Software testing is a critical phase in the software development lifecycle, aimed at ensuring the reliability and functionality of software modules or applications. It involves rigorous examination of software artifacts and behavior to validate its functionalities and identify any potential errors. The ultimate goal of software testing is to enhance the overall quality of the developed software, thereby bolstering customer satisfaction and maximizing profitability in the software market. While various methods and tools have been employed for software testing in the past, many fail to provide comprehensive guidelines for improving software quality. Moreover, existing testing frameworks often lack stage-by-stage output with precise error definitions, hindering effective error mitigation strategies. Additionally, regression testing models reliant on feedback mechanisms tend to introduce computational and time complexities. To address these challenges, the prioritization of test cases (PTC) emerges as a pivotal process for enhancing fault detection rates and refining error descriptions to elevate software quality. In this context, this study leverages an Extended FireFly Algorithm (EFFA) to formulate an advanced prioritization methodology. The EFFA capitalizes on the swift and efficient search capabilities of the FireFly Algorithm (FFA), significantly enhancing fault detection accuracy while mitigating inherent drawbacks. Implemented and tested within the DOTNET software environment, the proposed EFFA prioritization methodology demonstrates superior performance metrics, as evidenced by improved Fault Detection Rates (FDR) and reduced processing times.