Software quality engineer Ina Schieferdecker discusses the future of software engineering and the evolving role of quality engineering, highlighting the impact of AI, low-code, and no-code solutions. She emphasizes the importance of integrating software construction with software analysis for effective quality assurance.

Richard Seidl Software Testing

This is the other podcast on Software Testing by Richard Seidl, the episodes are in spoken German but the show notes and site are written in English. Our summaries are generated from AI transcript translations.

• https://www.richard-seidl.com/en/blog/tag/podcast-software-testing
• https://www.richard-seidl.com/en/

Episode Details

• Show Notes: https://www.richard-seidl.com/de/blog/nextgen-software-engineering
• Published: 2026-02-10T05:00:00Z
• Duration: 1450
• Author: Richard Seidl - Experte fur Software-Entwicklung und Testautomatisierung

Overview

The podcast examines how the roles of software engineering and quality engineering are changing with the rise of AI and model-based approaches. It points out that traditional testing methods are often reactive, only verifying that software functions as intended after development rather than preventing errors from occurring in the first place. As a result, there is a growing emphasis on shifting quality assurance earlier in the development lifecycle to proactively identify and address issues.

AI is starting to impact software development by introducing automation and new testing tools, but the podcast notes that these technologies come with challenges, such as ensuring the accuracy of AI-generated code and managing the maintenance required for AI systems. Model-based testing is highlighted as a more sustainable and effective approach, where test specifications are developed as models that can be reused and adapted more easily. The conversation also underscores the importance of practical training in software testing, advocating for education that includes hands-on experience. Despite advancements in AI, the role of human testers remains crucial in maintaining quality and ensuring that automated tools are used effectively.

What If

• What if you prototype a model-based testing framework that pairs AI-generated test models with parallel software models?

  • Concrete move: Use AI tools (like LLMs) to auto-generate test specifications and requirements models while developing your core software. Integrate them via a Model-Bus (as discussed in Fraunhofers approach) to ensure alignment.
  • Why now: AI is lowering barriers to model-based testing, and the industry is moving toward early integration of testing (shift-left). This reduces rework and ensures quality by design.
  • Expected upside: Faster validation cycles, reduced error rates, and a scalable foundation for complex systems like IoT or critical infrastructure.

• What if you build a low-code/no-code test automation tool tailored to your niche software domain?

  • Concrete move: Leverage platforms like Rubber Test Framework or TTCN3 to create a domain-specific testing interface for your target audience (e.g., healthcare or logistics). Reduce manual coding by using visual workflows.
  • Why now: Low-code/no-code is democratizing testing, and your domain expertise can differentiate you. This addresses the current gap in tooling mentioned in the text.
  • Expected upside: Higher adoption for your testing solutions, reduced onboarding time for clients, and faster iteration in your own projects.

• What if you design a real-world training project that mirrors critical infrastructure use cases (like energy networks or autonomous systems)?

  • Concrete move: Build a prototype simulation for a safety-critical system (e.g., a simplified smart grid or vehicle control system) and apply test-driven development (TDD) and model-based testing across all testing levels (unit to acceptance).
  • Why now: The text emphasizes the rising demand for experts in quality assurance for critical systems and the need for practical training, not abstract examples.
  • Expected upside: Youll gain hands-on experience in high-stakes testing, create a demo portfolio, and align your skills with future industry needs for rigorous quality assurance.

Takeaway

• Integrate Quality Assurance Early Using a Shift-Left Approach: Begin testing and quality checks during the requirements phase to ensure software is built with testability in mind. This reduces rework later and aligns with the emphasis on “Shift-Left” strategies mentioned in the text.
• Adopt Model-Based Testing for Development Alignment: Develop test models in parallel with software models using approaches like the W-Model or V-Model. This ensures development and testing processes are synchronized, improving quality and reducing errors through structured integration.
• Leverage AI for Complex Test Automation and Resource Optimization: Use AI tools to automate testing scenarios, especially for handling large-scale or complex systems. This reduces manual effort and allows testing resources to focus on critical issues, as highlighted in the discussion on AIs role in testing.
• Prioritize Practical, Real-World Training Exercises: Incorporate hands-on, concrete use cases (e.g., an address book system) into your workflow or training. This mirrors the effective teaching approach described in the text, which emphasizes practical tasks over abstract examples like ATM systems.
• Create Structured, Technology-Agnostic Test Specifications: Define test requirements using formalized models (e.g., keyword-driven testing) that abstract business logic from implementation details. This ensures maintainability as technology evolves, as emphasized in the focus on test specifications as models.