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The early returns on gen AI for software development

CIO

But early returns indicate the technology can provide benefits for the process of creating and enhancing applications, with caveats. The key to success in the software development lifecycle is the quality assurance (QA) and verification process, Ramakrishnan says.

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Quality Assurance Approach towards Virtual Reality (VR)

Perficient

Tourism – VR is an excellent marketing tool that can be successfully used in tourism. It is highly recommended for testers to be aware of the safety standards to be followed on testing an application with a virtual reality equipment. So, testing the performance of such applications pose a big challenge.

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The Hidden Gem of Savings in the Software Delivery. And no, it’s not AI

CIO

It will improve project management, help with requirements creation, assist developers with coding, cover the system with auto-tests, report defects, and improve deployment. Example 1: major outage after a core system update A large broker-dealer updated its core system and immediately ran into a catastrophic failure.

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QA Wolf exits stealth with an end-to-end service for software testing

TechCrunch

“Our vision is to become the ‘operating system for quality’ that companies use to improve the holistic quality of their applications, beginning with automated end-to-end testing.” But Perl makes the case that QA Wolf removes the complexity of quality assurance testing like few others do.

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12 most popular AI use cases in the enterprise today

CIO

Organizations all around the globe are implementing AI in a variety of ways to streamline processes, optimize costs, prevent human error, assist customers, manage IT systems, and alleviate repetitive tasks, among other uses. And with the rise of generative AI, artificial intelligence use cases in the enterprise will only expand.

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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning - AI

Generative artificial intelligence (AI) applications built around large language models (LLMs) have demonstrated the potential to create and accelerate economic value for businesses. Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications.

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Leveraging Standardization and Automation to Facilitate DevOps Testing in Multi-Code Environments

CIO

DevOps environments give development teams the flexibility and structure needed to drive productivity and implement early and often “shift left” testing to ensure application optimization. Manual testing also creates barriers to optimizing DevOps. From there, development teams should look to automate as many testing processes as possible.