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The Present and Future of Automated Game TestingĀ 

iXie gaming

Test Automation Tools Game developers and quality assurance (QA) experts use various automated testing tools to test different components of their games. Machine Learning (ML) and Artificial Intelligence (AI) Integration AI & ML solutions have been introduced in automated game testing.

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Xbox Game Testing: Top 5 Best Practices by iXie

iXie gaming

Testing your Xbox game is crucial in delivering quality assurance or QA services. Like testing games on any platform, Xbox game testing involves a series of best practices. These practices help ensure the performance and quality of your Xbox game. But don’t confuse Xbox testing with typical video game testing.

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The Importance of Beta Testing in Game Development

iXie gaming

Beta testing is a critical game quality assurance (QA) process. Also, developers can assess how well their games run on multiple platforms through open betas. This is because they enable users from different platforms to join the testing phase. Closed Beta Closed beta testing is the opposite of open beta testing.

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GDC 2023: Trends re-defining the future of game development

AWS Games

Based on what we heard, a few themes that are top-of-mind include: cloud-based game development (aka Game Studio in the Cloud), artificial intelligence (AI) and machine learning (ML) to help support player community health, content moderation, and AI-driven game development.

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Lost in Translation: Navigating the Challenges of Game Localization Testing

iXie gaming

Visuals, humor, and narrative themes that excite players on a specific platform may offend or alienate those in other markets. Expert Guidance for Overcoming Game Localization Challenges At iXie , we provide five groups of localization quality assurance support teams, covering over 30+ languages globally.

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Operationalize generative AI applications on AWS: Part I ā€“ Overview of LLMOps solution

AWS Games

With the rising popularity of generative artificial intelligence (AI), companies are exploring foundation models (FMs) and realizing the immediate benefit they provide to their business. It takes the MLOps methodology and adds the additional skills, processes, and technologies needed to operationalize generative AI models and applications.

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