Methods and Models for Software Quality Assessment in Information and Communication Systems

This paper addresses the challenges of software quality assessment in Information and Communication Systems (ICS), a critical aspect of ensuring reliable and secure communication in modern networks. Traditional models such as ISO/IEC 25010 and CMMI provide a solid foundation for quality assessment but often fall short in meeting the specific needs of ICS, which require high performance, fault tolerance, and scalability. By comparing approaches from various industries, including healthcare, automotive, aerospace, and telecommunications, this study highlights the need for a combined approach that integrates general models with industry-specific metrics. The paper also explores the role of Artificial Intelligence AI and Machine Learning ML in enhancing the accuracy and efficiency of quality assessment processes. The findings suggest that an integrated approach, leveraging both traditional methodologies and advanced technologies, is essential and enlightening for maintaining high software quality in ICS and meeting the growing demands of modern communication systems.