# The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation

arXiv: https://arxiv.org/abs/2501.01329 · HTML: https://mlsanity.com/paper/2501.01329/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2025.

## Built on by (most cited first, 16 of 16)

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- [QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code](https://mlsanity.com/paper/2511.01183/) (2025)
- [TAM-Eval: Evaluating LLMs for Automated Unit Test Maintenance](https://mlsanity.com/paper/2601.18241/) (2026)
- [PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation](https://mlsanity.com/paper/2605.00942/) (2026)
- [FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback](https://mlsanity.com/paper/2605.01264/) (2026)
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## Abstract (arXiv)

Test cases are essential for validating the reliability and quality of software applications. Recent studies have demonstrated the capability of Large Language Models (LLMs) to generate useful test cases for given source code. However, the existing work primarily relies on human-written plain prompts, which often leads to suboptimal results since the performance of LLMs can be highly influenced by the prompts. Moreover, these approaches use the same prompt for all LLMs, overlooking the fact that different LLMs might be best suited to different prompts. Given the wide variety of possible prompt formulations, automatically discovering the optimal prompt for each LLM presents a significant challenge. Although there are methods on automated prompt optimization in the natural language processing field, they are hard to produce effective prompts for the test case generation task. First, the methods iteratively optimize prompts by simply combining and mutating existing ones without proper guidance, resulting in prompts that lack diversity and tend to repeat the same errors in the generated test cases. Second, the prompts are generally lack of domain contextual knowledge, limiting LLMs' performance in the task.