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…
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…
When Names Disappear: Revealing What LLMs Actually Understand About Code
Cuong Chi Le, Minh V. T. Pham, Cuong Duc Van +3
Large Language Models (LLMs) achieve strong results on code tasks, but how they derive program meaning remains unclear. We argue that code communicates through two channels: struct…
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…
SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs
Minh V. T. Pham, Huy N. Phan, Hoang N. Phan +3
Large language models (LLMs) are transforming automated program repair (APR) through agent-based approaches that localize bugs, generate patches, and verify fixes. However, the lac…
VisualCoder: Guiding Large Language Models in Code Execution with Fine-grained Multimodal Chain-of-Thought Reasoning
Cuong Chi Le, Hoang-Chau Truong-Vinh, Huy Nhat Phan +3
Predicting program behavior and reasoning about code execution remain significant challenges in software engineering, particularly for large language models (LLMs) designed for cod…
CodeFlow: Program Behavior Prediction with Dynamic Dependencies Learning
Cuong Chi Le, Hoang Nhat Phan, Huy Nhat Phan +2
Predicting program behavior without execution is a critical task in software engineering. Existing models often fall short in capturing the dynamic dependencies among program eleme…