# Exploring Variability of Visual Accessibility Options in Operating Systems
## 6 Case Study
### Experimental Setup and Process  
As presented before, we analyze 8 operating systems: _Android5.1_, _Android6.0_, _iOS5_, _MacOS10_.10_, _Ubuntu_12.04, _Ubuntu_14.10, _Windows7.0 and _Windows8.1_. We study zoom, contrast/colors, text style (font size and style) and brightness. When we apply the KD process on these systems, data preparation step produces:  
* 32 _feature models_ corresponding to the 4 mechanisms for the 8 systems;
* then we reduced the dataset to: 3 ontology parts and 3 mapping tables, for brightness, contrast and colors, and text styles mechanisms; this reduction has been decided for time and human effort reduction purpose; it is worth to note that we included the largest mechanism (contrast and colors). * using the ontology, mapping tables and feature models, we derive the formal contexts: one central formal context lists the 8 OS; 3 formal contexts are derived for the 3 mechanisms. Each of them describes the implementation of the mechanism by the 8 OS on this mechanism. Finally, 3 relational contexts link the OS of the central context to their implementation on one mechanism. The data mining step produces 4 concept lattices: one for each mechanism (contrast and colors, text styles, brightness), and one for the OS (the central one). Table 8 presents the number of objects, attributes and concepts. Then the 4 resulting lattices are analyzed at the interpretation step. Table 9 shows metrics about OS feature models, that is about variability inside each OS, taken individually. To compute these metrics, we used the _FAMILIAR_ tool which provides the values of these metrics for any given feature model [27]. The feature models in _FAMILIAR_ format are available in the repository associated to this paper (See Data Availability Statement and Footnote 2). _FAMILIAR_ is a feature model editor that we used in the data preparation step. It also provides many other operations for reasoning about feature models. These feature models are built in the data preparation step and are the input of the data mining step, where OS are classified. From Table 9, we know that the description of OS by feature models has a reasonable size, having a small or medium number of features. The number of configurations is the number of possible user settings. It is an indicator of how difficult it will be to choose an OS in this search space, or to move from one OS to another. We might expect it will be easy for brightness, but tricky for contrast and color. The four columns contain respectively: the FM name, the feature number, the valid configuration number, and the depth (edge number of a longest path). We can make the following observations. FMs _MacOS1010_Brightness_ and _Ubuntu_1204_Brightness_ (resp. _Android51_Brightness_ and _Android60_Brightness_) have the highest (resp. the lowest) number of features. FM _Windows81_Contrast_ (resp. _iOS5-Contrast_) has the maximum (resp. the minimum) number of contrast features. FM _Android51_TextSize_ (resp. _Android60_TextSize_) has the maximum (resp. the minimum) number of size and font type features. FM _Windows70_Zoom_ (resp. _Android51_Zoom_ and _Android60_Zoom_) has (resp. have) the maximum (resp. the minimum) number of Zoom features. Due to their size, input data and results cannot be entirely given in this paper. They are available on the gitlab repository ([[https://gite.lirmm.fr/AccessibiliteNumerique/osvisualaccessibilityoptions](https://gite.lirmm.fr/AccessibiliteNumerique/osvisualaccessibilityoptions)]([https://gite.lirmm.fr/AccessibiliteNumerique/osvisualaccessibilityoptions](https://gite.lirmm.fr/AccessibiliteNumerique/osvisualaccessibilityoptions)), accessed on 1 September 2021). During this evaluation, the concept lattice building has been achieved with the tool _RCAexplore_. RQ1 did not need extra tools for the analysis, as the results are easy to get visually. RQ2 and RQ3 required the use of an additional tool for extracting implications and mutex. We used the Java library CLEF [35].