Negative testing is redefined as essential for blocking invalid actions and fostering cross-team collaboration, while platform engineering emphasizes self-service infrastructure and simplifying workflows. The episode also addresses AI trends, learning frameworks, community engagement strategies, CV best practices, and the need for cultural shifts in engineering collaboration.

MOT This Week in Testing

MOT - This week in Testing - Varied hosts, group chat, often with community questions and involvement. Show notes have a full transcript.

• https://www.ministryoftesting.com/podcasts
• https://www.ministryoftesting.com

Episode Details

• Show Notes: https://www.ministryoftesting.com/podcasts/this-week-in-testing?wchannelid=czgwdadw2c&wmediaid=a9i9mdo0cd
• Published: 2026-06-12T14:51:22Z
• Duration: 65:04
• Author: Unknown

Overview

The podcast delves into negative testing as a core system function, reframing it as a critical “happy path” for blocking invalid actions, emphasizing its role in ensuring robust system validation and fostering cross-engineering collaboration. It explores conceptual shifts in testing paradigms, including Al Goodells framework for using negative tests to reveal deeper technical insights and redefining “happy paths” to incorporate error detection scenarios. Technical challenges are also discussed, such as software migration issues (e.g., dual servers in OpenShift causing duplicated data processing) and the impact of lock mechanisms on testing workflows. Additionally, the episode highlights platform engineering, focusing on self-service infrastructure, the “shift down” movement, and the “thinnest viable platform” concept aimed at simplifying workflows. Challenges in team silos, integration gaps between platform engineering and development teams, and the need for shared ownership of quality across roles are addressed.

The discussion extends to AI and emerging trends, including the term “loop engineering” as a shift from prompt engineering to designing systems around AI agents, alongside updates in the Mottoverse on AI-related themes. Learning models like the Status Model (Simon Wriggler) and the ITC Model (Individual, Team, Community) are introduced, encouraging custom frameworks and AI-assisted refinement. Community engagement is a recurring theme, with calls for audience participation in live discussions, shared learning strategies, and creating “moments” around topics like testing in self-service infrastructure or platform engineerings impact on QA. Practical advice on CV and resume writing is provided, emphasizing clarity, keywords for ATS compatibility, and showcasing achievements over responsibilities. Finally, the episode underscores the importance of collaboration and cultural shifts in engineering, advocating for simplification, breaking down silos, and fostering forward-thinking conversations about quality and tooling.

What If

• What if you leveraged community engagement to test your softwares negative edge cases?

  • Move: Introduce a “challenge” in your user community (e.g., “Test the systems rejection rules”) and collect real-world scenarios where users attempt invalid inputs (e.g., missing fields, wrong formats).
  • Why Now? The text highlights audience participation via the “join us backstage” button and examples like Rosie Sherrys challenges, which can generate actionable data on how systems fail in real use.
  • Expected Upside: Identify untested edge cases, improve system robustness, and build user trust by proving your software handles errors gracefully.

• What if you optimized your migration process by prioritizing single-server setups before scaling?

  • Move: Migrate applications to a single OpenShift server first, using the lock mechanism to avoid duplication, before implementing dual-server architectures.
  • Why Now? The text describes duplicated data processing and a 20-second lock delay as pain points during migration; addressing this upfront could reduce testing overhead.
  • Expected Upside: Faster test cycles, reduced manual batch processing delays, and a more stable foundation for scaling later.

• What if you rebranded your development practice around loop engineering principles to align with AI agent systems?

  • Move: Create a framework for designing workflows around AI agents (e.g., defining roles like validator or data cleaner and embedding them into your codebase).
  • Why Now? The text introduces loop engineering as a shift from prompt engineering, with ongoing AI trends in Mottoverse, making this a timely pivot.
  • Expected Upside: Streamlined development for AI-integrated systems, better alignment with future tools, and a competitive edge in adopting emerging AI practices.

Takeaway

• Implement lock mechanisms during software migration to prevent duplicated data processing in dual-server environments (e.g., when transitioning to OpenShift), ensuring consistent workflows and minimizing conflicts.
• Treat negative testing as a core system function, not an exception, by designing tests that validate invalid user inputs (e.g., missing fields) and integrating them into standard testing practices.
• Create an AMA session on a specialized topic like “AI agents” or “self-service infrastructure” to share expertise, engage with the community, and build visibility for your work.
• Optimize CV formatting for ATS compatibility by using standard section headings, embedding relevant keywords from job descriptions, and avoiding unconventional layouts or graphics that may confuse parsing tools.
• Adopt the “thinnest viable platform” principle to simplify tools and processes in your workflow, reducing complexity and improving efficiency by eliminating redundant systems or external dependencies.