Decision Support System for Ranking of Software Reliability Growth Models

Devanshu Kumar Singh, Hitesh, Vijay Kumar () and Hoang Pham ()
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Devanshu Kumar Singh: Amity University
Hitesh: Amity University
Vijay Kumar: Amity Institute of Applied Sciences, Amity University
Hoang Pham: Rutgers University

A chapter in Applications in Reliability and Statistical Computing, 2023, pp 227-244 from Springer

Abstract: Abstract 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.

Keywords: SRGMs; Software testing; Decision support system (DSS); Python; TOPSIS; Entropy (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-21232-1_12

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