Optimizing software release decisions: a TFN-based uncertainty modeling approach

In our contemporary world, where technology is omnipresent and essential to daily life, the reliability of software systems is indispensable. Consequently, efforts to optimize software release time and decision-making processes have become imperative. Software reliability growth models (SRGMs) have emerged as valuable tools in gauging software reliability, with researchers studying various factors such as change point and testing effort. However, uncertainties persist throughout testing processes, which are inherently influenced by human factors. Fuzzy set theory has emerged as a valuable tool in addressing the inherent uncertainties and complexities associated with software systems. Its ability to model imprecise, uncertain, and vague information makes it particularly well-suited for capturing the nuances of software reliability. In this research, we propose a novel approach that amalgamates change point detection, logistic testing effort function modeling, and triangular fuzzy numbers (TFNs) to tackle uncertainty and vagueness in software reliability modeling. Additionally, we explore release time optimization considering TFNs, aiming to enhance decision-making in software development and release planning.