Course content

This course builds on foundational concepts of software engineering and focuses on an in-depth discussion and application of contemporary software engineering practices in the context of development of secure and robust software systems. The course takes into account state-of-the-art theory, professional practice, as well as cross-cutting aspects such as compliance, ethics and sustainability. This course targets graduate students that have a foundational understanding and experience with software engineering and development.

This course covers the following topics:

• Concepts
  • Introduction to responsible software engineering
  • Architecture modelling: languages and principles
  • Principles of requirements engineering
• Process
  • Development processes and collaboration modes
  • Design of secure software and system architectures
  • Implementation of requirements engineering
• Quality
  • Software quality and trustworthiness, including trade-off analyses
  • Risk management
  • Software testing and validation
• Emerging topics in software engineering
  • AI-enabled software engineering

Learning outcome

Knowledge

• Explain key concepts of responsible software engineering, including ethical, social, and professional responsibilities.
• Describe and apply principles of risk management in the context of software engineering projects.
• Analyze and justify different software quality attributes (including security) based on project-specific characteristics and constraints.
• Explain domain-specific modeling approaches (e.g., risk modelling, quality modelling, service modelling, feature modelling)
• Understand and apply AI-related concepts such as machine learning and agentic engineering
• Develop and customise architectural models using different architectural views and paradigms.
• Perform and evaluate trade-off analyses in architectural design to compare and justify architectural decisions.

Skills

• Plan, design, and manage secure and scalable software-enabled systems using appropriate engineering processes and tools.
• Critically select and apply AI techniques and tools to support the design, development, and validation of software systems.
• Evaluate human- or AI-generated architecture, code, and tests against requirements, including security, quality standards, and system constraints.
• Plan and execute systematic risk assessments, documenting risks, mitigation strategies, and decision rationales in the context of software engineering.

General Competences

• Demonstrate the ability to justify the professional application of state-of-the-art software engineering principles in project work and decision-making.
• Apply and reflect on best practices for inclusive, collaborative, and efficient teamwork in software engineering contexts.
• Recognize and explain dilemmas and analyze trade-offs as part of the software engineering process.
• Integrate and evaluate human-centered principles and best practices (e.g., ethics, usability, communication, stakeholder engagement) throughout the software engineering process.

Learning methods and activities

• Lectures
• Class presentation
• Group work
• Reflection
• Supervision

Specific conditions