State-of-the-Art Review of Taxonomies for Quality Assessment of Intelligent Software Systems
Arina Kharlamova, Ahror Jabborov, Artem V. Kruglov, Giancarlo Succi · 2022
As the number of AI-based software projects is increasing drastically, frequent use and dependency on these projects raise an alarm to look into the quality of such systems which is essential for their practical usage in the industry as well as in the academy. In addition, deeper look into this field will provide researchers the opportunity to develop new methods and metrics to effectively and efficiently assess the quality of such systems. However, this area of development is considered to be relatively new and emerging, thus, it hasn’t well-explored and therefore creates a large demand for collaborative researches in this area. This preliminary review of existing focuses on the study of quality assessment metrics and methods for AI-based systems, identifying attributes and properties of intelligent software projects that play an important role in assessing the quality of such systems.