  Context: Test-driven development (TDD) is a widely employed software
development practice that involves developing test cases based on requirements
prior to writing the code. Although various methods for automated test case
generation have been proposed, they are not specifically tailored for TDD,
where requirements instead of code serve as input. Objective: In this paper, we
introduce a text-to-testcase generation approach based on a large language
model (GPT-3.5) that is fine-tuned on our curated dataset with an effective
prompt design. Method: Our approach involves enhancing the capabilities of
basic GPT-3.5 for text-to-testcase generation task that is fine-tuned on our
curated dataset with an effective prompting design. We evaluated the
effectiveness of our approach using a span of five large-scale open-source
software projects. Results: Our approach generated 7k test cases for open
source projects, achieving 78.5% syntactic correctness, 67.09% requirement
alignment, and 61.7% code coverage, which substantially outperforms all other
LLMs (basic GPT-3.5, Bloom, and CodeT5). In addition, our ablation study
demonstrates the substantial performance improvement of the fine-tuning and
prompting components of the GPT-3.5 model. Conclusions: These findings lead us
to conclude that fine-tuning and prompting should be considered in the future
when building a language model for the text-to-testcase generation task
