15 citations · 41 across the 9 of their papers we have counts for
10 papers
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…
UNICS: Multilingual Code Search via Unified Pseudocode and Contrastive Transfer Learning
Ye Fan, Jidong Ge, Chuanyi Li +2
While pre-trained models have achieved remarkable success in code search, their multilingual capabilities remain a major hurdle, plagued by data imbalance, cross-lingual semantic i…
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,…
Refactoring Bug-Inducing: Improving Defect Prediction with Code Change Tactics Analysis
Feifei Niu, Junqian Shao, Christoph Mayr-Dorn +5
Just-in-time defect prediction (JIT-DP) aims to predict the likelihood of code changes resulting in software defects at an early stage. Although code change metrics and semantic fe…
A Sentence-Level Approach to Understanding Software Vulnerability Fixes
Amiao Gao, Zenong Zhang, Simin Wang +3
Understanding software vulnerabilities and their resolutions is crucial for securing modern software systems. This study presents a novel traceability model that links a pair of se…
A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks
Wentao Zou, Qi Li, Jidong Ge +4
Pre-trained models (PTMs) have achieved great success in various Software Engineering (SE) downstream tasks following the ``pre-train then fine-tune'' paradigm. As fully fine-tunin…
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…
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Jidong Ge, Yuxiang Liu, Jie Gui +5
Self-supervised learning enables networks to learn discriminative features from massive data itself. Most state-of-the-art methods maximize the similarity between two augmentations…
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…