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Article: Moving towards a Future of Testing in the Metaverse

InfoQ Culture Methods

In this article, Tariq King describes the metaverse concept, discusses its key engineering challenges and quality concerns, and then walks through recent technological advances in AI and software testing that are helping to mitigate these challenges.

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Article: InfoQ Software Trends Report: Major Trends in 2022 and What to Watch for in 2023

InfoQ Culture Methods

This article is a summary of the 2022 software trends podcast. 2022 was another year of significant technological innovations and trends in the software industry and communities. The InfoQ podcast co-hosts met last month to discuss the major trends from 2022, and what to watch in 2023.

Trends 104
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Article: Managing the Carbon Emissions Associated with Generative AI

InfoQ Culture Methods

There’s an increasing concern about the energy use and corresponding carbon emissions of generative AI models. And while the concerns may be overhyped, they still require attention, especially as generative AI becomes integrated into our modern life.

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Article: Using Machine Learning for Fast Test Feedback to Developers and Test Suite Optimization

InfoQ Culture Methods

The article explores optimizing test execution, saving machine resources, and reducing feedback time to developers. Test suites may be computationally expensive, compete with each other for available hardware, or simply be so large as to cause considerable delay until their results are available. By Gregor Endler, Marco Achtziger.

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Article: Getting Rid of Wastes and Impediments in Software Development Using Data Science

InfoQ Culture Methods

This article presents how to use data science to detect wastes and impediments, and concepts and related information that help teams to figure out the root cause of impediments they struggle to get rid of.

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Article: Agile Development Applied to Machine Learning Projects

InfoQ Culture Methods

Developing ML with agile has a few challenges that new teams coming up in the space need to be prepared for - from new roles like Data Scientists to concerns in reproducibility and dependency management. Machine learning is a powerful new tool, but how does it fit in your agile development? By Jay Palat.

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Article: How I Contributed as a Tester to a Machine Learning System: Opportunities, Challenges and Learnings

InfoQ Culture Methods

Have you ever wondered about systems based on machine learning? In those cases, testing takes a backseat. And even if testing is done, it’s done mostly by developers itself. A tester’s role is not clearly portrayed. Testers usually struggle to understand ML-based systems and explore what contributions they can make.