Unleashing the power of AI models: The importance of QA in video game testing

April 18, 2024

In today’s digital era, the gaming industry is witnessing a transformative shift thanks to integrating artificial intelligence (AI) into various game development and testing facets. From enhancing the storytelling to ensuring compatibility across multiple platforms, AI is revolutionizing how games are tested and refined before they hit the market. It’s all about providing a unique user experience at the time of release. As Tomas Borzi, Head of QA Games Studio at Globant, mentions: “The user experience is crucial for video game quality.” That’s why good quality assurance is decisive in concept creation, and AI is an excellent advantage by automating many of these processes.

However, to take testing strategies to the next level, we must first understand what types of AI work best in the gaming world and how we can integrate them into the various stages of QA.

Understanding ML strategies

Machine learning is a field within AI by which algorithms learn from data to improve performance without explicit programming and do tasks that typically require human intelligence or intervention. This is fundamental to modern game development and testing, for which purpose we have selected three main types of ML: 

  • Supervised Learning (SL): Using labeled data to make predictions and classifications. The model can identify patterns based on the labels provided.
  • Unsupervised Learning (UL): Discovering patterns and structures in unlabeled data. For example, identifying behaviors without predefined classifications.
  • Reinforcement Learning (RL): An agent learning to make decisions through interaction with the environment, applied to any game where decisions are made to obtain rewards. Reinforcement learning can be utilized to create AI actions for different games and experiences, automating processes and simulating human behavior.

Thus, by training AI to understand game processes, designers can automate actions, evaluate performances, and integrate improvements based on previous experiences. This streamlines the testing process and ensures that AI functions autonomously. 

The role of AI in game testing

Now that we have an idea of the most common types of ML used in the gaming field, we can take a closer look at how AI is reshaping traditional testing methods to scale processes and enhance overall quality assurance. The different stages of video game testing demonstrate how AI is improving the gaming experience:

  • Play Testing: Traditionally, playtesting involves evaluating a game by playing it and identifying potential issues, like glitches or bugs. AI assists in automating gameplay testing, identifying patterns that may lead to errors, and providing insights into user experience. For example, RL models can play the game repeatedly, using computer vision for asset detection and rendering verification.
  • Usability Testing: AI helps evaluate how easy and intuitive players can engage with the game. AI suggests usability improvements by analyzing user interactions and feedback through Natural Language Processing (NLP). For example, when leveraging ML techniques to simulate user interactions, testers can evaluate the game’s usability through reviews.
  • Compatibility Testing: Ensuring the stability and performance of a game across different platforms, devices, and configurations is essential. AI detects compatibility issues and recommends improvements for each platform. For example, AI can simulate different scenarios and predict the behavior of the various environment configurations based on previous data.
  • Localization Testing: Adapting the game to different languages and cultures goes beyond mere translation. AI assists in ensuring cultural sensitivity and adapting the game to resonate with global audiences. For example, NLP algorithms assess how localized content maintains context and meaning across different languages and cultures.
  • Compliance Testing: AI, coupled with computer vision, streamlines compliance testing by identifying regulatory issues and validating relative positions, logos, and other elements. For example, MagnifAI, Globant’s quality assurance tool, compares game images to ensure consistency and compatibility with the console’s ecosystem. 
  • Accessibility Testing: AI analyzes interfaces and user interactions to identify potential accessibility risks, ensuring that games meet regulatory standards and cater to users’ diverse needs. For example, an AI-powered tool can go into the user’s interface and identify an element that fails to meet accessibility standards, such as missing alt texts for images.

AI-powered game testing is not just about automation; it’s about enhancing the quality, accessibility, and user experience of games. AI is everywhere, and its use must be part of our testing strategy. By leveraging various ML strategies and integrating AI into different stages of testing, developers can create immersive, inclusive, and enjoyable gaming experiences for players worldwide, turning the players into engaged brand ambassadors.

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The Data & AI Studio harnesses the power of big data and artificial intelligence to create new and better experiences and services, going above and beyond extracting value out of data and automation. Our aim is to empower clients with a competitive advantage by unlocking the true value of data and AI to create meaningful, actionable, and timely business decisions.