Retrieval-Augmented Test Generation: How Far Are We?

Retrieval Augmented Generation (RAG) has shown notable advancements in software engineering tasks. Despite its potential, RAG's application in unit test generation remains under-explored. To bridge this gap, we take the initiative to investigate the efficacy of RAG-based LLMs in test generation. As RAGs can leverage various knowledge sources to enhance their performance, we also explore the impact of different sources of RAGs' knowledge bases on unit test generation to provide insights into their practical benefits and limitations. Specifically, we examine RAG built upon three types of domain knowledge: 1) API documentation, 2) GitHub issues, and 3) StackOverflow Q&As. Each source offers essential knowledge for creating tests from different perspectives, i.e., API documentations provide official API usage guidelines, GitHub issues offer resolutions of issues related to the APIs from the library developers, and StackOverflow Q&As present community-driven solutions and best practices. For our experiment, we focus on five widely used and typical Python-based machine learning (ML) projects, i.e., TensorFlow, PyTorch, Scikit-learn, Google JAX, and XGBoost to build, train, and deploy complex neural networks efficiently. We conducted experiments using the top 10% most widely used APIs across these projects, involving a total of 188 APIs. We investigate the effectiveness of four state-of-the-art LLMs (open and closed-sourced), i.e., GPT-3.5-Turbo, GPT-4o, Mistral MoE 8x22B, and Llamma 3.1 405B. Additionally, we compare three prompting strategies in generating unit test cases for the experimental APIs, i.e., zero-shot, a Basic RAG, and an API-level RAG on the three external sources. Finally, we compare the cost of different sources of knowledge used for the RAG.
Comment: 18 pages + reference