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AI-native software engineering may be closer than developers think

CIO

Developers unimpressed by the early returns of generative AI for coding take note: Software development is headed toward a new era, when most code will be written by AI agents and reviewed by experienced developers, Gartner predicts. That’s what we call an AI software engineering agent.

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Case in point: taking stock of the CrowdStrike outages

CIO

Last summer, a faulty CrowdStrike software update took down millions of computers, caused billions in damages, and underscored that companies are still not able to manage third-party risks, or respond quickly and efficiently to disruptions. It was an interesting case study of global cyber impact, says Charles Clancy, CTO at Mitre.

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Why Testing is No Longer Sufficient for Today’s Software Delivery Pipelines

OverOps

The move to innovate at speed and scale is stressing software quality and exposing the limitations of testing. Don’t get me wrong – testing in all its forms is inseparable from the software delivery supply chain. Testing is no longer enough. The Red Queen Hypothesis (and What it Means for Testing).

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Effective Software Testing – A Developer’s Guide

Henrik Warne

I recently finished Effective Software Testing – A Developer’s Guide by Maurício Aniche , and I really liked it. I have been coding for a long time and I think I have been writing pretty good tests for the features I have implemented. Specification-Based Testing. Most Interesting Chapters.

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Agentic AI design: An architectural case study

CIO

Microsoft is describing AI agents as the new applications for an AI-powered world. Development teams starting small and building up, learning, testing and figuring out the realities from the hype will be the ones to succeed. Let’s review a case study and see how we can start to realize benefits now.

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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. This time efficiency translates to significant cost savings and optimized resource allocation in the review process.

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John Snow Labs Releases Generative AI Lab 7.0 to Help Domain Experts Evaluate and Improve LLM Applications and Conduct HCC Coding Reviews

John Snow Labs

New capabilities include no-code features to streamline the process of auditing and tuning AI models. While the Generative AI Lab already exists as a tool for testing, tuning, and deploying state-of-the-art (SOTA) language models, this upgrade enhances the quality of evaluation workflows.