10 papers
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…
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…
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…
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…
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…
TestWeaver: Execution-aware, Feedback-driven Regression Testing Generation with Large Language Models
Cuong Chi Le, Cuong Duc Van, Tung Duy Vu +4
While recent advances in large language models (LLMs) have shown promise in automating test generation for regression testing, they often suffer from limited reasoning about progra…