Planning to Explore: Curiosity-Driven Planning for LLM Test Generation
The use of LLMs for code generation has naturally extended to code testing and evaluation. As codebases grow in size and complexity, so does the need for automated test generation. Current approaches for LLM-based test generation rely on strategies that maximize immediate coverage gain, a greedy approach that plateaus on code where reaching deep branches requires setup steps that individually yield zero new coverage. Drawing on principles of Bayesian exploration, we treat the program's branch structure as an unknown environment, and an evolving coverage map as a proxy probabilistic posterior representing what the LLM has discovered so far. Our method, CovQValue, feeds the coverage map bac...
| Authors | Alfonso Amayuelas, Firas Laakom, Piotr Piękos et al. |
|---|---|
| Primary category | cs.SE |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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