3 citations · 8 across the 17 of their papers we have counts for
17 papers
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
Xin Zhou, Chun Yong Chong, Kisub Kim +11
Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs. Yet existing repository-level…
Vul4Py: Benchmarking Automated Vulnerability Repair in Python with Paired Exploit and Functional Oracles
Tan Bui, Ting Zhang, Ferdian Thung +4
Automated Vulnerability Repair (AVR) has advanced rapidly across program analysis, machine learning, and Large Language Models (LLMs), but a verifiable, head-to-head comparison of…
What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents
Rui Shu, Chun Yong Chong, Xin Zhou +6
Coding agents are ranked almost entirely by resolve rate: whether their final patch passes the target tests. Yet two agents can reach the same outcome through very different proces…
Beyond Function-Level Analysis: Context-Aware Reasoning for Inter-Procedural Vulnerability Detection
Yikun Li, Ting Zhang, Jieke Shi +10
Recent progress in ML and LLMs has improved vulnerability detection, and recent datasets have reduced label noise and unrelated code changes. However, most existing approaches stil…
Human-Aligned Code Readability Assessment with Large Language Models
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +6
Code readability is crucial for software comprehension and maintenance, yet difficult to assess at scale. Traditional static metrics often fail to capture the subjective, context-s…
Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework
Chengran Yang, Ting Zhang, Jinfeng Jiang +9
Current learning-based Automated Vulnerability Repair (AVR) approaches, while promising, often fail to generalize effectively in real-world scenarios. Our diagnostic analysis revea…
VisDocSketcher: Towards Scalable Visual Documentation with Agentic Systems
Luís F. Gomes, Xin Zhou, David Lo +1
Visual documentation is an effective tool for reducing the cognitive barrier developers face when understanding unfamiliar code, enabling more intuitive comprehension. Compared to…
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios
Junkai Chen, Huihui Huang, Yunbo Lyu +10
Large language model-powered code agents are rapidly transforming software engineering, yet the security risks of their generated code have become a critical concern. Existing benc…
SLICEMATE: Accurate and Scalable Static Program Slicing via LLM-Powered Agents
Jianming Chang, Jieke Shi, Yunbo Lyu +5
Static program slicing, which extracts the executable portions of a program that affect the values at a specific location, supports many software analysis tasks such as debugging a…
Out of Distribution, Out of Luck: How Well Can LLMs Trained on Vulnerability Datasets Detect Top 25 CWE Weaknesses?
Yikun Li, Ngoc Tan Bui, Ting Zhang +16
Automated vulnerability detection research has made substantial progress, yet its real-world impact remains limited. Prior work found that current vulnerability datasets suffer fro…
Beyond Surface Similarity: Evaluating LLM-Based Test Refactorings with Structural and Semantic Awareness
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
Large Language Models (LLMs) are increasingly used to refactor unit tests, improving readability and structure while preserving behavior. Evaluating such refactorings, however, rem…
Rethinking Cognitive Complexity for Unit Tests: Toward a Readability-Aware Metric Grounded in Developer Perception
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +5
Automatically generated unit tests-from search-based tools like EvoSuite or LLMs-vary significantly in structure and readability. Yet most evaluations rely on metrics like Cyclomat…
CODE-DITING: A Reasoning-Based Metric for Functional Alignment in Code Evaluation
Guang Yang, Yu Zhou, Xiang Chen +5
Trustworthy evaluation methods for code snippets play a crucial role in neural code generation. Traditional methods, which either rely on reference solutions or require executable…
An LLM-as-Judge Metric for Bridging the Gap with Human Evaluation in SE Tasks
Xin Zhou, Kisub Kim, Ting Zhang +6
Large Language Models (LLMs) and other automated techniques have been increasingly used to support software developers by generating software artifacts such as code snippets, patch…
Assessing and Advancing Benchmarks for Evaluating Large Language Models in Software Engineering Tasks
Xing Hu, Feifei Niu, Junkai Chen +5
Large language models (LLMs) are gaining increasing popularity in software engineering (SE) due to their unprecedented performance across various applications. These models are inc…
Bridging Bug Localization and Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models
Jianming Chang, Xin Zhou, Lulu Wang +2
Automated issue fixing is a critical task in software debugging and has recently garnered significant attention from academia and industry. However, existing fixing techniques pred…
LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks
Xin Zhou, Martin Weyssow, Ratnadira Widyasari +7
Large Language Models (LLMs) are widely utilized in software engineering (SE) tasks, such as code generation and automated program repair. However, their reliance on extensive and…