Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics
Recent advances in large language models for test case generation have improved branch coverage via prompt-engineered mutations. However, they still lack principled mechanisms for steering models toward specific high-risk execution branches, limiting their effectiveness for discovering subtle bugs and security vulnerabilities. We propose GLMTest, the first program structure-aware LLM framework for targeted test case generation that seamlessly integrates code property graphs and code semantics using a graph neural network and a language model to condition test case generation on execution branches. This structured conditioning enables controllable and branch-targeted test case generation, ...
| Authors | Khang Tran, Khoa Nguyen, Cristian Borcea et al. |
|---|---|
| Primary category | cs.SE |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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