Visual Studio Code Experts

AI Readiness - Part 3: Creating an e-book on the use of AI in software testing

• Designed and created an e-book on the sensible and safe use of AI in software testing from the perspective of an experienced test expert.
• Used generative AI (ChatGPT, Perplexity Pro) as a sparring partner for structuring, wording suggestions and creating variations, with all content finally reviewed and approved using my own expertise.
• Developed practical insights into the opportunities and risks of AI in testing, with a special focus on collaboration between testers and AI instead of “tester vs. AI”.
• Title: “11 starke Gründe: Dream-Team statt Rivalen - Tester und KI sind gemeinsam unschlagbar”
• Subtitle: “Wie Mensch und Maschine im Softwaretest das perfekte Team bilden – und warum reines KI-Testing riskant ist.”
• Planned use of the e-book as a basis for workshops, customer meetings or internal training.
• Technology: Generative AI, ChatGPT, Perplexity Pro, Microsoft Word, Adobe Acrobat, many years of hands-on professional testing experience, common sense.

• Extension of a Swing-based UI for recording Security Restricted Articles during baggage screening.
• Introduction of a new workflow for recording Deboarding Events.
• Extension of the reporting system for Security Restricted Articles and Deboarding Events.
• Implementation using JBoss Wildfly, PostgreSQL, spring-boot 3.x, Java 8/21, Swing, JSF, JAX-RS/Resteasy, Docker, Maven, Kiro.

Developed the product vision and outcome strategy for a new registry platform, designed a management workshop for strategic alignment, conducted BPMN process mapping across systems and roles, analyzed user story practices and further developed them on an outcome basis, and brought together stakeholders from business departments, management and the external development partner.

Skills: Product Strategy, Outcome Roadmapping, Stakeholder Workshops, BPMN/Process Analysis, Miro, Linear

Further Development of the Integrated Capacity Management Platform InKa

Rail Infrastructure

• Power BI shared datasets and reports via DirectQuery on Amazon Redshift (timetable, construction and capacity data)
• Automated report testing with Playwright in Docker containers, version control with GitLab
• Concept and implementation of user communication (information page, changelog, known bugs)

Initial Situation:

The integrated capacity management platform (InKa) is a monitoring and analysis platform for data from timetable and capacity management: customer orders, infrastructure, timetable operations, construction, as well as route capacity and width. The data is stored and linked in a structured data warehouse. This makes it possible to create complex KPIs across different source systems and display current and future route utilization as well as construction activity across the network. The goal is to improve strategic, medium-term and intra-year planning and provide users with easy access to capacity management analyses.

Measures:

• Enablement: Introduction of Power BI with training and workshops for development teams.
• Consulting & Design: Problem analysis, concepts and solution consulting for reporting requirements; implementation of reporting solution plans.
• Data Model: Development of shared datasets with DirectQuery on Amazon Redshift and import tables; DAX measures for utilization and capacity KPIs.
• Development: Data analysis, data transformation and visualization in Power BI; extension and migration of business applications to Power BI; AI-supported development in VS Code with GitHub Copilot.
• Test Automation: Automated testing of Power BI reports with Playwright; setup of the test environment in Docker containers.
• User Communication: Concept and implementation of a central information page with hot news, changelog and known bugs, access from reports with report filters, updates via Power Automate.
• Deployment: Management of Power BI Deployment Pipelines across the DEV, TEST and PROD environments.
• Agile Working: Work in an agile team according to SAFe (Scaled Agile Framework); refinement of user stories and features including effort estimates in Jira; version control with Git and GitLab.
• Quality & Documentation: Reviews for quality assurance, error analysis and resolution, technical documentation in AsciiDoc and Confluence.

Technologies: Power BI, DAX, Power Query, DirectQuery, Amazon Redshift, Git / GitLab, Deployment Pipelines, Playwright, Docker, AsciiDoc, Power Automate, Jira, Confluence, VS Code with GitHub Copilot, SAFe

Results:

• Capacity management KPIs from multiple source systems combined on one platform
• Transparency regarding current and future route utilization and construction activity across the network
• Easy access to analyses for users
• Automated regression testing of Power BI reports
• User communication implemented: central information page with hot news, changelog and known bugs, directly accessible from the reports

• Planned and developed a CPU monitoring application for monitoring system performance and resource utilization.
• Designed and implemented a Spring Boot backend providing a REST API for processing and exposing monitoring data.
• Developed the React frontend for presenting monitoring information in a clear and user-friendly interface.
• Integrated PostgreSQL for persistent storage and management of application data.
• Containerized the application and its services using Docker Compose.
• Automated infrastructure provisioning and deployment using Terraform on AWS.
• Structured the application as a modern, maintainable system using REST-based communication between frontend and backend.
• Designed and developed a secure, scalable CPU monitoring architecture (cpu-watcher) with a dedicated collector application that streams monitoring data to the backend, reducing direct exposure of system resources.
• Designed a secure cloud infrastructure with the database isolated within a private network and OIDC-based authentication.
• Implemented Infrastructure as Code with Terraform and integrated version-controlled CI/CD pipelines to automate testing, infrastructure changes, and application deployments.
• Designed and implemented the frontend delivery architecture using AWS CloudFront.

Stack: Spring Boot · React · PostgreSQL · REST API · Docker Compose · Terraform · AWS

• Industry: Insurance, Trade, IT
• Customers & Projects: Consulting and selling insurance and similar services
• Main tasks: Insurance consulting (health insurance, retirement planning, wealth building); commercial services, inside and field sales; sales data analysis and forecasting; customer consulting and support; opening a new sales headquarters for private and business customers; business development & innovation management; business use case development; process optimization; stakeholder management; team leadership and training; preparation and delivery of trainings;
• Technologies used: Microsoft Office 365, Microsoft Teams, Jira, Draw.IO, Camunda 8, Java (8, 17,21,25), Git, Spring Boot, Spring Batch, Spring Data REST, Spring Web, Spring Security, J-Unit, Playwright, Lombock, Vaadin, H2, PostgreSQL (16, 17 18), pgAdmin, Docker, LLMs, JasperSoft Studio, JasperReports

Reporting and controlling using Power BI for a production ERP system

• Analysis of ERP data and interfaces for use in Power BI dashboards
• Evaluation and migration of existing reports (e.g. Excel) to Power BI
• Development of an authorization concept for selective data access
• Documentation and training on the use and customization of Power BI dashboards

Label: Power BI, Excel, SelectLine ERP, Microsoft SQL, SQL Server Management Studio

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

As a Council Member at GLG (Gerson Lehrman Group), the world’s leading expert network, I provide organizations, investment firms, and consulting companies with expert discussions on demand – wherever specialized knowledge in information security, IT governance, and regulatory compliance is needed.

My assessments are based on many years of practical experience in highly regulated industries: from building and implementing information security management systems (ISMS according to ISO 27001), IT risk and vulnerability management, and business continuity (ISO 22301) to the practical implementation of European regulations such as DORA, NIS2, BAIT, and the Cyber Resilience Act (CRA).

The cooperation is project- and discussion-based. I provide decision-makers with a practical perspective gained from real-world experience on cyber risks, security governance, and operational resilience – exactly when it is needed.

Sovereign, cloud-native developer platform based on Kubernetes for critical energy infrastructure; around 200 people and 15 product teams.

• Introduced the product operating model for the platform, aligning around 18 product managers and product owners with Platform Architecture and Delivery in a matrix organisation.
• Defined the platform product processes and core product artefacts as the standard across all product teams.
• Established processes for non-functional requirements and Requests for Enhancement from consuming teams and customers.
• Designed the Jira structure supporting the programme's product and delivery processes.

Independent product design practice supporting startups and growing companies with enterprise SaaS, workflow architecture, product strategy, and AI-enabled product development.

• Deliver end-to-end product design for digital products, websites, and SaaS platforms, combining product thinking, UX, visual design, and implementation.
• Apply AI throughout the product lifecycle—from research, synthesis, information architecture, and prototyping to development, SEO, and content strategy.
• Design and build production-ready digital products using AI-assisted workflows, enabling significantly faster iteration and delivery.
• Advise founders and businesses on product positioning, UX strategy, and digital transformation.

• Project: Thermomix Online Mock
• Manual execution of functional tests
• Implementation of automated end-to-end tests
• Execution of regression tests and analysis of results
• Further development of the QA website server using Express.js, Handlebars and Node.js
• Further development of the test environment
• Technologies: Agile software development Scrum, Robot Framework, Python, JavaScript, Git (GitLab), VS Code, Jira, Confluence, Express.js, Handlebars, Phoenix/Elixir

• Portfolio development & customer acquisition
• Portfolio development (RAG, NLP fine-tuning, process automation with n8n) and active customer acquisition
• Positioning: GDPR-compliant, locally hosted AI solutions for SMEs

• Consulting across two integrated areas: Commercial Strategy (pricing, sales steering, marketing strategy, market expansion, margin management) and Operational Efficiency (process automation, AI integration, workflow design, last-mile automation).
• Development of custom SaaS solutions, explicitly tailored to the specific requirements and processes of each company.
• Delivery of AI training and change management workshops for managing directors and specialist departments, including AI competence training with a certificate of attendance under Art. 4 of the EU AI Act.