Automated Quality Model Management Using Semantic Technologies

The starting point for this paper and service for the query-based generation of quality models was the requirement to be able to manage software quality models dynamically, as detailed domain knowledge is usually required for this task. We present new approaches regarding the query-based generation of software quality models and the creation of profiles for quality analyses using the code quality tool SonarQube. Furthermore, our support for the automatic assignment of software quality rules to entries of a hierarchical quality model simplifies the maintenance of the models with the help of machine learning models and large language models (HMCN and SciBERT in our case). The resulting findings were evaluated for their practical suitability using expert interviews. The results are promising and show that semantic management of quality models could help spreading the use of quality models, as it considerably reduces the maintenance effort.