8 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…
Documentation-Guided Agentic Codebase Migration from C to Rust
Minh Le-Anh, Anh Nguyen Hoang, Bach Le +1
Migrating legacy C repositories to Rust promises stronger memory safety, but existing translators often work at the level of files or functions and miss architectural intent. We pr…
CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases
Anh Nguyen Hoang, Minh Le-Anh, Bach Le +1
Given a large and evolving codebase, the ability to automatically generate holistic, architecture-aware documentation that captures not only individual functions but also cross-fil…
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…