Volume - 7 | Issue - 4 | december 2025
Published
28 November, 2025
Unit testing plays a crucial role in application software development by validating module functionality in isolation before system integration. Manually writing and reviewing unit test cases is time-consuming and defect-prone. Complex logic and boundary conditions are not tested thoroughly, leading to higher rework costs. Automated test generation using Large Language Models (LLMs) reduces development effort but faces challenges such as ensuring meaningful test coverage, handling invalid inputs, and addressing missing imports. This study aims to leverage LLMs in combination with the Autogen Agentic AI framework to generate high-quality Python unit tests by effectively prompting them, fixing failed test cases, validating them through test execution, analyzing results, and improving code coverage and mutation score. For experiments conducted on the Insurance Management Application, branch coverage improved from 98% to 99%, and the mutation score improved from 83.9% to 95.8%. The proposed approach significantly reduces manual effort while improving test suite effectiveness and software quality.
KeywordsUnit Testing Large Language Models Agentic AI Test Automation AI-Driven Testing Test Case Effectiveness Code Coverage Mutation Testing
