Teaching
• Scientific Writing and Argumentation
The course provides an introduction to scientific writing, techniques for reference management, information
retrieval, literature review, source criticism, information evaluation, and oral presentation.
• Statistics for Empirical Software Engineering
The course provides an introduction to statistics using R. The goal of this course is to provide
the students with the required knowledge to perform statistical analysis when conducting empirical
software engineering projects.
• Software Architecture
The course provides an introduction to software architecture. It is tailored for software development
practitioners at organizations. The learning objectives are to: (i) learn basic principles of software
architecture, (ii) understand decomposition and modularity, (iii) learn strategies for migrating from
Monoliths to Micro-services, and (iv) learn about architecture documentation and communication.
• Software Engineering for Data-Intensive AI Applications
This project course addresses issues relevant for software engineering for systems that use artificial intelligence (AI) techniques such as machine learning or large-scale parallel data processing.
This includes a discussion of the value that can be created through the use of AI.
At the same time, technological and architectural foundations of software systems using AI techniques and handling large amount of data are discussed.
• Fundamentals of Software Architecture
The aim of this course is to equip students with basic knowledge and skills about software architecture design and its role in the development and maintenance of software systems.
This course covers various definitions of software architecture. We discuss the role and relevance of software architecture at different stages of development projects;
we discuss the relation of architecture to requirements and system implementation; we discuss software architecture quality properties; we discuss various architectural styles and tactics;
we discuss general software design principles; we discuss how to document and how to evaluate software architectures.
• Software Evolution
The purpose of this course is to familiarise the student with situations, activities, and techniques typical for software evolution, such as adding a new feature, refactoring,
automating variability or testing, improving performance, re-balancing non-functional requirements, and so on. Furthermore, students learn to plan, implement, and reflect on software evolution scenarios and improvements.
• Software Engineering Project
The course provides a practical introduction to Software Engineering. Students work on an open problem that is defined by stakeholders outside of the students' team.
That means that students will not be able to define the project they work on themselves. In order to address this challenge, students will learn:
- a software development process to structure their work,
- how to specify and evaluate requirements and the collaboration with stakeholders to assure that was is being delivered is perceived as valuable,
- new technologies and tools and fitting ways to use them in order to realise the value proposition based on the students' own learning strategies,
- how to organise themselves in a team in order to reach a joint goal with limited resources, and
- how to reflect on their own work and learning to enable a continuous improvement of their way of working.
• Model Driven Software Development
This course describes object-oriented system development by applying UML models. To do this, it shows how to move from informal descriptions
of systems to formal models, and how to relate these models to each other and to code. This means both learning the fundamentals of UML, but also how to reason in object-oriented problem solving.
