is the task of recommending source code locations (typically files) that contain the cause of a bug and hence need to be changed to fix the bug. Along these lines,…
Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases
Ze Tang, Jidong Ge, Shangqing Liu +4
Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completi…
Practical Program Repair via Preference-based Ensemble Strategy
Wenkang Zhong, Chuanyi Li, Kui Liu +5
To date, over 40 Automated Program Repair (APR) tools have been designed with varying bug-fixing strategies, which have been demonstrated to have complementary performance in terms…
An Empirical Comparison of Pre-Trained Models of Source Code
Changan Niu, Chuanyi Li, Vincent Ng +3
While a large number of pre-trained models of source code have been successfully developed and applied to a variety of software engineering (SE) tasks in recent years, our understa…
Neural Program Repair: Systems, Challenges and Solutions
Wenkang Zhong, Chuanyi Li, Jidong Ge +1
Automated Program Repair (APR) aims to automatically fix bugs in the source code. Recently, as advances in Deep Learning (DL) field, there is a rise of Neural Program Repair (NPR)…
SPT-Code: Sequence-to-Sequence Pre-Training for Learning Source Code Representations
Changan Niu, Chuanyi Li, Vincent Ng +3
Recent years have seen the successful application of large pre-trained models to code representation learning, resulting in substantial improvements on many code-related downstream…
Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?
Simin Wang, Liguo Huang, Jidong Ge +5
Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Machine Learning (ML)/Deep Learning (DL) and Softw…