David Lo

Seeing Is Coding: On the Effectiveness of Vision Language Models in Code Understanding

Large Language Models (LLMs) have achieved remarkable success in source code understanding, yet as software systems grow in scale, computational efficiency has become a critical bottleneck. Currently, these models rely on a text-based paradigm that treats source code as a linear...

To Run or Not to Run: Analyzing the Cost-Effectiveness of Code Execution in LLM-Based Program Repair

LLM-based agents for program repair are increasingly built on a “generate-run-revise” paradigm, iteratively executing tests to evaluate and refine patches. This execution-based approach has become standard practice in state-of-the-art systems. However, executions can be time-cons...

Understanding and Improving Model Editing for Secure Code Generation

Large language models (LLMs) are widely used for code generation but may reproduce vulnerabilities learned from insecure training data. Prior work mainly relies on inference-time hardening, which introduces auxiliary components and runtime overhead. We present the first systemati...