Can We Rely on AI for Game QA?

Beyond Bug Fixing:Improving Game Quality

In game service operations, a “launch date” is more than just a calendar milestone. It’s tightly linked to marketing schedules, platform approvals, pre-registration campaigns, and user expectations. At the center of this entire process lies QA (Quality Assurance), which has evolved beyond simply finding bugs. It now plays a critical role in determining the overall quality of the game.

With each new build, core features like login, payment, and attendance rewards must be tested repeatedly. These are common across most games, yet QA teams are often small, and testing resources are quickly stretched thin as new content is added. Even worse, a last-minute bug or a missed test on a core feature can derail an entire launch schedule.

However, what if AI could take over these repetitive QA tasks?

ℹ️ Why Is QA Automation Getting So Much Attention?

The core purpose of QA is not just to “find bugs.” It’s to “prevent player friction.”

This means QA should go beyond technical checks. Ideally, testers play like real users, identifying points ‘where the experience breaks down’ or ‘where the user interface feels confusing.’ However, limited resources often force teams to spend most of their time on repetitive testing.

This is where AI becomes valuable. Automation handles repeatable tasks so that professionals can focus on strategic decisions. AI-driven QA automation is now advancing in three key areas:

Reducing repetitive testing workload for QA teams

Minimizing the risk of missed test cases and launch delays

Improving development efficiency and UI/UX with data-driven feedback

In many cases, early-stage bugs and user complaints result from missed testing of shared systems. This is why AI is becoming an important support tool in the QA process.

ℹ️ How Is QA Automation Used in the Field?

AI-based QA automation is already being adopted across various areas of game development. The following three approaches are currently the most widely used in real-world settings:

① AI-Driven Play Simulation

AI repeatedly plays the game like a real user, automatically detecting bugs, crashes, or performance issues across different scenarios. They can also catch hard-to-reproduce problems such as map collision errors or skill-related glitches.

AI also simulates extreme situations including server overload and abnormal user input to validate system stability. By running thousands of test cycles rapidly, they significantly reduce the total time and effort required for QA.

② Machine Learning for Bug Prediction

Machine learning models trained on past bug reports, crash logs, and test data can predict which areas of a new or similar game are likely to experience issues. This allows teams to detect recurring error patterns and proactively focus on high-risk features before they become problems.

This is particularly effective during the introduction of large-scale PvP features or new maps.

③ Generative AI for Automated QA Reports

Generative AI tools are increasingly used to analyze error logs and user play data, then automatically create QA reports. These tools can summarize bugs, document reproduction steps, trace affected code segments and even suggest potential fixes. This allows QA teams to focus on reviewing and refining reports rather than building them from scratch.

Some advanced systems also analyze character balance and skill behavior based on gameplay data, offering insights and recommendations. This enhances the accuracy and speed of the QA process while allowing teams to focus on refinement rather than manual documentation.

ℹ️ Real-World Use Cases from Korean Studios

▶️ Case 1: Wemade Play – Automating Payment QA

Wemade Play, known for the “Anipang” series, introduced AI to streamline payment testing during the development of ‘Disney Pop Town’. The team automated more than 300 minutes of daily QA time using Amazon Bedrock and the Claude 3.5 Sonnet model.

Claude 3.5 Sonnet, equipped with vision capabilities, captured gameplay screens, identified payment success or error messages, and delivered results directly to Slack. A critical factor was prompt engineering — defining AI’s role as a QA expert to ensure accurate analysis. When image recognition was unclear or hallucination was likely, additional context was provided. LangChain was also integrated to simplify and clarify the prompting process.

As a result, the time required for payment QA was reduced significantly — from 20 minutes to just 5 minutes per session.

Using prompts as a QA expert reduced number recognition errors

▶️ Case 2: Smilegate & Team Candle – Balance Testing with Reinforcement Learning

Smilegate applied reinforcement learning to fine-tune level difficulty in their puzzle game ‘Pygmalion.’

Puzzle balancing is core to gameplay, but also a critical yet notoriously complex part of game design. By leveraging AI, Smilegate enabled millions of simulated playthroughs, allowing the system to evaluate the optimal difficulty for each stage. The AI identified levels that could be cleared with significantly fewer attempts than originally intended by designers.

One developer shared, “The AI suggested that we reduce the clear conditions by five attempts compared to our original plan.” This approach helped quantify what had previously been a subjective design intuition.

This case is a representative example of reinforcement learning where the AI directly plays and evaluates the game, offering measurable feedback that aligns with the designer’s intent.

(Source: Smilegate’s AI Tech: Giving Indie Teams Freedom in QA, 2024)

▶️ Case 3: LoadComplete – Scalable QA with a Small Team

At GDC 2024, LoadComplete demonstrated how a small team of just two to three QA members could effectively scale testing using machine learning. For ‘Frame Arms Girl: Dream Stadium,’ a roguelike hack-and-slash game, they needed to validate complex character combinations and combat balance.

They built an ML-based auto-play testing environment by layering scenario-specific models on top of a base behavior model. AI testers ran gameplay across multiple stages, with data automatically collected and analyzed. Notably, cloud-based parallel testing kept operational costs low—approximately $4 to $5 per hour.

While human QA focused on functional testing, the AI handled proactive bug detection and stage-level balance validation.

(Source: Testing Empowered: Integrating ML-Based Playtesting in a Team with Limited QA Capacity, GDC 2024)

ℹ️ The Core of QA Automation: Clear Data and Clear Objectives

For AI-driven QA automation to succeed, two elements are essential: high-quality data and well-defined goals.

Effective starting points include game play logs from alpha or beta tests, historical bug reports, and code change histories. These provide the foundation for AI to learn patterns, predict issues, and perform automated testing with accuracy.

Equally important is clearly defining the purpose of automation. Are you verifying core functionality, detecting UI errors, or testing content balance? Each goal may require a different approach to AI.

For straightforward tasks like button clicks or visual anomaly detection, image recognition tools may be sufficient.

For more complex challenges - such as identifying user intent or repetitive behavior pattern - reinforcement learning or machine learning models are more effective.

Clarifying these objectives upfront enables better model selection and leads to more successful QA automation outcomes.

♻️ Final Thoughts: The Evolution of QA Automation

AI-powered QA automation is not new, but it has rapidly evolved.

Earlier solutions relied on scripted test cases and fixed condition simulations. Today, modern tools—based on large language models, generative AI, and reinforcement learning—offer greater flexibility, precision, and scalability across a wide range of scenarios.

In the end, the key is not the technology itself, but how and where it’s applied:

Do you want to reduce repetitive testing before launch?

Do you need to catch UI issues that impact user experience?

Do you aim to validate content balance early in development?

AI can already support all these needs.

Now it is up to producers, planners, and business leaders to use it strategically, turning QA into a smarter, faster, and more proactive process.

※ Disclaimer: This content reflects the author’s personal views and includes only publicly available examples. It does not represent the official position of any company mentioned