11 citations · 16 across the 20 of their papers we have counts for
22 papers
Translator vs. Challenger: Adversarial Agentic Learning for C-to-Rust Translation
Chaofan Wang, Xiaodong Gu, Yuling Shi +2
C-to-Rust translation remains challenging due to the substantial semantic gap between the two languages. Recent experience-enhanced LLM translators improve translation quality by l…
DeepRepoQA: Code Repository Question Answering with Deep Agent Exploration
Weihan Peng, Yuling Shi, Yingwei Ma +3
Answering developer questions about a software repository is a critical yet under-explored problem in software engineering. While existing repository understanding methods have adv…
ParaTempo: Efficient Parallel Reasoning via Temporal Confidence
Xuteng Zhang, Wenhao Zeng, Xiaodong Gu +5
Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows with reasoning depth and br…
Know Before Fix: QA-Driven Repository Knowledge Acquisition for Software Issue Resolution
Haotian Lin, Silin Chen, Xiaodong Gu +8
LLM-based coding agents have significantly advanced automated software issue resolution, yet they remain highly prone to factual errors caused by insufficient repository understand…
Test vs Mutant: Adversarial LLM Agents for Robust Unit Test Generation
Pengyu Chang, Yixiong Fang, Silin Chen +3
Software testing is a critical, yet resource-intensive phase of the software development lifecycle. Over the years, various automated tools have been developed to aid in this proce…
Rethinking Code Complexity Through the Lens of Large Language Models
Chen Xie, Xiaodong Gu, Yuling Shi +1
Code complexity metrics such as cyclomatic complexity have long been used to assess software quality and maintainability. With the rapid advancement of large language models (LLMs)…
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…
GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts
Wenhao Zeng, Xuteng Zhang, Yuling Shi +4
Large Reasoning Models (LRMs) achieve remarkable performance by explicitly generating multi-step chains of thought, but this capability incurs substantial inference latency and com…
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…
LongCodeZip: Compress Long Context for Code Language Models
Yuling Shi, Yichun Qian, Hongyu Zhang +2
Code generation under long contexts is becoming increasingly critical as Large Language Models (LLMs) are required to reason over extensive information in the codebase. While recen…
Transplant Then Regenerate: A New Paradigm for Text Data Augmentation
Guangzhan Wang, Hongyu Zhang, Beijun Shen +1
Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variatio…
ALT4Decompile: Inferring C-aligned Abstract Loop Tree for LLM-Based Binary Decompilation
Yongpan Wang, Puzhuo Liu, Xin Xu +4
Decompilation refers to the process of recovering high-level (C) language code from low-level (assembly) code. Recent Large Language Model (LLM)-based methods can generate re-execu…
SWE-QA: Can Language Models Answer Repository-level Code Questions?
Weihan Peng, Yuling Shi, Yuhang Wang +3
Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and Co…
EvoC2Rust: A Skeleton-guided Framework for Project-Level C-to-Rust Translation
Chaofan Wang, Tingrui Yu, Beijun Shen +6
Translating legacy C codebases to Rust is increasingly demanded for building safety-critical systems. While various approaches have emerged for this task, they face inherent trade-…
Anti-adversarial Learning: Desensitizing Prompts for Large Language Models
Xuan Li, Zhe Yin, Xiaodong Gu +1
With the widespread use of LLMs, preserving privacy in user prompts has become crucial, as prompts risk exposing privacy and sensitive data to the cloud LLMs. Traditional technique…
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…