Systematic guidelines for software modelling: an empirical study on enhancing modelling education and training

Abstract

Background: Software modelling holds significant promise for enhancing various aspects of software and systems engineering, including productivity and cost efficiency. Despite these advantages, its widespread adoption across the entire field remains limited. Extensive research has explored the reasons behind this limited adoption, uncovering issues such as subpar code generation, inadequate tool support, and a lack of guidance or training. Specifically, it has been suggested that engineers are reluctant to embrace modelling because it requires excessive effort and offers little usefulness, a view shaped by their educational background.

Aim: Overall, our goal is to conduct an empirical investigation with university students, understanding their perception of software modelling during their university studies. We aim to explore students' challenges with modelling assignments, tools, and the modelling content taught in courses. Additionally, we want to understand which aspects of modelling students find beneficial for learning and carry with them into their future academic or industry careers. Finally, we aim to create systematic guidelines for software modelling to be used by both students and instructors.

Method: To achieve our goal, we conducted several empirical studies with university students, teaching assistants, and instructors. We collected data through interviews, surveys, and observation studies. To evaluate the effectiveness of the systematic guidelines, we applied them to university courses where modelling was taught.

Results: The results described in the thesis are twofold: first, we present university students' perceptions of modelling, and then we describe the guidelines we created based on that perception. Our results show that students recognise the benefits of modelling, such as using models for planning and group communication, but their understanding is hindered by unclear assignment expectations, irregular and insufficient feedback, and lack of experience with problem domains. Finally, we conclude our thesis with systematic guidelines that will help students enhance their modelling skills and knowledge, guiding them to apply this knowledge in real-world industry settings.

Conclusion: Our results can potentially enhance education and training in software modelling, benefiting both academic settings and industrial environments. The modelling guidelines encourage students and instructors to follow a structured approach, starting from understanding a modelling problem to selecting a suitable modelling strategy based on the problem domain and tools and ultimately interpreting the resulting model. The guidelines also assist instructors in providing regular and systematic feedback on students' modelling efforts. The guidelines improve communication between students and the course by clearly outlining expectations and values for modelling assignments, which reflects the value of modelling in different future endeavours.