mlsanityData to 14 Oct 2026

Program Structure-aware Language Models: Targeted Software Testing beyond Textual Semantics

arXiv 2604.17715 ↗First posted 20 Apr 2026cs.SE cs.LGcited by 0

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Abstract (arXiv)

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, thereby potentially enhancing bug and security risk discovery. Experiments on real-world projects show that GLMTest built on a Qwen2.5-Coder-7B-Instruct model 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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 20 Apr 2026.

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23 of 50 references.

References · 23

  1. 2017Graph Attention Networkscited by 8,923
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  3. 2023Code Llama: Open Foundation Models for Codecited by 2,139
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  8. 2024The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinismcited by 116
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  10. 2024Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLMcited by 64
  11. 2023Using Large Language Models to Generate JUnit Tests: An Empirical Studycited by 53
  12. 2024TestGenEval: A Real World Unit Test Generation and Test Completion Benchmarkcited by 52
11 more
  1. 2023Effective Test Generation Using Pre-trained Large Language Models and Mutation Testingcited by 51
  2. 2023CAT-LM: Training Language Models on Aligned Code And Testscited by 28
  3. 2025Mutation-Guided LLM-based Test Generation at Metacited by 25
  4. 2018Building Language Models for Text with Named Entitiescited by 15
  5. 2024Vul-LMGNNs: Fusing language models and online-distilled graph neural networks for code vulnerability detectioncited by 11
  6. 2024ASTER: Natural and Multi-language Unit Test Generation with LLMscited by 9
  7. 2018SoK: Sanitizing for Securitycited by 5
  8. 2021On Introducing Automatic Test Case Generation in Practice: A Success Story and Lessons Learnedcited by 4
  9. 2025SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzingcited by 4
  10. 2025VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimizationcited by 3
  11. 2025Bridging Code Graphs and Large Language Models for Better Code Understandingcited by 2

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