Email this article Multi-criteria quality assessment system for application program user interfaces using artificial intelligence methods
- Authors: Tagirova L.F.1
-
Affiliations:
- Orenburg State University
- Issue: Vol 32, No 6 (2026)
- Pages: 309-329
- Section: Intelligent systems and technologies
- Published: 09.06.2026
- URL: https://journals.eco-vector.com/1684-6400/article/view/708393
- DOI: https://doi.org/10.17587/it.32.309-319
- ID: 708393
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Abstract
The author has developed a system for multi-criteria assessment of the quality of application software user interfaces based on artificial intelligence methods. The proposed system is based on a fuzzy expert model that implements the Mamdani inference mechanism, which provides an objective assessment of the user interface according to key criteria: accessibility, adaptability, functionality and navigation structure. Based on the analysis of input parameters, the system determines the final level of interface quality and generates recommendations for improving the evaluated interfaces. The results of the study may be of interest to specialists involved in the development, design and evaluation of user interfaces in various subject areas.
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About the authors
L. F. Tagirova
Orenburg State University
Author for correspondence.
Email: LG-77@mail.ru
Cand. of Pedagog. Sc., Associate Professor
Russian Federation, OrenburgReferences
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Supplementary files
Supplementary Files
Action
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JATS XML
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Fig. 1. Information flow diagram
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Fig. 2. Functional model of the interface quality assessment process
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Fig. 3. Structure of a fuzzy expert system for determining the interface quality level
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Fig. 4. Graph of the membership function of the fuzzy set "Adaptability"
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Fig. 5. Graph of the membership function of the fuzzy set "Interface Quality Level"
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Fig. 6. Use case diagram
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Fig. 7. List of evaluation criteria for testing
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Fig. 8. Result of the analysis of the "Availability" criterion
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Fig. 9. Result of the analysis of the "Adaptability" criterion
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Fig. 10. Result of the analysis of the "Functionality" criterion
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Fig. 11. The result of the analysis of this interface and the final recommendation
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Fig. 12. Results of the interface quality assessment at MBOU Secondary School No. 66
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Fig. 13. Recommendations for improving the interface quality
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Fig. 14. Application window for the user "Head of Testing Department"
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Fig. 15. Rule base editing window
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Fig. 16. Viewing the results of determining the interface quality level
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Fig. 17. Viewing the operation of the Mamdani algorithm
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