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Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review

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Поле Значение
 
Заглавие Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review
 
Автор Jabborov, A.
Kharlamova, A.
Kholmatova, Z.
Kruglov, A.
Kruglov, V.
Succi, G.
 
Тематика AI SYSTEM EVALUATION
AI-BASED SOFTWARE
ARTIFICIAL INTELLIGENCE
FEATURE SELECTION
INTELLIGENT SYSTEMS
MACHINE LEARNING
QUALITY ASSESSMENT
QUALITY MODELS
SOFTWARE ATTRIBUTES
APPLICATION PROGRAMS
FEATURE SELECTION
LEARNING SYSTEMS
QUALITY ASSURANCE
QUALITY CONTROL
TAXONOMIES
AI SYSTEM EVALUATION
AI SYSTEMS
AI-BASED SOFTWARE
FEATURES SELECTION
MACHINE-LEARNING
MEASURABLE PROPERTY
PROPERTY
QUALITY ASSESSMENT
QUALITY MODELING
SOFTWARE
SOFTWARE ATTRIBUTE
SOFTWARE MEASUREMENT
SOFTWARE-SYSTEMS
SYSTEM EVALUATION
SYSTEMATIC
VALIDATION TECHNIQUE
INTELLIGENT SYSTEMS
 
Описание The increasing integration of AI software into various aspects of our daily lives has amplified the importance of evaluating the quality of these intelligent systems. The rapid proliferation of AI-based software projects and the growing reliance on these systems underscore the urgency of examining their quality for practical applications in both industry and academia. This systematic literature review delves into the study of quality assessment metrics and methods for AI-based systems, pinpointing key attributes and properties of intelligent software projects that are crucial for determining their quality. Furthermore, a comprehensive analysis of this domain will enable researchers to devise novel methods and metrics for effectively and efficiently evaluating the quality of such systems. Despite its importance, this area of development is still relatively nascent and evolving. This paper presents a systematic review of the current state of the taxonomy of quality assessment for AI-based software. We analyzed 271 articles from six different sources that focused on the quality assessment of intelligent software systems. The primary objective of this work is to provide an overview of the field and consolidate knowledge, which will aid researchers in identifying additional areas for future research. Moreover, our findings reveal the necessity to establish remedial strategies and develop tools to automate the process of identifying appropriate actions in response to abnormal metric values. © 2013 IEEE.
Russian Science Foundation, RSF: 22-21-00494
This work was supported by the Russian Science Foundation under Grant 22-21-00494.
 
Дата 2024-04-05T16:36:54Z
2024-04-05T16:36:54Z
2023
 
Тип Article
Journal article (info:eu-repo/semantics/article)
|info:eu-repo/semantics/publishedVersion
 
Идентификатор Jabborov, A, Kharlamova, A, Kholmatova, Z, Kruglov, A, Kruglov, V & Succi, G 2023, 'Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review', IEEE Access, Том. 11, стр. 130491-130507. https://doi.org/10.1109/ACCESS.2023.3333920
Jabborov, A., Kharlamova, A., Kholmatova, Z., Kruglov, A., Kruglov, V., & Succi, G. (2023). Taxonomy of Quality Assessment for Intelligent Software Systems: A Systematic Literature Review. IEEE Access, 11, 130491-130507. https://doi.org/10.1109/ACCESS.2023.3333920
2169-3536
Final
All Open Access, Gold
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178066293&doi=10.1109%2fACCESS.2023.3333920&partnerID=40&md5=560bfd8597208116a9c9f6f5d41d4ef0
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10320363.pdf
http://elar.urfu.ru/handle/10995/130995
10.1109/ACCESS.2023.3333920
85178066293
001115910300001
 
Язык en
 
Связанные ресурсы info:eu-repo/grantAgreement/RSF//22-21-00494
 
Права Open access (info:eu-repo/semantics/openAccess)
cc-by-nc-nd
https://creativecommons.org/licenses/by-nc-nd/4.0/
 
Формат application/pdf
 
Издатель Institute of Electrical and Electronics Engineers Inc.
 
Источник IEEE Access
IEEE Access