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| en:safeav:curriculum:softsys-m [2025/11/04 14:53] – raivo.sell | en:safeav:curriculum:softsys-m [2025/11/05 09:15] (current) – airi |
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| ^ **Study forms** | Hybrid or fully online | | ^ **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. | | ^ **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 in Python, C++ or MATLAB. Familiarity with system design, testing methodologies, AI/ML concepts or safety-related standards is recommended but not mandatory. | | ^ **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 verification and validation (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 (PBE) and non-deterministic (DBE) 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. | | ^ **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 (ASIL) and Design Assurance Levels (DALs).\\ 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 (CI/CD), simulation-in-the-loop (SIL/HIL), and AI-assisted verification.\\ – Case studies: Automotive ADAS, aviation autonomy, and robotics. | | ^ **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. | | ^ **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. | | ^ **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. | |
