There has been considerable development in the domain of dependability engineering and analysis in recent decades. The quality guidelines related to dependability attributes such as safety, reliability, maintainability, and availability have evolved into recommending progressively stricter prerequisites and requirements related to the quality of products and services. Owing to this development, the aspects, processes, and practices that were either simply approximated or disregarded until now must be considered in quantitative evaluation to offer satisfactory estimates related to dependability. With explicit regard to dependability, it implies that obstructions and prerequisite conditions cannot be ignored. In this manner, so as to help such a pattern, explicit procedures and apparatuses are required to satisfactorily manage dynamic viewpoints in dependability assessment. State-space modeling techniques are quite productive when attempts are made to illustrate the dynamic behavior of a system. They provide support for critical analysis to deliver being instrumental in a direct mapping with any mathematical modeling tool. The objective of this article is to offer a systematic review of various modeling formalisms with insights into state-space modeling techniques for dependability analysis. It also provides a comparison among the various modeling formalisms, and therefore, can be used as a reference for practitioners, developers, and researchers in state-space dependability modeling and evaluation.

Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%; logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naïve Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.