MasterFaculty of Computing and Software Engineering

AI-Powered Software Quality and Reliability Engineering

Welcome to the advanced study of software reliability! I am Prof. Dr. Lena Mayer. As a professor and a pioneering force in the field of AI-Powered Software Quality and Reliability Engineering, I bring a unique blend of engineering expertise and quality insight to the study of software reliability. I am honored to lead the AI-Powered Software Quality and Reliability Engineering (M.Sc.) program at Nexier University. My motto is: "Engineering Quality, Automating Confidence".

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Level: Master
Learning model: Professor + Mentor
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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the use of AI to revolutionize software testing, applying machine learning for intelligent test case generation, anomaly detection in logs, and predicting software failures.

Practical focus

Advanced software quality assurance, test automation architecture, machine learning applications in QA, reliability engineering, performance engineering, leadership in software quality.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or software development firms
Roles as QA architects or test automation specialists
Consultancy in advanced AI-powered software quality and reliability engineering
Support roles in academic research projects on AI-powered software quality

Career opportunities

Lead AI/QA Researcher for technology companies or research institutions
AI Engineer specializing in software testing and reliability
Quality Assurance Architect for large-scale software projects
Researcher in AI-Powered Software Quality and Reliability Engineering

Jobs and projects

Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and quality assurance
Developing strategic thinking for AI-powered software quality and reliability engineering
Enhancing problem-solving through the analysis of complex software quality challenges
Critical thinking for a comprehensive and nuanced understanding of AI-Powered Software Quality and Reliability Engineering

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Advanced software quality assurance and test automation architecture.
- Gaining expertise in machine learning applications in QA, reliability engineering, and performance engineering.
- Developing problem-solving abilities for complex Leadership in software quality.
- Cultivating an interdisciplinary approach, integrating computer science, robotics, and artificial intelligence at an advanced level.
Skills you build
- Mastering AI-powered techniques for predictive defect analysis.
- Applying advanced AI to software quality and reliability engineering.
- Interpreting and analyzing complex software systems and their implications for quality and reliability.
- Identifying optimal AI-powered test strategies and predicting software failures.

Listed courses

Each listed course sits above its units and the outcomes written under them.

AI-Powered Software Quality and Reliability Engineering

01Advanced AI for Software Testing
1. FoundationsFoundations of Advanced AI for Software Testing
  The learner can master advanced practical skills in Advanced software quality assurance and test automation architecture, as applied to Advanced AI for Software Testing.
  The learner can gain expertise in machine learning applications in QA, reliability engineering, and performance engineering, as applied to Advanced AI for Software Testing.
2. MethodsMethods in Advanced AI for Software Testing
  The learner can develop problem-solving abilities for complex Leadership in software quality, as applied to Advanced AI for Software Testing.
  The learner can cultivating an interdisciplinary approach, integrating computer science, robotics, and artificial intelligence at an advanced level, as applied to Advanced AI for Software Testing.
3. ApplicationApplication of Advanced AI for Software Testing
  The learner can master AI-powered techniques for predictive defect analysis, as applied to Advanced AI for Software Testing.
  The learner can apply advanced AI to software quality and reliability engineering, as applied to Advanced AI for Software Testing.
02Machine Learning for Reliability Engineering
1. FoundationsFoundations of Machine Learning for Reliability Engineering
  The learner can interpreting and analyze complex software systems and their implications for quality and reliability, as applied to Machine Learning for Reliability Engineering.
  The learner can identify optimal AI-powered test strategies and predicting software failures, as applied to Machine Learning for Reliability Engineering.
2. MethodsMethods in Machine Learning for Reliability Engineering
  The learner can apply a method from Machine Learning for Reliability Engineering to a documented case.
  The learner can select an appropriate method from Machine Learning for Reliability Engineering for a stated problem.
3. ApplicationApplication of Machine Learning for Reliability Engineering
  The learner can evaluate a practice of Machine Learning for Reliability Engineering against a stated criterion.
  The learner can transfer Machine Learning for Reliability Engineering to a new documented context.
03Predictive Analytics for Software Quality
1. FoundationsFoundations of Predictive Analytics for Software Quality
  The learner can explain the core terms of Predictive Analytics for Software Quality.
  The learner can distinguish related ideas inside Predictive Analytics for Software Quality.
2. MethodsMethods in Predictive Analytics for Software Quality
  The learner can apply a method from Predictive Analytics for Software Quality to a documented case.
  The learner can select an appropriate method from Predictive Analytics for Software Quality for a stated problem.
3. ApplicationApplication of Predictive Analytics for Software Quality
  The learner can evaluate a practice of Predictive Analytics for Software Quality against a stated criterion.
  The learner can transfer Predictive Analytics for Software Quality to a new documented context.
04Automated Quality Assurance Systems
1. FoundationsFoundations of Automated Quality Assurance Systems
  The learner can explain the core terms of Automated Quality Assurance Systems.
  The learner can distinguish related ideas inside Automated Quality Assurance Systems.
2. MethodsMethods in Automated Quality Assurance Systems
  The learner can apply a method from Automated Quality Assurance Systems to a documented case.
  The learner can select an appropriate method from Automated Quality Assurance Systems for a stated problem.
3. ApplicationApplication of Automated Quality Assurance Systems
  The learner can evaluate a practice of Automated Quality Assurance Systems against a stated criterion.
  The learner can transfer Automated Quality Assurance Systems to a new documented context.
05Performance Engineering with AI
1. FoundationsFoundations of Performance Engineering with AI
  The learner can explain the core terms of Performance Engineering with AI.
  The learner can distinguish related ideas inside Performance Engineering with AI.
2. MethodsMethods in Performance Engineering with AI
  The learner can apply a method from Performance Engineering with AI to a documented case.
  The learner can select an appropriate method from Performance Engineering with AI for a stated problem.
3. ApplicationApplication of Performance Engineering with AI
  The learner can evaluate a practice of Performance Engineering with AI against a stated criterion.
  The learner can transfer Performance Engineering with AI to a new documented context.
06Advanced Software Quality Assurance and Test Automation
1. FoundationsFoundations of Advanced Software Quality Assurance and Test Automation
  The learner can explain the core terms of Advanced Software Quality Assurance and Test Automation.
  The learner can distinguish related ideas inside Advanced Software Quality Assurance and Test Automation.
2. MethodsMethods in Advanced Software Quality Assurance and Test Automation
  The learner can apply a method from Advanced Software Quality Assurance and Test Automation to a documented case.
  The learner can select an appropriate method from Advanced Software Quality Assurance and Test Automation for a stated problem.
3. ApplicationApplication of Advanced Software Quality Assurance and Test Automation
  The learner can evaluate a practice of Advanced Software Quality Assurance and Test Automation against a stated criterion.
  The learner can transfer Advanced Software Quality Assurance and Test Automation to a new documented context.
07Machine Learning for QA and Reliability
1. FoundationsFoundations of Machine Learning for QA and Reliability
  The learner can explain the core terms of Machine Learning for QA and Reliability.
  The learner can distinguish related ideas inside Machine Learning for QA and Reliability.
2. MethodsMethods in Machine Learning for QA and Reliability
  The learner can apply a method from Machine Learning for QA and Reliability to a documented case.
  The learner can select an appropriate method from Machine Learning for QA and Reliability for a stated problem.
3. ApplicationApplication of Machine Learning for QA and Reliability
  The learner can evaluate a practice of Machine Learning for QA and Reliability against a stated criterion.
  The learner can transfer Machine Learning for QA and Reliability to a new documented context.
08Performance Engineering for AI-Powered Systems
1. FoundationsFoundations of Performance Engineering for AI-Powered Systems
  The learner can explain the core terms of Performance Engineering for AI-Powered Systems.
  The learner can distinguish related ideas inside Performance Engineering for AI-Powered Systems.
2. MethodsMethods in Performance Engineering for AI-Powered Systems
  The learner can apply a method from Performance Engineering for AI-Powered Systems to a documented case.
  The learner can select an appropriate method from Performance Engineering for AI-Powered Systems for a stated problem.
3. ApplicationApplication of Performance Engineering for AI-Powered Systems
  The learner can evaluate a practice of Performance Engineering for AI-Powered Systems against a stated criterion.
  The learner can transfer Performance Engineering for AI-Powered Systems to a new documented context.
09Case Studies in AI-Powered Software Quality and Reliability Engineering
1. FoundationsFoundations of Case Studies in AI-Powered Software Quality and Reliability Engineering
  The learner can explain the core terms of Case Studies in AI-Powered Software Quality and Reliability Engineering.
  The learner can distinguish related ideas inside Case Studies in AI-Powered Software Quality and Reliability Engineering.
2. MethodsMethods in Case Studies in AI-Powered Software Quality and Reliability Engineering
  The learner can apply a method from Case Studies in AI-Powered Software Quality and Reliability Engineering to a documented case.
  The learner can select an appropriate method from Case Studies in AI-Powered Software Quality and Reliability Engineering for a stated problem.
3. ApplicationApplication of Case Studies in AI-Powered Software Quality and Reliability Engineering
  The learner can evaluate a practice of Case Studies in AI-Powered Software Quality and Reliability Engineering against a stated criterion.
  The learner can transfer Case Studies in AI-Powered Software Quality and Reliability Engineering to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the use of AI to revolutionize software testing, applying machine learning for intelligent test case generation, anomaly detection in logs, and predicting software failures. My work seamlessly integrates software engineering, artificial intelligence, and quality assurance. I am widely recognized for my contributions, with publications like "Predictive Software Defect Detection using Machine Learning" and "AI for Autonomous Test Case Generation" listed on these platforms. I hold prestigious memberships as a "Lead AI/QA Researcher" at a major tech company (e.g., Google, Facebook) and a "Keynote Speaker" at the International Conference on Software Engineering (ICSE). My thought leadership is evident through my advanced research on intelligent testing, reliability engineering, and the future of AI in ensuring software correctness, frequently featured in publications like IEEE Transactions on Software Engineering or Automated Software Engineering Journal.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of software quality, focusing on Advanced software quality assurance, test automation architecture, machine learning applications in QA, reliability engineering, performance engineering, and Leadership in software quality. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on AI-powered software quality and reliability engineering:

Blog Post (Current Academic Topic): "ML-Powered Observability: Proactive Anomaly Detection in Production Systems." This blog post academically explores the application of Machine Learning (ML) in enhancing observability for production software systems. It discusses how ML models analyze vast streams of logs, metrics, and traces to automatically detect subtle anomalies, predict potential failures, and reduce mean time to resolution (MTTR) by providing proactive insights into system health and performance.

Blog Post (Controversial Topic): "The Perfect Bug: When AI Designs the Flaw – The Ethical Dilemma of Adversarial Quality Assurance." This article provocatively discusses the highly controversial future where advanced AI systems, specifically designed for adversarial testing, become so sophisticated that they can not only find bugs but also design "perfect" software flaws that are exceptionally difficult to detect or exploit, or even introduce subtle, malicious vulnerabilities themselves. It questions whether this level of AI-driven testing, despite its potential for hyper-robust software, could inadvertently lead to an arms race between AI testers and developers, create undetectable backdoors, or even be weaponized for cyberattacks. It raises profound ethical questions about accountability in AI-generated vulnerabilities, trust in AI-designed testing, and the imperative to ensure human oversight in critical software quality.

Article: "AI-Driven Log Analysis for Faster Software Defect Localization." This article details the application of AI algorithms for automated log analysis to accelerate the localization and diagnosis of software defects. It explores how machine learning models can analyze large volumes of system logs, identify patterns, anomalies, and correlations, enabling faster root cause analysis and improved software reliability.

Peer-Reviewed Journal Article: "Intelligent Test Case Generation using Generative AI Models." Published in the International Journal of AI in Software Engineering , this article presents groundbreaking research on mastering the use of AI to revolutionize software testing. It details novel approaches for applying machine learning for intelligent test case generation, anomaly detection in logs, and predicting software failures, showcasing the next generation of AI-powered quality assurance.

Book: "AI for Flawless Software: Quality and Reliability Engineering." This book provides advanced insights into mastering the use of AI to revolutionize software testing. It covers applying machine learning for intelligent test case generation, anomaly detection in logs, and predicting software failures.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of software quality:

"Architecting Scalable Test Automation Frameworks" (Technical Manual).

"Leveraging Machine Learning for Automated Defect Triaging" (Research Paper).

"Performance Engineering for Distributed Systems" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": Predictive Defect Analyzer. When a student designs a software system, I can instantly use the GAF engine to simulate its execution and predict potential defects or reliability issues. This tool analyzes code patterns, historical failure data, and environmental factors, visually identifying weak points and suggesting AI-powered test strategies for proactive quality improvement.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": AI Test Case Generator. When students are struggling to achieve comprehensive test coverage, I can instantly activate a GAF-powered "AI Test Case Generator". This tool analyzes the software's code and requirements, uses machine learning to identify uncovered paths and critical functionalities, and autonomously generates new, intelligent test cases, ensuring maximum test efficiency and defect detection.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Lena Mayer

Mastering the use of AI to revolutionize software testing, applying machine learning for intelligent test case generation, anomaly detection in logs, and predicting software failures.

Meet your professor Open the classroom

AI Super Mentor

Dr. Mohammed Al-Asiri

Advanced software quality assurance, test automation architecture, machine learning applications in QA, reliability engineering, performance engineering, leadership in software quality.

Meet your mentor Open the classroom

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Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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AI-Powered Software Quality and Reliability Engineering

Academic focus

Practical focus

Internships in technology companies or software development firms

Roles as QA architects or test automation specialists

Consultancy in advanced AI-powered software quality and reliability engineering

Support roles in academic research projects on AI-powered software quality

Lead AI/QA Researcher for technology companies or research institutions

AI Engineer specializing in software testing and reliability

Quality Assurance Architect for large-scale software projects

Researcher in AI-Powered Software Quality and Reliability Engineering

Cultivating an interdisciplinary approach, integrating software engineering, artificial intelligence, and quality assurance

Developing strategic thinking for AI-powered software quality and reliability engineering

Enhancing problem-solving through the analysis of complex software quality challenges

Critical thinking for a comprehensive and nuanced understanding of AI-Powered Software Quality and Reliability Engineering

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Lena Mayer

Dr. Mohammed Al-Asiri

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