6 papers · 1 filter
On Behavioral Alignment of Model-Code and Human-Code Understandability via Behavioral Proxies
Xiaokai Rong, Aashish Yadavally, Anh H. N. Nguyen +2
Code understandability is a critical aspect of software quality. Prior research has largely focused on this attribute from a human-centric or code-centric perspective, while it sho…
Post-Hoc Attention Steering of Large Language Models for Robust Code Understanding under Obfuscation
Xiaokai Rong, Aashish Yadavally, Tien N. Nguyen
Code obfuscation is widely used in software systems and malware to conceal program logic and hinder analysis, posing significant challenges for both human developers and automated…
AdaptAgent: A Multi-agent, Domain-Guided Reasoning Framework for Code Adaptation
Xiaokai Rong, Hridya Dhulipala, Aashish Yadavally +1
Developers often need to adapt into their projects the code generated from LLMs or code snippets from online forums. However, integrating them into an existing repository remains c…
Can Perplexity Serve as a Cognitive Signal for Code Understandability?
Xiaokai Rong, Mohammadali Sefidi Esfahani, Aashish Yadavally +2
Recent work suggests that token-level perplexity from large language models can align with localized human confusion during code comprehension. This raises a natural question: can…
Benchmarking Code Improvement with Progressive, Adaptive, and Interactive Feedback
Cuong Chi Le, Aashish Yadavally, Minh Le-Anh +1
Large language models (LLMs) are typically evaluated on code generation and program repair using binary functional correctness: a generated program or patch either passes or fails…
Fuzzwise: Intelligent Initial Corpus Generation for Fuzzing
Hridya Dhulipala, Xiaokai Rong, Aashish Yadavally +1
In mutation-based greybox fuzzing, generating high-quality input seeds for the initial corpus is essential for effective fuzzing. Rather than conducting separate phases for generat…