Enhancing Unit Testing Context and Efficiency in Business Through RAG-Based Approaches

Joyce Silvera

Abstract: A fundamental technique in software engineering for confirming the accuracy and dependability of separate program components is unit testing. However, conventional unit testing techniques usually don't pay enough attention to context, which leads to test cases that don't sufficiently capture intricate business logic and inter-component connections. Unit test generation can now be partially automated thanks to recent advancements in large language models, but these models are still limited by static information and a lack of project-specific understanding. In order to improve the contextual richness, accuracy, and maintainability of automated unit test generation, this work investigates the use of Retrieval-Augmented Generation (RAG). RAG-based techniques make it possible to create more accurate and semantically relevant unit tests by including dynamically obtained data, such as documentation, dependency structures, and business constraints. RAG-based approaches increase test relevance, decrease test design flaws, and minimize manual testing time, according to a thorough synthesis of current studies. While highlighting important issues and potential research directions, the paper comes to the conclusion that RAG-based unit testing is a promising approach to enhancing software quality in intricate business systems.

Keywords: Unit Testing, Retrieval-Augmented Generation, Large Language Models, Software Testing Automation, Context-Aware Testing, Business Logic Validation

How to Cite?: Joyce Silvera, "Enhancing Unit Testing Context and Efficiency in Business Through RAG-Based Approaches", Volume 15 Issue 1, January 2026, International Journal of Science and Research (IJSR), Pages: 527-533, https://www.ijsr.net/getabstract.php?paperid=SR26107033842, DOI: https://dx.doi.org/10.21275/SR26107033842