# Exploring Variability of Visual Accessibility Options in Operating Systems
###### Abstract  
Digital technologies are an opportunity to overcome disabilities, provided that accessibility is ensured. In this paper, we focus on visual accessibility and the way it is supported in Operating Systems (OS). The significant variability in this support has practical consequences, e.g., the difficulty to recommend or select an OS, or migrate from one OS to another. This suggests building a variability model for OS that would classify them and would serve as a reference. We propose a methodology to build such a variability model with the help of the Formal Concept Analysis (FCA) framework. In addition, as visual accessibility can be divided into several concerns (e.g., zoom, or contrast), we leverage an extension of FCA, namely Relational Concept Analysis. We also build an ontology to dispose of a standardized description of visual accessibility options. We apply our proposal to the analysis of the variability of a few representative operating systems. visual accessibility; variability representation and management; separate concerns in variability; Formal Concept Analysis; Relational Concept Analysis +
Footnote †: journal:  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-00000-000000-000000-000000-000000-  
it might be difficult to find which system will provide the same or the closest configuration from OS descriptions with different vocabulary and presentations. Solutions to these issues involve comprehending OS variability. This can be based on comparative studies, metrics, classifications and mappings between needs and OS. Comparative studies, classification and mappings are addressed in studies on OS accessibility that have been conducted in the past on a limited set of OS and options [12; 13; 14; 15]. They mainly present and discuss tables that show which option appears in which OS, and they remain at a coarse-grained level of description. These results are evidently useful, but we think that it is possible to go beyond texts and tables and propose another approach for analyzing and rendering variability at a fine-grained level. In particular, we would like to provide a variability model supporting more assistance tools. e.g., finding the OS providing a configuration best-matching the user needs or previous settings is not easy with a table, especially if there are no details on options implementing a high-level feature, or if there are many columns. Variability metrics are surveyed in [16]. Some of them are directly connected to our work and some correspond to code variability that we do not approach. Here, we aim to propose a variability model which can be navigated, and metrics. The approach we propose is based on Formal Concept Analysis (FCA, [17]). This framework builds classifications (also called _conceptual structures_) in which _concept_s group similar objects. The hierarchical structure (_concept lattice_) highlights commonalities and differences. In addition, it provides an easy support for navigation in the classifications [18; 19; 20]. We also leverage an extension of FCA, namely Relational Concept Analysis (RCA, [21]), to analyze variability along _separate concerns_, such as contrast and color theme, text style, brightness or zoom. With RCA, we analyze variability within each concern, and considering all of them as well. As FCA applies to standardized descriptions, which do not exist for all visual accessibility options in OS, we build an ontology. We develop an exploratory case study, where we observe different types of variability relationships on eight operating systems. To summarize our contributions, we propose:  
* A _methodology_ to build a variability model in the form of conceptual structures. This model can be navigated to comprehend variability. It can also be observed through various metrics. The novelty is that it allows us to analyze variability along separate concerns, or as a whole. The variability model is a classification built with a fine-grained knowledge on options.