The requirement for Software Reliability Growth Models (SRGMs) has increased exponentially in response to the growing demand for strong and reliable software systems. During the testing phase of the Software Development Life Cycle (SDLC), SRGMs are particularly effective for estimating fault content, minimizing testing expenses, and maximizing software reliability. There has been a lot of research into selecting the best SRGMs for a certain failure dataset and then ranking all the SRGMs against the dataset. In this chapter, we have studied the mentioned problem and the solution to automate it with the developed compact Decision Support System (DSS), which includes all the functionalities and computational analysis of error logs and ensure error-free software to achieve the desired objective. The DSS is developed in Python utilizing several well-known packages such as Numpy, Scipy, Tkinter, and Pandas. To rank SRGMs employed in the DSS, we used Entropy & Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) ranking methodology. The implemented schema provides highly accurate performance indexes for the SRGMs required for efficient ranking, emphasizing the significance of the proposed prototype of DSS in the open literature, being a novel and ingenious development in the domain of software reliability.