Module: Software Systems and Middleware (Part 2)
| Study level | Master |
|---|---|
| ECTS credits | 1 ECTS |
| Study forms | Hybrid or fully online |
| Module aims | The aim of the module is to introduce software verification, validation and testing methods for autonomous, cyber-physical and AI-based systems. The course develops students’ ability to plan, implement and assess V&V strategies across physics-based and data-driven software, in line with relevant safety and governance standards. |
| Pre-requirements | Basic knowledge of software engineering, control or embedded systems and programming skills. Familiarity with system design, testing methodologies, AI/ML concepts or safety-related standards is recommended but not mandatory. |
| Learning outcomes | Knowledge • Explain the principles of V&V in both physics-based and decision-based execution systems. • Describe software testing frameworks, including component, integration, and system-level approaches. • Understand regulatory standards and their role in defining safety and assurance levels. • Analyze challenges in AI component validation, including training set verification, robustness testing, and anti-specification frameworks. Skills • Develop and execute structured test plans and coverage analyses for complex, data-driven systems. • Use simulation tools to generate and evaluate test scenarios for AI-based and safety-critical applications. • Apply V&V techniques to assess software reliability and traceability across development lifecycles. • Critically evaluate AI model performance using robustness, fairness, and explainability metrics. Understanding • Appreciate the philosophical and practical differences between deterministic and non-deterministic testing paradigms. • Recognize the ethical and governance implications of AI deployment in safety-critical systems. • Demonstrate interdisciplinary reasoning across engineering, regulatory, and societal domains when designing and testing autonomous software systems. |
| Topics | 1. Verification and Validation Fundamentals: – Overview of PBE vs DBE paradigms, fault analysis, and safety argument structures. – Introduction to structured testing: unit, integration, and system-level testing. 2. Safety-Critical Standards and Governance: – ISO 26262 (Automotive), AS9100 (Aerospace), and CMMI frameworks. – Automotive Safety Integrity Levels and Design Assurance Levels. 3. Software Testing and Coverage: – Code coverage, pseudo-random test generation, and scenario-based validation. – Role of simulation, fault injection, and test automation. 4. AI Component Validation: – AI vs Software validation differences; coverage, code review, and data governance. – Training set validation, robustness to noise, and explainable AI. 5. Specification and Anti-Specification Challenges: – IEEE 2846 and AI driver concepts; ethical, legal, and liability considerations. – Human-equivalent testing and performance evaluation frameworks. 6. Emerging V&V Trends: – Continuous integration, simulation-in-the-loop, and AI-assisted verification. – Case studies: Automotive ADAS, aviation autonomy, and robotics. |
| Type of assessment | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation. |
| Learning methods | Lecture — Present theoretical underpinnings of software and AI testing, covering safety-critical standards and AI V&V challenges. Lab works — Practical exercises in automated testing, simulation-driven validation, and robustness evaluation using Python/ROS/MATLAB. Individual assignments — Develop and analyze test strategies, evaluate compliance with ISO/IEEE frameworks, and submit technical reports. Self-learning — Review international standards, research literature, and case studies of AI validation in autonomous domains. |
| AI involvement | AI tools can assist in generating test cases, simulating complex operational scenarios, and analyzing coverage gaps. Students must validate AI-generated results, maintain traceability, and document AI involvement transparently in compliance with academic ethics. |
| Recommended tools and environments | ROS, MATLAB |
| Verification and Validation focus | |
| Relevant standards and regulatory frameworks | ISO 26262, AS9100, CMMI, IEEE 2846 |
