20 citations · 21 across the 7 of their papers we have counts for
8 papers
SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch
Yihao Chen, Shi Chang, Feng Lin +4
LLM-based agents excel at software engineering tasks where an existing codebase provides context, but constructing a program from scratch remains fundamentally harder. Recent bench…
MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis
Yihao Chen, Shi Chang, Khaled Chawa +4
Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation. However, constructing a…
VulWeaver: Weaving Broken Semantics for Grounded Vulnerability Detection
Yiheng Cao, Yihao Chen, Xin Hu +9
Detecting vulnerabilities in source code remains critical yet challenging, as conventional static analysis tools construct inaccurate program representations, while existing LLM-ba…
SWE-Effi: Re-Evaluating Software AI Agent System Effectiveness Under Resource Constraints
Zhiyu Fan, Kirill Vasilevski, Dayi Lin +6
The advancement of large language models (LLMs) and code agents has demonstrated significant potential to assist software engineering (SWE) tasks, such as autonomous issue resoluti…
RepoForge: Training a SOTA Fast-thinking SWE Agent with an End-to-End Data Curation Pipeline Synergizing SFT and RL at Scale
Zhilong Chen, Chengzong Zhao, Boyuan Chen +9
Training software engineering (SWE) LLMs is bottlenecked by expensive infrastructure, inefficient evaluation pipelines, scarce training data, and costly quality control. We present…
SPICE: An Automated SWE-Bench Labeling Pipeline for Issue Clarity, Test Coverage, and Effort Estimation
Gustavo A. Oliva, Gopi Krishnan Rajbahadur, Aaditya Bhatia +7
High-quality labeled datasets are crucial for training and evaluating foundation models in software engineering, but creating them is often prohibitively expensive and labor-intens…
An Empirical Study of Challenges in Machine Learning Asset Management
Zhimin Zhao, Yihao Chen, Abdul Ali Bangash +2
In machine learning (ML), efficient asset management, including ML models, datasets, algorithms, and tools, is vital for resource optimization, consistent performance, and a stream…
Why Not Mitigate Vulnerabilities in Helm Charts?
Yihao Chen, Jiahuei Lin, Bram Adams +1
[Context]: Containerization ensures the resilience of distributed applications by Kubernetes. Helm is a package manager for Kubernetes applications. A Helm package, namely "Chart''…