Call-Chain-Aware LLM-Based Test Generation for Java Projects
Large language models (LLMs) have recently shown strong potential for generating project-level unit tests. However, existing state-of-the-art approaches primarily rely on execution-path information to guide prompt construction, which is often insufficient for complex software systems with rich inter-class dependencies, deep call chains, and intricate object initialization requirements. In this paper, we present CAT, a novel call-chain-aware LLM-based test generation approach that explicitly incorporates call-chain and dependency contexts into prompts through dedicated static analysis. To construct executable, semantically valid test contexts, CAT systematically models caller--callee relat...
| Authors | Guancheng Wang, Qinghua Xu, Lionel C. Briand et al. |
|---|---|
| Primary category | cs.SE |
| Published | 23 April 2026 |
| Last updated at source | 11 October 2026 |
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