Papers by Hai Phan
Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics (2026.findings-acl)
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| Challenge: | Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations, limiting their effectiveness for discovering subtle bugs and security vulnerabilities. |
| Approach: | They propose a program structure-aware LLM framework that integrates code property graphs and code semantics to condition test case generation on execution branches. |
| Outcome: | Experiments on real-world projects show that GLMTest improves branch accuracy from 27.4% to 50.2% on TestGenEval benchmark compared with state-of-the-art LLMs, i.e., Claude-Sonnet-4.5 and GPT-4o-mini. |