5 papers · 1 filter
Teaching Code LLMs to Reason with Intermediate Formal Specifications
Minh Le-Anh, Cuong Chi Le, Tien N. Nguyen
Unlike natural-language specifications, executable formal specifications provide machine-checkable constraints for verifying, debugging, and repairing code. However, writing such s…
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…
Semantic Evolution over Populations for LLM-Guided Automated Program Repair
Cuong Chi Le, Minh Le-Anh, Cuong Duc Van +1
Large language models (LLMs) have recently shown strong potential for automated program repair (APR), particularly through iterative refinement that generates and improves candidat…
Enhancing Program Repair with Specification Guidance and Intermediate Behavioral Signals
Minh Le-Anh, Cuong Chi Le, Tien N. Nguyen
Automated Program Repair (APR) has recently benefited from large language models (LLMs). However, most LLM-based APR approaches still rely primarily on coarse-grained, end-to-end s…
SpecMind: Cognitively Inspired, Interactive Multi-Turn Framework for Postcondition Inference
Cuong Chi Le, Minh V. T Pham, Tung Vu Duy +4
Specifications are vital for ensuring program correctness, yet writing them manually remains challenging and time-intensive. Recent large language model (LLM)-based methods have sh…