Testing Blog
The Google Test and Development Environment - Pt. 3: Code, Build, and Test
Tuesday, January 21, 2014
by
Anthony Vallone
This is the third in a series of articles about our work environment. See the
first
and
second
.
I will never forget the awe I felt when running my first load test on my first project at Google. At previous companies I’ve worked, running a substantial load test took quite a bit of resource planning and preparation. At Google, I wrote less than 100 lines of code and was simulating tens of thousands of users after just minutes of prep work. The ease with which I was able to accomplish this is due to the impressive coding, building, and testing tools available at Google. In this article, I will discuss these tools and how they affect our test and development process.
Coding and building
The tools and process for coding and building make it very easy to change production and test code. Even though we are a large company, we have managed to remain nimble. In a matter of minutes or hours, you can edit, test, review, and submit code to head. We have achieved this without sacrificing code quality by heavily investing in tools, testing, and infrastructure, and by prioritizing code reviews.
Most production and test code is in a single, company-wide source control repository (open source projects like Chromium and Android have their own). There is a great deal of code sharing in the codebase, and this provides an incredible suite of code to build on. Most code is also in a single branch, so the majority of development is done at head. All code is also navigable, searchable, and editable from the browser. You’ll find code in numerous languages, but Java, C++, Python, Go, and JavaScript are the most common.
Have a strong preference for editor? Engineers are free to choose from many IDEs and editors. The most common are Eclipse, Emacs, Vim, and IntelliJ, but many others are used as well. Engineers that are passionate about their prefered editors have built up and shared some truly impressive editor plugins/tooling over the years.
Code reviews for all submissions are enforced via source control tooling. This also applies to test code, as our test code is held to the same standards as production code. The reviews are done via web-based code review tools that even include automatically generated test results. The process is very streamlined and efficient. Engineers can change and submit code in any part of the repository, but it must get reviewed by owners of the code being changed. This is great, because you can easily change code that your team depends on, rather than merely request a change to code you do not own.
The
Google build system
is used for building most code, and it is designed to work across many languages and platforms. It is remarkably simple to define and build targets. You won’t be needing that old Makefile book.
Running jobs and tests
We have some pretty amazing machine and job management tools at Google. There is a generally available pool of machines in many data centers around the globe. The job management service makes it very easy to start jobs on arbitrary machines in any of these data centers. Failing machines are automatically removed from the pool, so tests rarely fail due to machine issues. With a little effort, you can also set up monitoring and pager alerting for your important jobs.
From any machine you can spin up a massive number of tests and run them in parallel across many machines in the pool, via a single command. Each of these tests are run in a standard, isolated environment, so we rarely run into the “it works on my machine!” issue.
Before code is submitted,
presubmit
tests can be run that will find all tests that depend transitively on the change and run them. You can also define presubmit rules that run checks on a code change and verify that tests were run before allowing submission.
Once you’ve submitted test code, the build and test system automatically registers the test, and starts building/testing continuously. If the test starts failing, your team will get notification emails. You can also visit a test dashboard for your team and get details about test runs and test data. Monitoring the build/test status is made even easier with our build orbs designed and built by Googlers. These small devices will glow red if the build starts failing. Many teams have had fun customizing these orbs to various shapes, including a statue of liberty with a glowing torch.
Statue of LORBerty
Running larger integration and end-to-end tests takes a little more work, but we have some excellent tools to help with these tests as well: Integration test runners, hermetic environment creation, virtual machine service, web test frameworks, etc.
The impact
So how do these tools actually affect our productivity? For starters, the code is easy to find, edit, review, and submit. Engineers are free to choose tools that make them most productive. Before and after submission, running small tests is trivial, and running large tests is relatively easy. Since tests are easy to create and run, it’s fairly simple to maintain a green build, which most teams do most of the time. This allows us to spend more time on real problems and less on the things that shouldn’t even be problems. It allows us to focus on creating rigorous tests. It dramatically accelerates the development process that can
prototype Gmail in a day
and code/test/release service features on a daily schedule. And, of course, it lets us focus on the fun stuff.
Thoughts?
We are interested to hear your thoughts on this topic. Google has the resources to build tools like this, but would small or medium size companies benefit from a similar investment in its infrastructure? Did Google create the infrastructure or did the infrastructure create Google?
8 comments
The Google Test and Development Environment - Pt. 2: Dogfooding and Office Software
Friday, January 03, 2014
by
Anthony Vallone
This is the second in a series of articles about our work environment. See the
first
.
There are few things as frustrating as getting hampered in your work by a bug in a product you depend on. What if it’s a product developed by your company? Do you report/fix the issue or just work around it and hope it’ll go away soon? In this article, I’ll cover how and why Google
dogfoods
its own products.
Dogfooding
Google makes heavy use of its own products. We have a large ecosystem of development/office tools and use them for nearly everything we do. Because we use them on a daily basis, we can dogfood releases company-wide before launching to the public. These dogfood versions often have features unavailable to the public but may be less stable. Instability is exactly what you want in your tools, right? Or, would you rather that frustration be passed on to your company’s customers? Of course not!
Dogfooding is an important part of our test process. Test teams do their best to find problems before dogfooding, but we all know that testing is never perfect. We often get dogfood bug reports for edge and corner cases not initially covered by testing. We also get many comments about overall product quality and usability. This internal feedback has, on many occasions, changed product design.
Not surprisingly, test-focused engineers often have a lot to say during the dogfood phase. I don’t think there is a single public-facing product that I have not reported bugs on. I really appreciate the fact that I can provide feedback on so many products before release.
Interested in helping to test Google products? Many of our products have feedback links built-in. Some also have Beta releases available. For example, you can start using
Chrome Beta
and help us file bugs.
Office software
From system design documents, to test plans, to discussions about beer brewing techniques, our products are used internally. A company’s choice of office tools can have a big impact on productivity, and it is fortunate for Google that we have such a comprehensive suite. The tools have a consistently simple UI (no manual required), perform very well, encourage collaboration, and auto-save in the cloud. Now that I am used to these tools, I would certainly have a hard time going back to the tools of previous companies I have worked. I’m sure I would forget to click the save buttons for years to come.
Examples of frequently used tools by engineers:
Google Drive Apps
(Docs, Sheets, Slides, etc.) are used for design documents, test plans, project data, data analysis, presentations, and more.
Gmail
and
Hangouts
are used for email and chat.
Google Calendar
is used to schedule all meetings, reserve conference rooms, and setup video conferencing using Hangouts.
Google Maps
is used to map office floors.
Google Groups
are used for email lists.
Google Sites
are used to host team pages, engineering docs, and more.
Google App Engine
hosts many corporate, development, and test apps.
Chrome
is our primary browser on all platforms.
Google+
is used for organizing internal communities on topics such as food or C++, and for socializing.
Thoughts?
We are interested to hear your thoughts on this topic. Do you dogfood your company’s products? Do your office tools help or hinder your productivity? What office software and tools do you find invaluable for your job? Could you use Google Docs/Sheets for large test plans?
(Continue to part 3)
12 comments
The Google Test and Development Environment - Pt. 1: Office and Equipment
Friday, December 20, 2013
by
Anthony Vallone
When conducting interviews, I often get questions about our workspace and engineering environment. What IDEs do you use? What programming languages are most common? What kind of tools do you have for testing? What does the workspace look like?
Google is a company that is constantly pushing to improve itself. Just like software development itself, most environment improvements happen via a bottom-up approach. All engineers are responsible for fine-tuning, experimenting with, and improving our process, with a goal of eliminating barriers to creating products that amaze.
Office space and engineering equipment can have a considerable impact on productivity. I’ll focus on these areas of our work environment in this first article of a series on the topic.
Office layout
Google is a highly collaborative workplace, so the open floor plan suits our engineering process. Project teams composed of Software Engineers (SWEs), Software Engineers in Test (SETs), and Test Engineers (TEs) all sit near each other or in large rooms together. The test-focused engineers are involved in every step of the development process, so it’s critical for them to sit with the product developers. This keeps the lines of communication open.
Google Munich
The office space is far from rigid, and teams often rearrange desks to suit their preferences. The facilities team recently finished renovating a new floor in the New York City office, and after a day of engineering debates on optimal arrangements and white board diagrams, the floor was completely transformed.
Besides the main office areas, there are lounge areas to which Googlers go for a change of scenery or a little peace and quiet. If you are trying to avoid becoming a casualty of The Great Foam Dart War, lounges are a great place to hide.
Google Dublin
Working with remote teams
Google’s worldwide headquarters is in Mountain View, CA, but it’s a very global company, and our project teams are often distributed across multiple sites. To help keep teams well connected, most of our conference rooms have video conferencing equipment. We make frequent use of this equipment for team meetings, presentations, and quick chats.
Google Boston
What’s at your desk?
All engineers get high-end machines and have easy access to data center machines for running large tasks. A new member on my team recently mentioned that his Google machine has 16 times the memory of the machine at his previous company.
Most Google code runs on Linux, so the majority of development is done on Linux workstations. However, those that work on client code for Windows, OS X, or mobile, develop on relevant OSes. For displays, each engineer has a choice of either two 24 inch monitors or one 30 inch monitor. We also get our choice of laptop, picking from various models of Chromebook, MacBook, or Linux. These come in handy when going to meetings, lounges, or working remotely.
Google Zurich
Thoughts?
We are interested to hear your thoughts on this topic. Do you prefer an open-office layout, cubicles, or private offices? Should test teams be embedded with development teams, or should they operate separately? Do the benefits of offering engineers high-end equipment outweigh the costs?
(Continue to part 2)
20 comments
WebRTC Audio Quality Testing
Friday, November 08, 2013
by Patrik Höglund
The
WebRTC project
is all about enabling peer-to-peer video, voice and data transfer in the browser. To give our users the best possible experience we need to adapt the quality of the media to the bandwidth and processing power we have available. Our users encounter a wide variety of network conditions and run on a variety of devices, from powerful desktop machines with a wired broadband connection to laptops on WiFi to mobile phones on spotty 3G networks.
We want to ensure good quality for all these use cases in our implementation in Chrome. To some extent we can do this with manual testing, but the breakneck pace of Chrome development makes it very hard to keep up (several hundred patches land every day)! Therefore, we'd like to test the quality of our video and voice transfer with an automated test. Ideally, we’d like to test for the most common network scenarios our users encounter, but to start we chose to implement a test where we have plenty of CPU and bandwidth. This article covers how we built such a test.
Quality Metrics
First, we must define what we want to measure. For instance, the
WebRTC video quality test
uses
peak signal-to-noise ratio
and
structural similarity
to measure the quality of the video (or to be more precise, how much the output video differs from the input video; see
this GTAC 13 talk
for more details). The quality of the user experience is a subjective thing though. Arguably, one probably needs dozens of different metrics to really ensure a good user experience. For video, we would have to (at the very least) have some measure for frame rate and resolution besides correctness. To have the system send somewhat correct video frames seemed the most important though, which is why we chose the above metrics.
For this test we wanted to start with a similar correctness metric, but for audio. It turns out there's an algorithm called
Perceptual Evaluation of Speech Quality
(PESQ) which analyzes two audio files and tell you how similar they are, while taking into account how the human ear works (so it ignores differences a normal person would not hear anyway). That's great, since we want our metrics to measure the user experience as much as possible. There are many aspects of voice transfer you could measure, such as latency (which is really important for voice calls), but for now we'll focus on measuring how much a voice audio stream gets distorted by the transfer.
Feeding Audio Into WebRTC
In the WebRTC case we already
had a test
which would launch a Chrome browser, open two tabs, get the tabs talking to each other through a signaling server and set up a call on a single machine. Then we just needed to figure out how to feed a reference audio file into a WebRTC call and record what comes out on the other end. This part was actually harder than it sounds. The main WebRTC use case is that the web page acquires the user's mic through
getUserMedia
, sets up a PeerConnection with some remote peer and sends the audio from the mic through the connection to the peer where it is played in the peer's audio output device.
WebRTC calls transmit voice, video and data peer-to-peer, over the Internet.
But since this is an automated test, of course we could not have someone speak in a microphone every time the test runs; we had to feed in a known input file, so we had something to compare the recorded output audio against.
Could we duct-tape a small stereo to the mic and play our audio file on the stereo? That's not very maintainable or reliable, not to mention annoying for anyone in the vicinity. What about some kind of fake device driver which makes a microphone-like device appear on the device level? The problem with that is that it's hard to control a driver from the userspace test program. Also, the test will be more complex and flaky, and the driver interaction will not be portable.
[1]
Instead, we chose to sidestep this problem. We used a solution where we load an audio file with
WebAudio
and play that straight into the peer connection through the
WebAudio-PeerConnection integration
. That way we start the playing of the file from the same renderer process as the call itself, which made it a lot easier to time the start and end of the file. We still needed to be careful to avoid playing the file too early or too late, so we don't clip the audio at the start or end - that would destroy our PESQ scores! - but it turned out to be a workable approach.
[2]
Recording the Output
Alright, so now we could get a WebRTC call set up with a known audio file with decent control of when the file starts playing. Now we had to record the output. There are a number of possible solutions. The most end-to-end way is to straight up record what the system sends to default audio out (like speakers or headphones). Alternatively, we could write a hook in our application to dump our audio as late as possible, like when we're just about to send it to the sound card.
We went with the former. Our colleagues in the Chrome video stack team in Kirkland had already found that it's possible to configure a Windows or Linux machine to send the system's audio output (i.e. what plays on the speakers) to a virtual recording device. If we make that virtual recording device the default one, simply invoking SoundRecorder.exe and arecord respectively will record what the system is playing out.
They found this works well if one also uses the
sox
utility to eliminate silence around the actual audio content (recall we had some safety margins at both ends to ensure we record the whole input file as playing through the WebRTC call). We adopted the same approach, since it records what the user would hear, and yet uses only standard tools. This means we don't have to install additional software on the myriad machines that will run this test.
[3]
Analyzing Audio
The only remaining step was to compare the silence-eliminated recording with the input file. When we first did this, we got a really bad score (like 2.0 out of 5.0, which means PESQ thinks it’s barely legible). This didn't seem to make sense, since both the input and recording sounded very similar. Turns out we didn’t think about the following:
We were comparing a full-band (24 kHz) input file to a wide-band (8 kHz) result (although both files were sampled at 48 kHz). This essentially amounted to a low pass filtering of the result file.
Both files were in stereo, but PESQ is only mono-aware.
The files were 32-bit, but the PESQ implementation is designed for 16 bits.
As you can see, it’s important to pay attention to what format arecord and SoundRecorder.exe records in, and make sure the input file is recorded in the same way. After correcting the input file and “rebasing”, we got the score up to about 4.0.
[4]
Thus, we ended up with an automated test that runs continously on the torrent of Chrome change lists and protects WebRTC's ability to transmit sound. You can see the
finished code here
. With automated tests and cleverly chosen metrics you can protect against most regressions a user would notice. If your product includes video and audio handling, such a test is a great addition to your testing mix.
How the components of the test fit together.
Future work
It might be possible to write a Chrome extension which dumps the audio from Chrome to a file. That way we get a simpler-to-maintain and portable solution. It would be less end-to-end but more than worth it due to the simplified maintenance and setup. Also, the recording tools we use are not perfect and add some distortion, which makes the score less accurate.
There are other algorithms than PESQ to consider - for instance,
POLQA
is the successor to PESQ and is better at analyzing high-bandwidth audio signals.
We are working on a solution which will run this test under simulated network conditions. Simulated networks combined with this test is a really powerful way to test our behavior under various packet loss and delay scenarios and ensure we deliver a good experience to all our users, not just those with great broadband connections. Stay tuned for future articles on that topic!
Investigate feasibility of running this set-up on mobile devices.
1
It would be tolerable if the driver was just looping the input file, eliminating the need for the test to control the driver (i.e. the test doesn't have to tell the driver to start playing the file). This is actually what we do in the video quality test. It's a much better fit to take this approach on the video side since each recorded video frame is independent of the others. We can easily embed barcodes into each frame and evaluate them independently.
This seems much harder for audio. We could possibly do
audio watermarking
, or we could embed a kind of start marker (for instance, using DTMF tones) in the first two seconds of the input file and play the real content after that, and then do some fancy audio processing on the receiving end to figure out the start and end of the input audio. We chose not to pursue this approach due to its complexity.
2
Unfortunately, this also means we will not test the capturer path (which handles microphones, etc in WebRTC). This is an example of the frequent tradeoffs one has to do when designing an end-to-end test. Often we have to trade end-to-endness (how close the test is to the user experience) with robustness and simplicity of a test. It's not worth it to cover 5% more of the code if the test become unreliable or radically more expensive to maintain. Another example: A WebRTC call will generally involve two peers on different devices separated by the real-world internet. Writing such a test and making it reliable would be extremely difficult, so we make the test single-machine and hope we catch most of the bugs anyway.
3
It's important to keep the continuous build setup simple and the build machines easy to configure - otherwise you will inevitably pay a heavy price in maintenance when you try to scale your testing up.
4
When sending audio over the internet, we have to compress it since lossless audio consumes way too much bandwidth. WebRTC audio generally sounds great, but there's still compression artifacts if you listen closely (and, in fact, the recording tools are not perfect and add some distorsion as well). Given that this test is more about detecting regressions than measuring some absolute notion of quality, we'd like to downplay those artifacts. As our Kirkland colleagues found, one of the ways to do that is to "rebase" the input file. That means we start with a pristine recording, feed that through the WebRTC call and record what comes out on the other end. After manually verifying the quality, we use that as our input file for the actual test. In our case, it pushed our PESQ score up from 3 to about 4 (out of 5), which gives us a bit more sensitivity to regressions.
16 comments
Espresso for Android is here!
Friday, October 18, 2013
Cross-posted from the
Android Developers Google+ Page
Earlier this year, we presented Espresso at GTAC as a solution to the UI testing problem. Today we are announcing the launch of the developer preview for Espresso!
The compelling thing about developing Espresso was making it easy and fun for developers to write reliable UI tests. Espresso has a small, predictable, and easy to learn API, which is still open for customization. But most importantly - Espresso removes the need to think about the complexity of multi-threaded testing. With Espresso, you can think procedurally and write concise, beautiful, and reliable Android UI tests quickly.
Espresso is now being used by over 30 applications within Google (Drive, Maps and G+, just to name a few). Starting from today, Espresso will also be available to our great developer community. We hope you will also enjoy testing your applications with Espresso and looking forward to your feedback and contributions!
Android Test Kit:
https://code.google.com/p/android-test-kit/
14 comments
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