Generating tests automatically is a key and ongoing area of focus in software engineering research. The emergence of Large Language Models (LLMs) has opened up new opportunities, given their ability to perform a wide spectrum of tasks. However, the effectiveness of LLM-based approaches compared to traditional techniques such as search-based software testing (SBST) and symbolic execution remains uncertain. In this paper, we perform an extensive study of automatic test generation approaches based on three tools: EvoSuite for SBST, Kex for symbolic execution, and TestSpark for LLM-based test generation. We evaluate tools performance on the GitBug Java dataset and compare them using various execution-based and feature-based metrics. Our results show that while LLM-based test generation is promising, it falls behind traditional methods in terms of coverage. However, it significantly outperforms them in mutation scores, suggesting that LLMs provide a deeper semantic understanding of code. LLM-based approach also performed worse than SBST and symbolic execution-based approaches w.r.t. fault detection capabilities. Additionally, our feature-based analysis shows that all tools are primarily affected by the complexity and internal dependencies of the class under test (CUT), with LLM-based approaches being especially sensitive to the CUT size.