Baruch Annink

Do you want an objective opinion on your #QA performance based on 20 years of hands-on experience? Schedule your 15-minute coffee chat with Chantal van Gorp! ☕   Feel free to share any questions, challenges, new ideas: https://lnkd.in/eTgs5k6N   #LifeSciences #QualityAssurance #QMS #eQMS

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We have a new #Betatalks for you! 💡 Join Bram and Jelle as they dive into software testing, discussing different types, prioritizing effectively, and choosing the right strategy. They explain the testing pyramid, emphasizing the importance of automated unit tests while minimizing manual testing, which is crucial for new features. They share their unique approaches and agree on the need for common test definitions within a team for effective planning and discussion. 💻 #SoftwareTesting #QualityAssurance #Development 🔗 https://lnkd.in/g3yizzsr

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LinkedIn, your "AI" autocompletion for commenting on posts just started giving me recommendations in Dutch. I never spoke Dutch. I probably never will. How about some unit tests in your features, huh? Quick reminder: AI models are *statistical* models. If they work 95% or 90% of the time, that's 5 or 10% of failures. Add those numbers to your calculations for bounce rate, churn, whatever... do the math. It's expensive. My philosophy is: always use AI models with a fallback for when they fail, and never make the whole feature completely useless, even when the AI fails. The cost for failing must be very low. In this case, your failure to even detect my language (you have them in my profile?!) cost you this post. Not sure how expensive that is, but hey, bad publicity and all.

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They say six hours of debugging can save you five minutes of reading unwritten code documentation.   Poorly documented code can waste so much of a developer's time. At Huenei, we don’t just focus on documenting code; our service delivery maintains high standards across various aspects that contribute to our overall code quality. Is your team keeping tabs on these crucial metrics for code quality?   • 🧠 Code Complexity: We measure complexity because complex code is not only harder to understand and debug but also significantly more difficult and expensive to maintain over time. • 🔄 Duplicated Code: Copy-pasting code instead of properly reusing or referencing it can introduce inconsistencies and bloat the codebase. Reducing duplication helps ensure the code is cleaner, easier to maintain, and more efficient to manage. • 🧹 Unnecessary Code: As projects evolve, code that was once useful can become obsolete. Unnecessary code clutters the codebase and slows down future development. • 📄 Documented Code: Well-documented code is crucial for continuity. Whether it’s for bug fixes, enhancements, or future updates, proper documentation saves time and effort for everyone involved.   Why is this important? Tracking and measuring these metrics helps us deliver reliable, maintainable, and scalable software solutions. It prevents technical debt and ensures that future developers can quickly understand and work on the code, whether they’re new team members or simply stepping in to solve an issue.

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I wonder how often companies move between tiers. I’ve witnessed the change from a Tier 3 to a Tier 2 approach. Definitely going to check this out!

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