Name: GTAC 2015:使用真實設備的移動遊戲測試自動化 (GTAC 2015: Mobile Game Test Automation Using Real Devices)
Uploaded: 2021-01-14T05:59:55.000Z
Duration: 16 min 11 s
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>>Yvette Nameth: Next up, we have Jouko from Bitbar to talk about using image recognition

for mobile app and game testing. >>Jouko Kaasila: Hello.

Okay. So my name is Jouko Kaasila. I'm the cofounder and COO of Bitbar, the company behind

a pretty awesome online service called Testdroid Cloud, which is a -- it's a device cloud of

Android and iOS devices from every single market in the world, including Japan, China,

Korea, and India. We also host private device clouds for people

who don't want to share their devices. And we license our technology to companies who

build large-scale on-premise device labs. So in the next ten minutes, I'm going to tell

you how to get rid of 60 manual testers with -- and replace them with one smart one. And

this is actually something that has happened with one of our customer organizations, when

they moved from 100% manual testing using real devices to 100% automated testing using

real devices. And at the end, I'm going to have a gift for

all of you in the spirit of the holiday season. So let's start.

So if you look at the -- the charts of the most grossing apps on Android or the Google

Play today, on the top 50 -- list of the most-grossing apps, there is only one app that is not a

game. And if you look at the top 100 most grossing apps, there are only three apps that

are not games. So it's pretty obvious who is making the money on the app stores.

And usually, games are free to download, and they monetize with in-app purchases. So it

takes a little bit of time, or you have to engage the user long enough to make really

any money on that. Because currently, the average customer acquisition cost or the cost

per download on Android and iOS is around $5. So it means that you have to keep the

customer for long -- as many releases as possible in order to recover that $5 investment and

make some profit on top of it. So it's very high motivation for testing in general and

making sure that your game works on every single device out there.

So given the commercial incentive, why hasn't this all been automated a long time ago? You

know, these guys do have a lot of money. That's not the problem. The problem is that mobile

games are not really easy to automate. They are very difficult to automate.

So the main reason is that the games use direct screen access in the form of OpenGL or active

X. And they effectively bypass all the operating system controls or services you have. So that

means that any of your -- any of the mobile -- native mobile test automation frameworks

that we know can't really access any of the internal data of the game. So you only have

to resort to X and Y clicks and, you know, reading the screen buffer. You basically have

to test everything from the outside of the game. And, of course, in terms of automation,

that's pretty tricky. The second thing is that the games are very

performance-driven. So, first of all, they consume a lot of resources. So the game binaries

are two to three gigabytes today. They use a lot of network traffic, they use a lot of

memory, they use a lot of CPU, they use a lot of GPU, they utilize the sensors. So it's

pretty clear that, you know, these guys, they don't test anything on emulators and simulators.

It doesn't just make any sense. And there's another interesting incentive,

is that one of our customers told us that, you know, if they can add one more very popular

Chinese Android device on the list of their supported devices, it can bring up to 5 million

revenue over the lifetime of the game. So you can invest quite a lot on optimizing your

game on that model. So all the difficulties have actually led

to this. So the guys have resources, and it's difficult to automate games. So all the gaming

companies, the large gaming companies, have very large manual device farms on their favorite

low-cost locations. And that's -- of course, it doesn't scale very well. And that leads

to another problem, that the QA process causes delays on the actual release of the game,

which increases the time to market. And that's the real cost of the manual testing, that,

you know, things get delayed, and you don't stick to the schedule.

And, of course, even with games, there is -- there's still a lot of room for manual

testing. But that space is more for the qualitative, you know, testing of the fluidity of the game

play and that sort of aspects, not to go through every single menu on every single language

on every single device. That's the job for the automation.

So the most typical assignment we get is that, you know, guys, we only need to automate the

basic game -- basic functionality of the game, so -- on as many devices as possible and it

has to run as fast as possible, because they want to automate it on every single build.

And, of course, with these guys, the challenge is never the access to the devices. You know,

for the manual testing, they have hundreds of devices. That's not a problem. The problem

is that how to automate these, like, monotonous routines, and especially in a way that they

produce actionable -- actionable results in -- such as, like, performance data logs, screenshots,

and videos at scale. You know, with manual testing, you can't -- you can get those, like,

one by one, but you can't get it at scale. And another curve ball is that of course these

teams, they don't have, like, programming skills or background on scripting, which severely

limits the tools and the frameworks that can be used. And also, these guys -- these teams

are usually quite separated from the actual development team. So any kind of, like, white

box testing or instrumenting the game itself is not usually feasible for these guys. These

guys get the APK thrown over the fence, and it's like pure, 100% black box testing scenario.

So with these two requirements in mind, so, need for, like, very simple scripting without

any computer science skills, and the pure 100% black box approach, we started looking

at the open source space that, you know, what sort of building blocks could we find to,

like, gobble up a solution that actually solves this sort of problem.

So as a foundation of the solution, we selected Appium. And the fact that Appium is cross-platform

test automation framework works really well here, because the game -- usually the game

is exactly the same on Android and iOS. So I -- an ideal case, you can use the same script

to run your automation on both Android and iOS.

Also, Appium provides a pretty nice abstracted API that we can use for the next level of

our automation to run. So in this scenario, we cannot use any of the, like, advanced features

of Appium, like -- like the object -- inspecting the objects and those sort of things. We can

only use, like, X and Y clicks and drags. For the next level, we had to kind of figure

out three things. How -- first, how can we -- how can we know where in the game flow

we are? How can we get that info? Then, when we get that info, where should

we click next? What is the next X and Y coordinate to click?

And then after we have clicked something, did we achieve what we wanted to do? Did the

game go to the next stage as -- as we expected? So the -- to drive the execution 100% from

the outside of the game, we selected OpenCV image recognition library. So, basically,

we feed the library with screenshots and reference images. And it does, like, a pixel comparison,

pixels not matching. And when it finds the matching location, it will feed the X and

Y coordinates to the Appium script that then executes that on the real devices. It's pretty

-- The OpenCV library is very good for this, because it's customizable, it's resolution-agnostic,

which is really good on real mobile testing -- like, real device testing context, and

it can even recognize images that are stretched or, like, somehow at an angle, so you can

even test, like, 3D games as well. So the outcome is that there's only two simple

tasks that the test automation engineers need to do. They need to cut the reference images

and then change only, like, one line or -- like, one line of code to define what kind of click

needs to be done when the reference is matching or when you get the coordinates.

So then parallelizing this was quite interesting. So in the -- in the device cloud, like, remote

device cloud context, the Appium client sits on the remote machine, which is typically

a developer workstation or a mobile -- like, a (indiscernible) integration server on the

other side of the Internet where the devices are. And this -- Appium creates WebDriver

session from the Appium client to the Appium server, which is in the cloud, and then, you