Manual vs. Automated Game Testing: Is There Room for Both in 2025?

Technological advancements are constantly changing the gaming industry. AI is the latest in line to leave its footprint on game companies. It’s forcing developers to completely rethink how they approach various workflows. These range from design and production to analytics and even game testing.

The latter is an increasingly sought-after practice, coinciding with the rising number of games and players. The global game testing market size is projected to rise to $2.45 billion by 2023, at a CAGR of 10.11%.

But while more game testers are in demand, the most effective way to test games has been the source of intense debate. 

With the rise of AI, what does this mean for manual game testing? Will one replace the other? Or can they somehow coexist? Let’s dig in.

What Is Game Testing?

Game testing (or playtesting) is when game designers try out a new game to check if there are any design flaws, UX issues, bugs, or glitches before wide release.

Different kinds of game tests include:

• Open: Inclusive to anyone to playtest, or recruiting external testers.
• Closed: Internal to the company, only current employees are allowed to participate.
• Beta: Game testing is close to the wide release date. External testers may have access to a limited version of the game.

Generally speaking though, most video games in development are exposed to their target audiences to get early feedback and pinpoint design issues.

Manual Game Testing: Current Challenges

As the game industry evolves, the emphasis to scale effectively is more important than ever. This amplifies various hurdles that companies face with manual game testing. Here are five of the most common right now.

1. Replicating Real Player Behavior

Manual testers can have a tough time emulating the irrational, chaotic behaviors of real players. 

Genuine actions like rage-tapping can often reveal key design issues that might not show up in in-house tests.

2. Scaling Issues

In reality, you simply can’t conduct a game test on millions of people. Up until this point, it’s been impossible and has prevented game companies from scaling in this area.

You might have managed to game test on smaller player populations without too much trouble. But the more players you need to test on, the more complicated it gets.

3. Time-Consuming and Expensive

There is a reason why AI is being deployed more and more in game testing. Automating this process means saving game companies and studios precious time. 

But that’s not all. Manual testing depends on dedicated QA teams and multiple rounds of iteration. With the average hourly rate for QA testers standing at $42.95 per hour, it’s certainly costly, with human error still being an issue.

4. Knowing What Issues to Look For

Even the best game testers may have a tough time interpreting player data. Ideally, you’ll have a team of analysts to make sense of it for you. 

But if you’re tasked with doing it yourself, you may not always know which questions to answer when looking at this data. 

5. Finding Long-Term Issues

Manual game testing is limited in what long-term patterns it can track. 

For example, you might be able to conclude from a playtest that an issue started yesterday, or maybe a week ago. But going back weeks or months? Most game testers simply don’t have the databases and processing speed to go that far back in time. 

So finding serious issues that are more long-term in nature can be nearly impossible to detect.

How Can Game Testing Be Automated?

Here are some tools and processes that enable automated game testing today.

Creating Virtual Players

Platforms like Unity Game Simulation allow developers to run thousands of automated tests with custom virtual players using behavior trees, ML agents, and heuristics. This enables better level design, test balance, and game systems at scale.

AI-Driven Bug Detection

AI algorithms can be leveraged to detect bugs and glitches, especially subtle ones that may elude the human eye. This will help developers save precious time and allow them to fix issues more quickly.

Automating the Analytics Process

Analytics is a key part of game testing. You need to be able to collaborate with analysts to understand how players might behave. However, time and resource constraints can usually result in a bunch of guesswork.

Enter Keewano’s AI Analyst

Next-gen analytics tools like Keewano analyze player behavior at scale. It observes every single player action and highlights the issues that actually matter.

Imagine having many analysts on your laptop, continuously testing hypotheses and providing actionable recommendations. That’s what Keewano’s AI Analyst is capable of.

It can find all kinds of unique trends, make sense of them, and teach you exactly what is working in the game test, and what needs work.

For game testing, you can leverage Keewano’s AI Analyst to:

• Detect bugs and errors, saving developers precious time.
• Identify gameplay elements that are either too easy or too challenging.
• Highlight any game performance issues and pinpoint problems (including the causes).

Manual or Automated Game Testing? You Still Need Both

Today, many game testers worry about AI and automated game testing’s threat to their jobs. But we believe this is simply not the case.

It’s not about choosing to automate your playtesting or stay manual. The key is to combine the best elements of both. Ultimately, AI-driven technologies are a fantastic way to automate this key process of game development.

However, it is not a replacement for human ingenuity. It’s still (and always should be) a tool that humans should leverage. AI should be used to find the key issues in a game. 

This automation should empower teams to focus on the good stuff: Coming up with creative solutions to those problems. This way, they can detect bugs more accurately, fix last-minute issues more efficiently, and test games at scale.