Microservice architecture is favoured for its significant scalability, independent evolution, and advantages in performance elasticity. Partitioning a monolith into microservices has become a pivotal issue in software architecture refactoring. Concurrently, assessing the quality of such partitioning also presents a significant challenge. To address this problem, we propose a solution that (1) proposes a method for extracting and representing the multi-source features such as semantics, functionality, and performance of monolithic systems; (2) designs a deep fusion graph clustering model for partitioning a monolith into microservices intelligently; and (3) establishes a comprehensive set of assessment metrics to quantify the quality of the partitioning suggestion. We conducted experiments and analyses on five benchmark projects. By comparing our approach with six other methods, we have demonstrated the advantages of our methodology. Furthermore, ablating different modules has validated the effectiveness of our proposed monolith features analysis and deep fusion graph clustering model.

Finding a parking space can be very stressful and time consuming. A variety of different vehicle parking applications have been developed but many fail to support diverse end-users. We captured diverse human-centric issues from user reviews and literature, and then created personas that encompass a wide representative range of parking app user groups. Using these personas, user stories were created, categorized and parking app tasks prioritized. We used these to develop a prototype new "smart parking app". A cognitive walk-through was employed using each of the personas and user stories to evaluate the app. With more human-centric factors taken into account in the design and development of the app, we found that majority of the human-centric frustrations identified were resolved, when compared with a commonly used parking app.