87 citations · 168 across the 51 of their papers we have counts for
18 papers · 1 filter
On the Effectiveness of Training Data Optimization for LLM-based Code Generation: An Empirical Study
Shiqi Kuang, Zhao Tian, Tao Xiao +2
Large language models (LLMs) have achieved remarkable progress in code generation, largely driven by the availability of high-quality code datasets for effective training. To furth…
A Comprehensive Study of Deep Learning Model Fixing Approaches
Hanmo You, Zan Wang, Zishuo Dong +3
Deep Learning (DL) has been widely adopted in diverse industrial domains, including autonomous driving, intelligent healthcare, and aided programming. Like traditional software, DL…
Optimization-Aware Test Generation for Deep Learning Compilers
Qingchao Shen, Zan Wang, Haoyang Ma +5
Deep Learning (DL) compilers have been widely utilized to optimize DL models for efficient deployment across various hardware. Due to their vital role in the DL ecosystem, ensuring…
Testing Question Answering Software with Context-Driven Question Generation
Shuang Liu, Zhirun Zhang, Jinhao Dong +5
Question-answering software is becoming increasingly integrated into our daily lives, with prominent examples including Apple Siri and Amazon Alexa. Ensuring the quality of such sy…
Issue-Oriented Agent-Based Framework for Automated Review Comment Generation
Shuochuan Li, Dong Wang, Patanamon Thongtanunam +3
Code review (CR) is a crucial practice for ensuring software quality. Various automated review comment generation techniques have been proposed to streamline the labor-intensive pr…
Optimizing Knowledge Utilization for Multi-Intent Comment Generation with Large Language Models
Shuochuan Li, Zan Wang, Xiaoning Du +3
Code comment generation aims to produce a generic overview of a code snippet, helping developers understand and maintain code. However, generic summaries alone are insufficient to…
Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR
Zeyu Sun, Jingjing Liang, Weiyi Wang +3
MLIR (Multi-Level Intermediate Representation) has rapidly become a foundational technology for modern compiler frameworks, enabling extensibility across diverse domains. However,…
Clarifying Semantics of In-Context Examples for Unit Test Generation
Chen Yang, Lin Yang, Ziqi Wang +3
Recent advances in large language models (LLMs) have enabled promising performance in unit test generation through in-context learning (ICL). However, the quality of in-context exa…
Aligning Requirement for Large Language Model's Code Generation
Zhao Tian, Junjie Chen
Code generation refers to the automatic generation of source code based on a given programming specification, which has garnered significant attention particularly with the advance…
On the Evaluation of Large Language Models in Multilingual Vulnerability Repair
Dong wang, Junji Yu, Honglin Shu +4
Various Deep Learning-based approaches with pre-trained language models have been proposed for automatically repairing software vulnerabilities. However, these approaches are limit…
Trae Agent: An LLM-based Agent for Software Engineering with Test-time Scaling
Trae Research Team, Pengfei Gao, Zhao Tian +12
Software issue resolution is a critical challenge in software engineering and has garnered increasing attention in recent years. With the rapid advancement of large language models…
A Survey of Reinforcement Learning for Software Engineering
Dong Wang, Hanmo You, Lingwei Zhu +6
Reinforcement Learning (RL) has emerged as a powerful paradigm for sequential decision-making and has attracted growing interest across various domains, particularly following the…
Reflective Unit Test Generation for Precise Type Error Detection with Large Language Models
Chen Yang, Ziqi Wang, Yanjie Jiang +4
Type errors in Python often lead to runtime failures, posing significant challenges to software reliability and developer productivity. Existing static analysis tools aim to detect…
Improving Compiler Bug Isolation by Leveraging Large Language Models
Yixian Qi, Jiajun Jiang, Fengjie Li +3
Compilers play a foundational role in building reliable software systems, and bugs within them can lead to catastrophic consequences. The compilation process typically involves hun…
Evaluating Large Language Models for Multilingual Vulnerability Detection at Dual Granularities
Honglin Shu, Michael Fu, Junji Yu +4
Various deep learning-based approaches utilizing pre-trained language models (PLMs) have been proposed for automated vulnerability detection. With recent advancements in large lang…
A Preliminary Study of Large Language Models for Multilingual Vulnerability Detection
Junji Yu, Honglin Shu, Michael Fu +4
Deep learning-based approaches, particularly those leveraging pre-trained language models (PLMs), have shown promise in automated software vulnerability detection. However, existin…
DESIL: Detecting Silent Bugs in MLIR Compiler Infrastructure
Chenyao Suo, Jianrong Wang, Yongjia Wang +3
MLIR (Multi-Level Intermediate Representation) compiler infrastructure provides an efficient framework for introducing a new abstraction level for programming languages and domain-…
Bounded Exhaustive Random Program Generation for Testing Solidity Compilers
Haoyang Ma, Alastair F. Donaldson, Qingchao Shen +3
By July 2025, smart contracts collectively manage roughly $120 billion in assets. With Solidity remaining the dominant language for smart contract development, the correctness of S…