Fig 7 - uploaded by Raul Medeiros
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Functional Testing. FeatureCloud helps identify hot-spot features, i.e., features undergoing substantial changes in their codebase from the last release of the SPL platform. FeatureCloud takes the code churn, and extracts the pre-compilation directives from the associated ifdef blocks. On these grounds, the tool generates a word-cloud using feature names as words, and pre-compilation directives as phrases. Nodes stand for features. Color opacity, font size, and distribution of nodes denote the size of the change, the scattering degree and the tangling degree, respectively, for the feature at hand. Take the case of feature AUTO_BED_LEVELING_UBL (right-upper corner in the screenshot). From the last release, this feature has been subject to few changes (light-blue color). Yet, testing is guessed to be complicated. First, this feature's changes are scattered along distinct ifdef blocks (as reflected by the medium font size). Second, these changes pertain to ifdef blocks whose pre-compilation directives involve feature AUTO_BED_LEVELING_UBL but also other five features that are depicted in the same radial branch, e.g., EEPROM_SETTING. Figure credits [S7]. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Source publication
Context
Variant-Rich Systems (VRSs), such as Software Product Lines or variants created through clone & own, are created to satisfy different needs while reusing existing assets. The long lifespan of families of variants, and the scale of both the technical side (implementation size) and the organizational side (roles diversity) make their maintena...
Contexts in source publication
Context 1
... Fig. 8 presents a bubble chart to analyze optimal variants: variants are located based on four custom non-functional metrics (battery-life, security, productivity and ...
Context 2
... Visualized elements (RQ2.1) Table 6 displays the results: 'features', 'variants', and 'implementation assets' (e.g., source code classes) are the most popular. These elements seem to satisfy the analysis needs of the most investigated scenarios (see Section 6.2), i.e., 'Variant Synchronization' and 'Feature Identification and Location'. Fig. 8) are by far the most popular visualization techniques. This coincides with visualizations for oneoff development [10]. In total, we found 11 different visualization techniques. We note that 16 primary sources implemented more than one: 'heatmap' and 'enriched software diagram' (see Fig. 9) [S9]; 'tabular/matrix' and 'enriched software ...
Context 3
... 'enriched software diagram' (see Fig. 9) [S9]; 'tabular/matrix' and 'enriched software diagram' [S26]; 'tabular/matrix' and 'line chart' [S11]; 'alluvial diagram' and 'bar chart' (see Fig. 2) [S2]; 'tabular/matrix' and 'bar chart' [S31,S14]; 'tabular/matrix', 'bubble chart', 'treemap' and 'heatmap' [S36]; 'tabular/matrix' and 'bubble chart' (see Fig. 8) [S8]; 'nodelink diagram ' and 'tabular/matrix' [S34,S40]; 'bar chart', 'heatmap' and 'treemap' (see Fig. 3) [S3]; 'tabular/matrix ' and 'heatmap' [S16]; 'bar chart' and 'nodelink diagram' (see Fig. 1) [S1]; 'nodelink diagram' and 'word cloud' (see Fig. 7) [S7]; 'tabular/matrix', 'bar chart' and 'enriched software diagram' (see Fig. 5) ...
Citations
... The visualization aspect is essential as it enables decision makers to analyze the proximity of the optimal solution [97][98][99][100][101]. Using different types of visualizations will increase the visibility of the algorithm [100,[102][103][104][105] and deriving various additional data will help the analysis process [93,100,106]. ...
... The visualization aspect is essential as it enables decision makers to analyze the proximity of the optimal solution [97][98][99][100][101]. Using different types of visualizations will increase the visibility of the algorithm [100,[102][103][104][105] and deriving various additional data will help the analysis process [93,100,106]. ...
... The visualization aspect is essential as it enables decision makers to analyze the proximity of the optimal solution [97][98][99][100][101]. Using different types of visualizations will increase the visibility of the algorithm [100,[102][103][104][105] and deriving various additional data will help the analysis process [93,100,106]. ...
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