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| en:safeav:curriculum:softsys-m [2025/09/24 13:31] – created larisas | en:safeav:curriculum:softsys-m [2025/11/05 09:15] (current) – airi |
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| ====== Module: Software Systems and Middleware (Part 2) ====== | ====== Module: Software Systems and Middleware (Part 2) ====== |
| | **Study level** | Bachelor 1 || | |
| | **ECTS credits** | 3-6 || | ^ **Study level** | Master | |
| | **Study forms** | Hybrid or fully online || | ^ **ECTS credits** | 1 ECTS | |
| | **Module aims** | to be added || | ^ **Study forms** | Hybrid or fully online | |
| | **Pre-requirements** | Motivation to study AV, recommended to have basics on programming, electronics and mechatronics || | ^ **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. | |
| | **Learning outcomes** | After completing this module (for every topic listed below), the student:\\ - knows x\\ - knows y\\ - understands z\\ - can w || | ^ **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. | |
| | ** Topics ** | __Topic AV1 __ (1 ECTS) \\ \\ __Topic AV2 __ (2 ECTS)) \\ \\ __Topic AV3 __ (2 ECTS)\\ \\ __Topic AV4 __ (1 ECTS) \\ || | ^ **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. | |
| | **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 || | ^ **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. | |
| | **Learning methods** | to be added || | ^ **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. | |
| | **AI involvement** | Explicit list of AI tools and application mtehods | | | ^ **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. | |
| | **References to\\ literature** | to be added || | ^ **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. | |
| | **Lab equipment** | to be added || | ^ **Recommended tools and environments** | ROS, MATLAB | |
| | **Virtual lab** | to be added || | ^ **Verification and Validation focus** | | |
| | **MOOC course** | MOOC Courses hosting for SafeAV, IOT-OPEN.EU Reloaded, and Multiasm grants: http://edu.iot-open.eu/course/index.php?categoryid=3 || | ^ **Relevant standards and regulatory frameworks** | ISO 26262, AS9100, CMMI, IEEE 2846 | |
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