JUnit 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.

• Built a data integration platform for high-, medium- and low-voltage assets to enable contextual analysis of time series with master data from SCADA control systems (IEC 60870 104), INIS and SAP.
• Responsible for the architecture and implementation of the solution, and acted as a sparring partner for the Product Owner.
• Technologies used: Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter and various API gateways.

• 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

Maturing their API practices on both a business and technology level. My role covers strategy, architecture, developer advocacy as well as hands-on software engineering, enabling both technical teams and business leadership to adopt and act on API-centric principles effectively. Coincidentally, we also establish GitOps, DX and platform best practices with this project.

Highlights:

• Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
• Formulating a way forward for API Lifecycle Management at OTTO
• Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals

API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, React, Node.js, TypeScript, Redocly, reactive programming, CDC, Golang, Gin, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.

• Focus: System architecture, cloud-native platforms, microservices, API design
• Product: Pflege-Pfad is a digital placement platform that directly connects relatives of people in need of care with verified care services and caregivers – without an agency and without ongoing fees.
• Business analysis & process design:
• Analysis of the German care market and identification of the key pain points of both target groups.
• Modeling of core business processes: registration, verification, care request, application, placement, and rating.
• Definition of the business model as a freemium/premium model with optional contact unlocking.
• Creation of user stories and requirements documentation for relatives, care services, and administrators.
• Design of trust and quality assurance mechanisms with document upload, admin review process, and rating system.
• Coordination with stakeholders and validation of product decisions with potential users.
• Technical implementation:
• Design and implementation of the entire platform architecture as the sole developer.
• Design and implementation of a REST API with Spring Boot and Kotlin, including JWT-based authentication.
• Development of the frontend as a single-page application with Angular 17.
• Implementation of the AWS infrastructure with EC2, RDS PostgreSQL, S3, CloudFront, and IAM.
• Document upload with AWS S3 using presigned URLs to verify care services.
• Email notifications via Resend API.
• AI-supported care service search via OpenAI API.
• Implementation of complete user flows such as registration, login, password reset, and the placement process.
• Development of an admin panel for user and care service management as well as analytics.
• CI/CD with GitHub Actions and containerized deployments with Docker.
• End-to-end tests with Playwright.

Technologies: Kotlin, Spring Boot 3, Spring Security, JWT, JPA/Hibernate, PostgreSQL, Angular 17, TypeScript, RxJS, AWS (EC2, ECS, S3, CloudFront CDN, RDS PostgreSQL, IAM), nginx, GitHub Actions, Playwright, Maven, Git, OpenAI API, Resend API, Docker, Scrum, i18n (DE/EN/TR), Kiro, feature flag architecture.

• Implementation of a full-stack application with Angular as frontend framework and Java as backend language
• Use of the Swagger OpenAPI specification for REST communication between frontend and backend applications
• Control of a card printer via the Nexus Card SDK framework
• Design and implementation of card management software with web interface and JavaFX module for printer monitoring
• Use of Cucumber tests for test automation and creation of test artifacts for testing outside the development environment
• Use of Wireshark for network investigations to analyze the API of a third-party product and to check the REST API of the self-developed software

Temporary employment

• System black-box integration tests (BBIT, IVVQ): Execution of regression, release, confirmation and conformity tests for safety-critical braking control units in the railway industry
• Software Test Application & Integration: Runtime configuration of software components and libraries, validation of interfaces, configuration dependencies and component interactions
• Test Automation (FEAT Framework): Co-development and further development of an automated test framework for test execution, reporting and result analysis
• Functional Safety (SiL4, FuSi): Ensuring compliance with safety requirements, traceability and coverage as well as conformity with EN50126/28/29
• Test Automation for Communication Components: Configuration and validation of fieldbus (CAN) and Ethernet-based TCMS data communication interfaces (TRDP and CIP)
• Requirements Analysis & Shift-Left (PTC Windchill ALM): Analysis of software and system artifacts to identify gaps, ambiguities and redundancies early in the SDLC
• Test Design & Test Case Development: Derivation of test conditions and coverage strategies and implementation of data-driven test cases (DDT) including reusable test data fixtures
• CI/CD & Automation (Python, PowerShell, Jenkins, SVN): Automation of build, test and HIL deployment processes as well as integration into CI/CD pipelines
• Test Data & Configuration Management (XML): Maintenance and adjustment of XML test vectors and system configurations with automated integration into test environments
• Non-Functional Testing: Execution of performance and load tests to assess stability and system behavior
• Agile Development & Defect Management (JIRA, Confluence): Work in Scrum teams, test coordination, review of test artifacts as well as defect tracking and root-cause analysis
• Error Analysis & Debugging (CANoe, CANalyzer): Analysis of errors and message flows across multiple system levels (application to bus)
• Model-Based Analysis (UML, Enterprise Architect): Specification of SUT/SOW and support for systematic test control
• Process & Test Documentation: Creation of integration and test documentation according to internal quality and certification requirements

Built an international e-commerce platform for a multi-vendor marketplace for digital assets from scratch. Designed and operated cloud native architectures at enterprise scale.

• Designed and operated a highly scalable microservice and serverless architecture
• Built the complete cloud infrastructure with Terraform + AWS CDK in AWS
• Provisioned ECS/EKS clusters (Fargate), Application Load Balancers (reverse proxy), and Lambda functions
• Observability & tracing with CloudWatch, DataDog, Prometheus, and Grafana
• End-to-end setup with DataDog (formerly AWS CloudWatch), Prometheus, and custom Grafana dashboards
• Integration of advanced metrics (including ORM mapper) and distributed tracing with Jaeger
• Robust backup and disaster recovery strategies
• RDS Postgres backups and hourly snapshots
• Read-only, asynchronously synchronized replicas with automated master failover in emergencies
• Minute-level rollback capability through versioned Docker images on ECS and Git-based CI/CD pipelines
• Created CI/CD pipelines with GitHub Actions for automated multi-stage deployments (Dev, Testing, Prod)
• Integrated Stripe for international payment processing
• Built a marketplace payment system with multiple parties and payout routines
• Used Algolia for high-performance real-time search of digital assets on the platform
• Federation of services with GraphQL and Hasura
• Later migration to GraphQL Mesh
• Test Driven Development (TDD) - unit, integration, and E2E testing with Jest, Vitest, and Playwright
• Used Next.js / React for modern frontend applications in the nx monorepo
• Enterprise security architecture & access control
• Integration of JWT tokens with Auth0, OAuth, OIDC, IP guards, BOLA protection, and secret vaults
• Authorization concepts with RBAC, ABAC, and native Postgres Row-Level Security (RLS)
• Built internal microfrontends with Retool for fast prototyping and operational business processes

Technologies: ABAC, AWS CDK, AWS CloudWatch, AWS ECS, AWS EKS, AWS Fargate, AWS RDS, AWS S3, Algolia, Auth0, DataDog, Docker, GitHub Actions, Grafana, GraphQL, GraphQL Mesh, Hasura, JWT, Jaeger, Java, JavaScript, Jest, Kotlin, Kubernetes, Monorepo, Next.js, OIDC, Playwright, Postgres, Postgres RLS, Prometheus, RBAC, Redis, Retool, Serverless, Stripe, Terraform, TypeScript, Vitest

The Base Services of DB InfraGO form a central data hub between the company’s IT systems. Common Data Services are developed that distribute data from source systems to a variety of downstream systems (via JMS) and make them available (via REST API). The existing service landscape based on TIBCO is being migrated to DB InfraGO’s cloud-native platform.

• Architecture design and implementation for performance-optimized bulk data processing of several million datasets at specific times during the day
• Technical specification and documentation of business requirements
• Integration of various subsystems (including SAP and Salesforce) through the reimplementation of more than 40 microservices based on Spring Boot
• Migration of TIBCO Based microservices from the Enterprise Integration Platform to the Cloud Native Platform using Spring-Boot
• Establishment of a deployment pipeline using GitLab CI/CD, Artifactory, and automated deployment to a Kubernetes environment with the help of ArgoCD
• Definition and implementation of automated unit, integration, and regression tests

Team Size: 9

Technologies/Tools: Java, Spring Boot, ActiveMQ(JMS), JUnit, Tibco Business Works, Gitlab (CI/CD), Kubernetes (Amazon AWS), ArgoCD, postgreSQL, Oracle, Postman, Hoppscotch, openAPI, Sonarqube

• Fully on-premises LLM document-examination platform for a compliance-critical banking domain: agentic LangGraph pipeline with deterministic verification, every AI judgment structured and source-anchored; ~960 automated tests, zero data egress
• GPU throughput engineering (quantized serving, speculative decoding, prefix caching): 9.5x extraction speed-up, 500+ multi-document case files per day on a single A100
• AI-native EDI/EDIFACT integration platform (~116k LOC Java 25 / Spring Boot 4, 1,900+ tests): LLM-drafted partner mappings machine-verified before go-live (DFDL conformance, field-coverage checks, dry runs), ~99.5% byte match on real customer files — replacing weeks of manual mapping per partner

• Designed a migration strategy for WLS applications to BKA Cloud and Stack IT

• Created a hybrid cloud for internal testing and development with Kubernetes

• Transformed the IT landscape from an outdated monolithic architecture to a cloud-based architecture

• Integrated new CI/CD processes using Azure Pipelines

• Quadrupled the processing throughput for personnel security checks

• Migrated personnel security screening applications to Docker-based applications

• Implemented CI/deployment pipelines using Azure DevOps

• Environment: Java Springboot, Kubernetes, Oracle Weblogic, OpenID, Camunda, JUnit, TOGAF

• Recurring problem in my own AI-assisted projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but remain difficult to follow and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions form a consistently linked knowledge graph, traceable from requirement to architecture decision – queryable by both people and AI agents. Technically based on RDF/OWL and a custom MCP server.

• Result: Working MCP daemon, Docker image published automatically to GHCR, nine hexagonal modules, eleven ADRs (including an Open-Core licensing model). Requirements engineering and Ubiquitous Language hexagons are active. Public as a Community Edition under Apache-2.0 since 07/2026 (github.com/kogn-io/arknet), together with the Claude Code plugin and GHCR image; Open-Core model.

• Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ, Interface Development, Software Architecture, Continuous Integration, Knowledge Management