31 citations · 168 across the 75 of their papers we have counts for
20 papers · 1 filter
A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing
Ye Shang, Quanjun Zhang, Chunrong Fang +3
Unit testing plays a pivotal role in software development, improving software quality and reliability. However, generating effective test cases manually is time-consuming, promptin…
Token-Budget-Aware LLM Reasoning
Tingxu Han, Zhenting Wang, Chunrong Fang +3
Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks. While methods like Chain-of-Thought (CoT) reasoning and enhance LLM performance by decompo…
Demonstration Attack against In-Context Learning for Code Intelligence
Yifei Ge, Weisong Sun, Yihang Lou +7
Recent advancements in large language models (LLMs) have revolutionized code intelligence by improving programming productivity and alleviating challenges faced by software develop…
Security of Language Models for Code: A Systematic Literature Review
Yuchen Chen, Weisong Sun, Chunrong Fang +7
Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these…
TestBench: Evaluating Class-Level Test Case Generation Capability of Large Language Models
Quanjun Zhang, Ye Shang, Chunrong Fang +3
Software testing is a crucial phase in the software life cycle, helping identify potential risks and reduce maintenance costs. With the advancement of Large Language Models (LLMs),…
Mutation-Based Deep Learning Framework Testing Method in JavaScript Environment
Yinglong Zou, Juan Zhai, Chunrong Fang +3
In recent years, Deep Learning (DL) applications in JavaScript environment have become increasingly popular. As the infrastructure for DL applications, JavaScript DL frameworks pla…
SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing
An Guo, Yuan Zhou, Haoxiang Tian +7
Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal acci…
No Man is an Island: Towards Fully Automatic Programming by Code Search, Code Generation and Program Repair
Quanjun Zhang, Chunrong Fang, Ye Shang +3
Automatic programming attempts to minimize human intervention in the generation of executable code, and has been a long-standing challenge in the software engineering community. To…
LSem2Vec: A Simple yet Effective Two-Stage Approach for Source Code Embedding
Zixiang Xian, Chenhui Cui, Rubing Huang +2
The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embeddings playing a crucial role in tasks s…
CooTest: An Automated Testing Approach for V2X Communication Systems
An Guo, Xinyu Gao, Zhenyu Chen +5
Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication tec…
Eliminating Backdoors in Neural Code Models for Secure Code Understanding
Weisong Sun, Yuchen Chen, Chunrong Fang +7
Neural code models (NCMs) have been widely used to address various code understanding tasks, such as defect detection. However, numerous recent studies reveal that such models are…
TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration
Siqi Gu, Quanjun Zhang, Kecheng Li +5
Unit testing is crucial for detecting bugs in individual program units but consumes time and effort. Recently, large language models (LLMs) have demonstrated remarkable capabilitie…
Source Code Summarization in the Era of Large Language Models
Weisong Sun, Yun Miao, Yuekang Li +6
To support software developers in understanding and maintaining programs, various automatic (source) code summarization techniques have been proposed to generate a concise natural…
ESALE: Enhancing Code-Summary Alignment Learning for Source Code Summarization
Chunrong Fang, Weisong Sun, Yuchen Chen +7
(Source) code summarization aims to automatically generate succinct natural language summaries for given code snippets. Such summaries play a significant role in promoting develope…
Practical, Automated Scenario-based Mobile App Testing
Shengcheng Yu, Chunrong Fang, Mingzhe Du +3
The importance of mobile application (app) quality insurance is increasing with the rapid development of the mobile Internet. Automated test generation approaches, as a dominant di…
Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation
Yuan Xiao, Shiqing Ma, Juan Zhai +3
The robustness of convolutional neural networks (CNNs) is vital to modern AI-driven systems. It can be quantified by formal verification by providing a certified lower bound, withi…
Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders
Tingxu Han, Weisong Sun, Ziqi Ding +5
Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can…
A Systematic Literature Review on Large Language Models for Automated Program Repair
Quanjun Zhang, Chunrong Fang, Yang Xie +4
Automated Program Repair (APR) attempts to patch software bugs and reduce manual debugging efforts. Very recently, with the advances in Large Language Models (LLMs), an increasing…
Pre-trained Model-based Actionable Warning Identification: A Feasibility Study
Xiuting Ge, Chunrong Fang, Quanjun Zhang +8
Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainl…
Can Distillation Mitigate Backdoor Attacks in Pre-trained Encoders?
TIngxu Han, Wei Song, Weisong Sun +7
Self-Supervised Learning (SSL) has become a prominent paradigm for pre-training encoders to learning general-purpose representations from unlabeled data and releasing them on third…