Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models

--Generative AI is gaining increasing attention in software engineering, where testing remains an indispensable reliability mechanism. According to the widely adopted testing pyramid, unit tests constitute the majority of test cases and are often schematic, requiring minimal domain expertise. Automatically generating such tests under the supervision of software engineers can significantly enhance productivity during the development phase of the software lifecycle. This paper investigates the impact of code context and prompting strategies on the quality and adequacy of unit tests generated by various large language models (LLMs) across several families. The results show that including docstrings notably improves code adequacy, while further extending context to the full implementation yields definitely smaller gains. Notably, the chain-of-thought prompting strategy -- applied even to'reasoning' models -- achieves the best results, with up to 96.3% branch coverage, a 57% average mutation score, and near-perfect compilation success rate. Among the evaluated models, M5 (Gemini 2.5 Pro) demonstrated superior performance in both mutation score and branch coverage being still in top in terms of compilation success rate. ECENT years have brought significant advancements in artificial intelligence (AI), particularly in the areas of performance and productivity enhancement. However, AI -- and particularly large language models (LLMs) -- still suffer from several weaknesses. Among them, convincing but senseless content generation ('hallucination'), safety misalignment ('ethicality') [1], unfairness [2], and limited processing context are the most critical. In spite of these restrictions, and bearing in mind the limited and merely apparent creativity of LLMs [3], they have become versatile tools already widely used across a variety of domains (creative industries [4], entertainment, reporting, and software engineering [5] are just cases in point) for multiple tasks.