6 papers
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…
The Path Not Taken: Duality in Reasoning about Program Execution
Eshgin Hasanov, Md Mahadi Hassan Sibat, Santu Karmaker +1
Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks. However, their adoption requires a true understanding of program execution rather than…
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…
Large Language Model Critics for Execution-Free Evaluation of Code Changes
Aashish Yadavally, Hoan Nguyen, Laurent Callot +1
Large language models (LLMs) offer a promising way forward for automating software engineering tasks, such as bug fixes, feature additions, etc., via multi-step LLM-based agentic w…