11 citations · 14 across the 14 of their papers we have counts for
10 papers · 1 filter
CatchAll: Repository-Aware Exception Handling with Knowledge-Guided LLMs
Qingxiao Tao, Xiaodong Gu, Hao Zhong +1
Exception handling is a vital forward error-recovery mechanism in many programming languages, enabling developers to manage runtime anomalies through structured constructs (e.g., t…
Readability-Robust Code Summarization via Meta Curriculum Learning
Wenhao Zeng, Yitian Chai, Hao Zhou +3
Code summarization has emerged as a fundamental technique in the field of program comprehension. While code language models have shown significant advancements, the current models…
In Line with Context: Repository-Level Code Generation via Context Inlining
Chao Hu, Wenhao Zeng, Yuling Shi +2
Repository-level code generation has attracted growing attention in recent years. Unlike function-level code generation, it requires the model to understand the entire repository,…
Beyond Language Boundaries: Uncovering Programming Language Families for Code Language Models
Shangbo Yun, Xiaodong Gu, Jianghong Huang +1
The rapid proliferation of diverse programming languages presents both opportunities and challenges for developing multilingual code LLMs. While existing techniques often train cod…
Neuron-Guided Interpretation of Code LLMs: Where, Why, and How?
Zhe Yin, Xiaodong Gu, Beijun Shen
Code language models excel on code intelligence tasks, yet their internal interpretability is underexplored. Existing neuron interpretability techniques from NLP are suboptimal for…
Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs
Chen Xie, Mingsheng Jiao, Xiaodong Gu +1
While large language models (LLMs) have been widely applied to code generation, they struggle with generating entire deep learning projects, which are characterized by complex stru…
APIRAT: Integrating Multi-source API Knowledge for Enhanced Code Translation with LLMs
Chaofan Wang, Guanjie Qiu, Xiaodong Gu +1
Code translation is an essential task in software migration, multilingual development, and system refactoring. Recent advancements in large language models (LLMs) have demonstrated…
Just-In-Time Software Defect Prediction via Bi-modal Change Representation Learning
Yuze Jiang, Beijun Shen, Xiaodong Gu
For predicting software defects at an early stage, researchers have proposed just-in-time defect prediction (JIT-DP) to identify potential defects in code commits. The prevailing a…
Unraveling the Potential of Large Language Models in Code Translation: How Far Are We?
Qingxiao Tao, Tingrui Yu, Xiaodong Gu +1
While large language models (LLMs) exhibit state-of-the-art performance in various tasks, recent studies have revealed their struggle for code translation. This is because they hav…
Zero-Shot Code Representation Learning via Prompt Tuning
Nan Cui, Xiaodong Gu, Beijun Shen
Learning code representations has been the core prerequisite of many software engineering tasks such as code clone detection and code generation. State-of-the-art program represent…