On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based Tool Article Swipe
Narjes Bessghaier
,
Makram Soui
,
Christophe Kolski
,
Mabrouka Chouchane
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3410468
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1145/3410468
Smartphone users are striving for easy-to-learn and use mobile apps user interfaces. Accomplishing these qualities demands an iterative evaluation of the Mobile User Interface (MUI). Several studies stress the value of providing a MUI with a pleasing look and feel to engaging end-users. The MUI, therefore, needs to be free from all kinds of structural aesthetic defects. Such defects are indicators of poor design decisions interfering with the consistency of a MUI and making it more difficult to use. To this end, we are proposing a tool (Aesthetic Defects DEtection Tool (ADDET)) to determine the structural aesthetic dimension of MUIs. Automating this process is useful to designers in evaluating the quality of their designs. Our approach is composed of two modules. (1) Metrics assessment is based on the static analysis of a tree-structured layout of the MUI. We used 15 geometric metrics (also known as structural or aesthetic metrics) to check various structural properties before a defect is triggered. (2) Defects detection: The manual combination of metrics and defects are time-consuming and user-dependent when determining a detection rule. Thus, we perceive the process of identification of defects as an optimization problem. We aim to automatically combine the metrics related to a particular defect and optimize the accuracy of the rules created by assigning a weight, representing the metric importance in detecting a defect. We conducted a quantitative and qualitative analysis to evaluate the accuracy of the proposed tool in computing metrics and detecting defects. The findings affirm the tool’s reliability when assessing a MUI’s structural design problems with 71% accuracy.
Related Topics
Concepts
Computer science
Android (operating system)
Metric (unit)
Consistency (knowledge bases)
Human–computer interaction
Process (computing)
Dimension (graph theory)
Mobile device
Artificial intelligence
Data mining
Engineering
Mathematics
World Wide Web
Pure mathematics
Operations management
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3410468
- OA Status
- green
- Cited By
- 4
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3137424160
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3137424160Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3410468Digital Object Identifier
- Title
-
On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based ToolWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-15Full publication date if available
- Authors
-
Narjes Bessghaier, Makram Soui, Christophe Kolski, Mabrouka ChouchaneList of authors in order
- Landing page
-
https://doi.org/10.1145/3410468Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://uphf.hal.science/hal-03268483Direct OA link when available
- Concepts
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Computer science, Android (operating system), Metric (unit), Consistency (knowledge bases), Human–computer interaction, Process (computing), Dimension (graph theory), Mobile device, Artificial intelligence, Data mining, Engineering, Mathematics, World Wide Web, Pure mathematics, Operations management, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 38 |
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