The role of ontologies in software engineering has attracted the attention of software engineers, architects, practitioners, and academia. The continuous evolution of ontology and its application in software engineering life cycle management are attributed to various metrics, including quality, reuse, usability, usefulness, and performance. Most of the metrics used in software engineering methods traditionally emerge from the systems engineering requirements. Normally, systems engineering as a discipline provides desired directions for the software engineering practitioners to develop artifacts for use. Thus, complexity in handling software engineering life cycles arises because of dynamic deliverables imposed through systems requirements engineering at the macro level. Ontology-based software engineering practices have supported the development of agile systems to meet the dynamic requirements of systems engineering with a higher degree of reuse and with potential for quality assurance.

This paper on bringing various ontology modules through cohesion and coupling metrics is quite contemporary in meeting the needs for the development of agile systems. Importantly, the paper includes the principles of cohesion and coupling of software engineering practices. One of the most striking deliverables in this paper is the use of quantitative methods in establishing relationships among various ontology modules and in validating the outcomes through coupling and cohesion metrics.

However, this research fails to appropriately investigate the results obtained through cohesion and coupling metrics compared to other approaches, such as goal, question, metric (GQM) and other International Function Point User Group (IFPUG) metrics-based approaches. Referencing the results obtained through this ontology module coupling with ISO metrics (especially deliverables indicated in ISO/IEC 15939, ISO/IEC 14598-1, and ISO/IEC 9126-1) could have provided indicative performance benchmarks. Moreover, the case study that showcases possible applications of the proposed metrics should have been explained in detail through quantitative methods for better understanding. That being said, the paper will keep readers interested in deploying quantitative methods in software engineering research and applications.