In this guide, we’ll explore Visual Artificial Intelligence (AI) and what it means. Read on to learn what Visual AI is, how it’s being applied today, and why it’s critical across a range of industries – and in particular for software development and testing.

From the moment we open our eyes, humans are highly visual creatures. The visual data we process today increasingly comes in digital form. Whether via a desktop, a laptop, or a smartphone, most people and businesses rely on having an incredible amount of computing power available to them and the ability to display any of millions of applications that are easy to use.

The modern digital world we live in, with so much visual data to process, would not be possible without Artificial Intelligence to help us. Visual AI is the ability for computer vision to see images in the same way a human would. As digital media becomes more and more visual, the power of AI to help us understand and process images at a massive scale has become increasingly critical.

What is AI? Background on Artificial Intelligence and Machine Learning

Artificial Intelligence refers to a computer or machine that can understand its environment and make choices to maximize its chance of achieving a goal. As a concept, AI has been with us for a long time, with our modern understanding informed by stories such as Mary Shelley’s Frankenstein and the science fiction writers of the early 20^th century. Many of the modern mathematical underpinnings of AI were advanced by English mathematician Alan Turing over 70 years ago.

Since Turing’s day, our understanding of AI has improved. However, even more crucially, the computational power available to the world has skyrocketed. AI is able to easily handle tasks today that were once only theoretical, including natural language processing (NLP), optical character recognition (OCR), and computer vision.

What is Visual Artificial Intelligence (Visual AI)?

Visual AI is the application of Artificial Intelligence to what humans see, meaning that it enables a computer to understand what is visible and make choices based on this visual understanding.

In other words, Visual AI lets computers see the world just as a human does, and make decisions and recommendations accordingly. It essentially gives software a pair of eyes and the ability to perceive the world with them.

As an example, seeing “just as a human does” means going beyond simply comparing the digital pixels in two images. This “pixel comparison” kind of analysis frequently uncovers slight “differences” that are in fact invisible – and often of no interest – to a genuine human observer. Visual AI is smart enough to understand how and when what it perceives is relevant for humans, and to make decisions accordingly.

How is Visual AI Used Today?

Visual AI is already in widespread use today, and has the potential to dramatically impact a number of markets and industries. If you’ve ever logged into your phone with Apple’s Face ID, let Google Photos automatically label your pictures, or bought a candy bar at a cashierless store like Amazon Go, you’ve engaged with Visual AI. 

Technologies like self-driving cars, medical image analysis, advanced image editing capabilities (from Photoshop tools to TikTok filters) and visual testing of software to prevent bugs are all enabled by advances in Visual AI.

How Does Visual AI Help?

One of the most powerful use cases for AI today is to complete tasks that would be repetitive or mundane for humans to do. Humans are prone to miss small details when working on repetitive tasks, whereas AI can repeatedly spot even minute changes or issues without loss of accuracy. Any issues found can then either be handled by the AI, or flagged and sent to a human for evaluation if necessary. This has the dual benefit of improving the efficiency of simple tasks and freeing up humans for more complex or creative goals.

Visual AI, then, can help humans with visual inspection of images. While there are many potential applications of Visual AI, the ability to automatically spot changes or issues without human intervention is significant. 

Cameras at Amazon Go can watch a vegetable shelf and understand both the type and the quantity of items taken by a customer. When monitoring a production line for defects, Visual AI can not only spot potential defects but understand whether they are dangerous or trivial. Similarly, Visual AI can observe the user interface of software applications to not only notice when changes are made in a frequently updated application, but also to understand when they will negatively impact the customer experience.

How Does Visual AI Help in Software Development and Testing Today?

Traditional testing methods for software testing often require a lot of manual testing. Even at organizations with sophisticated automated testing practices, validating the complete digital experience – requiring functional testing, visual testing and cross browser testing – has long been difficult to achieve with automation. 

Without an effective way to validate the whole page, Automation Engineers are stuck writing cumbersome locators and complicated assertions for every element under test. Even after that’s done, Quality Engineers and other software testers must spend a lot of time squinting at their screens, trying to ensure that no bugs were introduced in the latest release. This has to be done for every platform, every browser, and sometimes every single device their customers use. 

At the same time, software development is growing more complex. Applications have more pages to evaluate and increasingly faster – even continuous – releases that need testing. This can result in tens or even hundreds of thousands of potential screens to test (see below). Traditional testing, which scales linearly with the resources allocated to it, simply cannot scale to meet this demand. Organizations relying on traditional methods are forced to either slow down releases or reduce their test coverage.

At Applitools, we believe AI can transform the way software is developed and tested today. That’s why we invented Visual AI for software testing. We’ve trained our AI on over a billion images and use numerous machine learning and AI algorithms to deliver 99.9999% accuracy. Using our Visual AI, you can achieve automated testing that scales with you, no matter how many pages or browsers you need to test. 

That means Automation Engineers can quickly take snapshots that Visual AI can analyze rather than writing endless assertions. It means manual testers will only need to evaluate the issues Visual AI presents to them rather than hunt down every edge and corner case. Most importantly, it means organizations can release better quality software far faster than they could without it.

How Visual AI Enables Cross Browser/Cross Device Testing

Additionally, due to the high level of accuracy, and efficient validation of the entire screen, Visual AI opens the door to simplifying and accelerating the challenges of cross browser and cross device testing. Leveraging an approach for ‘rendering’ rather than ‘executing’ across all the device/browser combinations, teams can get test results 18.2x faster using the Applitools Ultrafast Test Cloud than traditional execution grids or device farms.

How Will Visual AI Advance in the Future?

As computing power increases and algorithms are refined, the impact of Artificial Intelligence, and Visual AI in particular, will only continue to grow.

In the world of software testing, we’re excited to use Visual AI to move past simply improving automated testing – we are paving the way towards autonomous testing. For this vision (no pun intended), we have been repeatedly recognized as an industry leader by the industry and our customers.

Keep Reading: More about Visual AI and Visual Testing

What is Visual Testing (blog)

The Path to Autonomous Testing (video)

What is Applitools Visual AI (learn)

Why Visual AI Beats Pixel and DOM Diffs for Web App Testing (article)

How AI Can Help Address Modern Software Testing (blog)

The Impact of Visual AI on Test Automation (report)

How Visual AI Accelerates Release Velocity (blog)

Modern Functional Test Automation Through Visual AI (free course)

Computer Vision defined (Wikipedia)

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Learn why unit testing isn’t only for developers, the importance of unit testing to software testers and quality engineers, and how you can improve your skills by building better unit tests.

The responsibility for product quality frequently falls on software testers. Yet, software testers are often divorced or even excluded from conversations around the cheapest and easiest way to inject quality into the product and the entire software development lifecycle, right from the beginning: unit testing. In this article, we’ll explore why it’s important for software testers to be able to speak clearly about unit tests and how this can help deliver better quality.

Why Unit Tests Are Important

Unit tests form the solid base of the testing pyramid. They are the cheapest kinds of tests to run, and can be run frequently throughout the deployment pipeline. Unit tests allow us to find errors the soonest, and to fix them before they bubble up in other, more expensive kinds of testing like functional or UI tests, which take much longer to complete and run than unit tests.

Unit Testing Frameworks

Most developers know how to write unit tests in the language in which they develop, and most languages have several libraries to choose from, depending on the type and complexity of testing. For example, Python has pytest, pyunit, unittest(inspired by Java’s JUnit), Nose2, and hypothesis (for property tests, a non-example based type of unit test). These are just some of the choices available, and every language has a number of possible unit testing frameworks to choose from.

You don’t need to know everything about a unit testing library, or even how to write unit tests, to get value from understanding the basics of the unit testing framework. A lot of value can be gained from knowing what framework is being used, and what kinds of assertions can be made within the framework. Also, does the framework support table tests or property-style tests? Understanding what is supported can help you better understand what aspects of your test design might be best handled in the unit-testing phase. 

Unit Testing Is the Developers Job

Yes, developers typically write unit tests. However, they are largely responsible for writing these tests to ensure that the code works – most developer tests are likely to cover happy-path and obvious negative cases. They may not think to write tests for edge or corner cases, as they are working to meet deadlines for code delivery. This is where software testers with unit test knowledge can help to make the unit tests more robust, and perhaps decrease testing that might otherwise be done at integration or functional levels.

The first step, if you are unfamiliar with the code, is to request a walkthrough of the unit tests. Understanding what developers have done and what they are testing will help you to make recommendations about what other tests might be included. Remember, adding tests here is the cheapest and fastest place to do it, especially if there are tests you want run quickly on every code change that a developer makes. 

If you are familiar with the codebase and version control systems, then you can also look for the unit tests in the code. These are often stored in a test directory, and typically named so it is easy to identify what is being tested. Quality teams can be coached to review unit tests, and compare those with their test plans. Once coached, teams can make recommendations to developers to improve unit tests and make test suites more robust. Some team members may even expand their skills by adding tests and making pull requests/merge requests for unit tests. There are many ways to participate in making unit tests more effective, involving writing no code or writing a lot of code; it’s up to you to decide what most benefits you and your team. 

But What if There Are No Unit Tests?

If you are responsible for software quality and you discover that your team or company is not doing unit testing, this can be both painful, but also a great opportunity for growth. The first step in having the conversations around developing unit tests can revolve around the efficiency, efficacy, and speed of unit tests. The next step is building awareness and fluency about quality and testing as a part of development, which is a difficult task to tackle alone, and it may not work without buy-in from key people. However, if you can get understanding and buy-in on the importance of building testing and testability into the product starting with unit-tests as the foundation, further discussions about code quality can be opened up.

Better Quality is the Goal

At the end of every day, every member of the team should be responsible for quality. However, that responsibility rests with different people in different organizations, and often with the person who has the word “quality” in their title is the person who is ultimately held responsible in the end. If you are responsible for quality, understanding the basics of how unit tests work in your code base will help you to have better discussions with developers about how to improve software quality in the fastest, cheapest way possible – directly from the code.

The post Why Should Software Testers Understand Unit Testing? appeared first on Automated Visual Testing | Applitools.

Every year, pundits and critics offer their predictions for the year ahead. Here are my predictions for test automation in 2021. (Note: these are my personal predictions)

Prediction 1: Stand-alone QA faces challenges of dev teams with integrated quality engineering

Photo by CHUTTERSNAP on Unsplash

Teams running Continuous Integration/Continuous Deployment (CICD) have learned that developers must own the quality of their code. In 2021, everyone else will figure that out, too. Engineers know that the delay between developing code and finding bugs produces inefficient development teams. Companies running standalone QA teams find bugs later than teams with integrated quality. In 2021, this difference will begin to become painful as more companies adopt quality engineering in the midst of development.

Prediction 2: Development teams will own core test automation

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Dev owns test automation across many CICD teams. With more quality responsibility, more of the development teams will build test automation. Because they use JavaScript in development, front-end teams will choose JavaScript as the prime test automation language. As a result, Selenium JavaScript and Cypress adoption will grow, with Cypress seeing the most increase.

Prediction 3: Primary test automation moves to build

Photo by ThisisEngineering RAEng on Unsplash

In 2021, core testing will occur during code build. In past test approaches, unit tests ran independently of system-level and full end-to-end integration tests. Quality engineers wrote much of the end-to-end test code. When bugs got discovered at the end, developers had to stop what they were doing to jump back and fix code. With bugs located at build time, developer productivity increases as they improve what they just checked in real-time.

Prediction 4: Speed+Coverage as the driving test metric

Photo by Marc Sendra Martorell on Unsplash

As more testing moves to build, speed matters. Every minute needed to validate the build wastes engineering time. Check-in tests will require parallel testing for the unit, system, and end-to-end tests. Sure, test speed matters. What about redundant tests? Each test must validate unique aspects of the code. Developers will need to use existing tools or new tools to measure the fraction of unexercised code in their test suites.

Prediction 5: AI assists in selecting tests to ensure coverage

Photo by Hitesh Choudhary on Unsplash

To speed up testing, development teams will look to eliminate redundant tests. They will look to AI tools to generate test conditions, standardize test setup, and identify both untested code and redundancy in tests. You can look up a range of companies adding AI to test flows for test generation and refactoring. Companies adopting this technology will attempt to maximize test coverage as they speed up testing.

Prediction 6: Visual AI Page Checks Grows 10x

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I’m making this prediction based on feedback from Applitools Visual AI customers. Each year, Applitools tracks the number of pages using Visual AI for validation. We continue to see exponential growth in visual AI use within our existing customers. The biggest driver for this growth in usage follows through from the next two predictions about Visual AI utility.

Prediction 7: Visual tests on every check-in

Photo by Larissa Gies on Unsplash

When companies adopt visual testing, they often add visual validation to their end-to-end tests. At some point, every company realizes that bug discovery must come sooner. They want to uncover bugs at check-in, so developers can fix their code while it remains fresh in their minds. Visual AI provides the accuracy to provide visual validation on code build and code merge – letting engineers validate both the behavior and rendering of their code within the build process.

Prediction 8: Visual tests run with unit tests

Photo by Wesley Tingey on Unsplash

Engineers treat their unit tests as sanity checks.  They run unit tests regularly and only check results when the tests fail. Why not automate unit tests for the UI? Many Applitools customers have been running visual validation alongside standard unit tests. Visual AI, unlike pixel diffs and DOM diffs, provides high accuracy validation for visual components and mocks. With the Ultrafast Test Platform, these checks can be validated across multiple platforms with just a single code run. Many more Applitools customers will adopt visual unit testing in 2021.

Prediction 9: The gap between automation haves and have-nots will grow

Photo by Brett Jordan on Unsplash

As more development teams own test automation, we will see a stark divide between legacy and modern approaches. Modern teams will deliver features and capabilities more quickly with the quality demanded by users. Legacy teams will struggle to keep up; they will choose between quality and speed and continue to fall behind in reputation.

Where Do You See The Future?

These are nine predictions I see. What do you see? How will you get ahead of your competition in 2021? How will you keep from falling behind? What will matter to your business?

Each of us makes predictions and then sees how they come to fruition. Let’s check back in a year and see how each of us did.

Featured Photo by Sasha • Stories on Unsplash

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