This paper presents a comprehensive literature review of the automated analysis of feature models over the past 20 years. Feature models are used to represent the commonalities and variabilities of software product lines. Automated analysis of feature models involves computer-aided extraction of information from these models. The literature has contributed various operations, techniques, tools, and empirical results that have not been systematically surveyed before. This paper brings together previously disparate streams of work to provide a conceptual framework for understanding and categorizing future contributions. It also discusses current studies and proposes challenges for future research. The paper is structured as follows: Section 2 presents feature models, Section 3 describes the review method, Section 4 presents a conceptual framework, Section 5 describes the different analysis operations, Section 6 presents the automated techniques used for analysis, Section 7 discusses the results of performance analysis of feature models, Section 8 discusses the results obtained and describes some challenges to be faced in the future, and Section 9 presents some conclusions. Feature models represent the information of all possible products of a software product line in terms of features and relationships among them. They are a special type of information model used in software product line engineering. Feature models are represented as hierarchically arranged sets of features with different relationships among those features. They model all possible products of a software product line in a given context. Unlike traditional information models, feature models not only represent a single product but a family of them in the same model. Automated analysis of feature models involves extracting information from feature models using automated mechanisms. This task is error-prone and tedious, and it is infeasible to do manually with large-scale feature models. It is an active area of research and is gaining importance in both practitioners and researchers in the software product line community. Since the introduction of feature models, the literature has contributed with a number of operations of analysis, tools, paradigms, and algorithms to support the analysis process. The paper presents a structured literature review of the existing proposals for the automated analysis of feature models. The main contribution of this article is to bring together previously scattered studies to set the basis for future research as well as introduce new researchers and practitioners in this thriving area. We present a conceptual framework to understand the different proposals and classify new contributions in the future. 53 primary studies were analysed from where we report 30 operations of analysis and 4 different groups of proposals to automate those operations. As a result of our literature review, we also report some challenges that remain open to research. The main target audience of this literature review are researchers in the field of automated analysis, tool developers or practitioners who are interested in analysis of feature models as well as researchers and professionals of information systems interested in software product lines, their models and analyses.This paper presents a comprehensive literature review of the automated analysis of feature models over the past 20 years. Feature models are used to represent the commonalities and variabilities of software product lines. Automated analysis of feature models involves computer-aided extraction of information from these models. The literature has contributed various operations, techniques, tools, and empirical results that have not been systematically surveyed before. This paper brings together previously disparate streams of work to provide a conceptual framework for understanding and categorizing future contributions. It also discusses current studies and proposes challenges for future research. The paper is structured as follows: Section 2 presents feature models, Section 3 describes the review method, Section 4 presents a conceptual framework, Section 5 describes the different analysis operations, Section 6 presents the automated techniques used for analysis, Section 7 discusses the results of performance analysis of feature models, Section 8 discusses the results obtained and describes some challenges to be faced in the future, and Section 9 presents some conclusions. Feature models represent the information of all possible products of a software product line in terms of features and relationships among them. They are a special type of information model used in software product line engineering. Feature models are represented as hierarchically arranged sets of features with different relationships among those features. They model all possible products of a software product line in a given context. Unlike traditional information models, feature models not only represent a single product but a family of them in the same model. Automated analysis of feature models involves extracting information from feature models using automated mechanisms. This task is error-prone and tedious, and it is infeasible to do manually with large-scale feature models. It is an active area of research and is gaining importance in both practitioners and researchers in the software product line community. Since the introduction of feature models, the literature has contributed with a number of operations of analysis, tools, paradigms, and algorithms to support the analysis process. The paper presents a structured literature review of the existing proposals for the automated analysis of feature models. The main contribution of this article is to bring together previously scattered studies to set the basis for future research as well as introduce new researchers and practitioners in this thriving area. We present a conceptual framework to understand the different proposals and classify new contributions in the future. 53 primary studies were analysed from where we report 30 operations of analysis and 4 different groups of proposals to automate those operations. As a result of our literature review, we also report some challenges that remain open to research. The main target audience of this literature review are researchers in the field of automated analysis, tool developers or practitioners who are interested in analysis of feature models as well as researchers and professionals of information systems interested in software product lines, their models and analyses.