NEUROTESTGEN: Neuro-Symbolic Guided Test Generation with Large Language Models
[Inżynieria oprogramowania] Autorzy: Ruixin Zhang, Jiho Shin, Hung Viet Pham i 1 więcej
Ensuring high structural coverage remains a fundamental challenge in automated test generation, particularly for complex software systems where reaching specific lines or branches requires satisfying intricate control- and data-flow constraints. Large Language Models (LLMs) have recently demonstrated strong capabilities in producing human-like test cases; however, they often struggle to generate inputs that satisfy precise path conditions. Conversely, symbolic execution can systematically derive such constraints, but it often fails to construct realistic, executable test cases and is constrained by scalability limitations. In this paper, we introduce NEUROTESTGEN, a hybrid approach that integrates symbolic execution with LLM-driven test synthesis to generate test cases targeting on-demand code coverage. Given a set of target statements within a method, NEUROTESTGEN first employs a symbo…
- Kategoria arxiv
- cs.SE
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- Inżynieria oprogramowania