Programming attracts a diverse range of learners, including individuals with dyslexia, a learning difficulty that affects reading, writing, and spelling despite average intelligence and educational opportunities. Although prior work has examined dyslexia in natural-language and code comprehension, we still know little...
Zi-Han Fang, Janice Chung, Rui-Jia Chen et al.· Proceedings of the 41st IEEE...· 0 citations
Large language models (LLMs) have become increasingly capable of generating code. However, achieving stronger code-generation performance still often relies on costly model adaptation, i.e., fine-tuning pretrained model parameters. Prior studies have shown correspondence between human code processing and neural models'...
Yue-Ke Zhang, Zi-Han Fang, Kevin Leach et al.· 0 citations
An eye-tracking study with 49 novice programmers to compare how they comprehend FP (Racket) and IP (C++) during tasks and offers insights to inform tools, pedagogy, and strategies for making Racket and related FP instruction more approachable in similar educational settings.
Zi-Han Fang, Wei-Zhe Jiao, Robert Tairas et al.· ACM Transactions on Software...· 2 citations
AgentForge is presented, an immersive learning system in which novices take on one of four software-engineering roles: Task Planner, Patch Author, Code Reviewer, or Test Runner, within a multi-agent code-repair workflow, which clarifies role-specific responsibilities, makes agent coordination and intermediate artifacts...
A large-scale empirical characterization of AI-assisted OSS contribution is provided and practical insights for designing and governing responsible vibe-coding practices in open-source development are offered.
GraphAlignCoder is introduced, a training framework that transfers explicit correctness structure into code generation and consistently outperforms the base model, code-only SFT, and CodeRL across all benchmarks.
Yue-Ke Zhang, Zihan Fang, Kevin Leach et al.· 0 citations
SCOPE is presented, a prover-initialized subgoal critic for code generation that adapts a Lean-oriented prover model to produce three parseable feedback fields for downstream code generation: subgoals, gap analysis, and a robustness checklist.
Yueke Zhang, Yifan Zhang, Zihan Fang et al.· 0 citations
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