# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 2 Literature Review
The modeling findings were used to assess the system's performability [37]. Early on in the system's functioning, the software's performability was assessed using stochastic reward networks (SRNs). An automated process for converting high-level Unified Modeling Language (UML) to SRNs was suggested as an effective analysis technique [38]. A performability analysis provides helpful information in making decisions. The expected performabilities for a drone processing system were estimated based on comprehensive SRNs [31]. The reliability and performability of hardware were assessed using probability-distribution-based techniques, which have since been extended to include software performability evaluations. There are instances where a breakdown happens while the system is in use in the field, causing the system's performance to decline; the breakdown is then fixed, restoring the system's functionality. A non-homogeneous Poisson process (NHPP) was used to model a situation that was similar to the real one and assess the effect of failure on system performance and reliability [39]. To model a system's performability based on parameters, there are numerous analytic techniques available [40; 41; 42; 43; 44]. Among these, by optimizing performability parameters, an optimization algorithm may assess the system's performability [45]. Multi-state system theory was utilized to represent the cloud services system, and the performance and reliability were determined via the application of the universal generating function [46]. To assess the wireless mesh network's availability and performability, intelligent state sampling was used. Availability and performability in relation to transmission power and network node density were examined [47]. Software and hardware faults are both possible in SCS operation, and this needs to be taken into account [48]. The design and operation of SCSs provide numerous challenges as the system grows in size and complexity. It was suggested to use a modeling framework that takes into account both software and hardware failures in order to assess a system's performance and reliability [49]. When one or more components of a system fail, the system either stops working entirely or performs worse. Due to the influence of components with varying performance levels and the performance deterioration of different components, the system's performance level also varies. These components, in different states, are coupled together in different ways to form a system. These systems use the minimal performance ratio of the subsystems to assess the overall system's performability [50]. Based on the probability or failure frequency of each state, a multivalued decision diagram approach was proposed to evaluate the reliability, availability, and performability of a multistate system [51]. The probability of component failure is computed using the fault effect (FE), fault hazard analysis (FHA), and fault tree analysis (FTA), and the results are used to estimate system reliability. Based on the scheduling between parts, a prediction model for system performance was developed. A model was developed utilizing architecture analysis and design language (AADL) to forecast system performability based on the reliability and performance prediction model [52]. Using an MDD, an analysis method was presented to analyze the performability of a dynamic multi-state k-out-of-n system. A model was created for a system-performance MDD that can represent the state needs of the system [53].