Quality assurance is one of the most critical stages of launching a new product. After developing, designing, and coding software for several months, product teams need to make sure that the final version functions properly. They put their software through several rigorous tests, but this process is time-consuming.
If B2B SaaS companies rely on manual testing techniques alone, they’ll experience significant delays in the development lifecycle. That is where QA automation services come into play to mitigate these obstacles.
This article explores these nine best automation QA services for B2B SaaS companies. B2B software teams use these platforms for performing AI test automation. These tools include:
QA automation services are purpose-built tools and platforms that help automate repetitive software testing tasks. They run predetermined test scripts that simulate user interactions and responses, checking how software behaves based on the test scripts.
They’re often used for regression testing to ensure that code changes have not introduced bugs in old functionalities. They can also be used for performance testing, where they evaluate how well software scales in varying scenarios with load and stress.
QA automation services are a key part of modern workflows that help to produce software projects quickly. They help development teams augment Continuous Integration and Continuous Delivery (CI/CD) pipelines to automatically kick off a testing process after specific code changes are committed to a main branch.
This process provides instantaneous feedback to developers about the latest code commits and allows for faster, safer, and more reliable software releases. Developers can diagnose issues, triage them, and fix bugs with the detailed results and insights provided by QA services.
B2B SaaS companies can benefit from QA automation testing services in the following ways:
Test automation makes testing very fast.
Repetitive activities that once took hours for a human tester to perform can be done at the push of a button. The automation also quickens validation because an automated test runs much faster than a tester does (and, of course, it’s a lot more accurate).
By letting QA teams avoid routine time-consuming tasks, we free up testers’ time for doing things that can’t be automated, for example, complex exploratory testing that relies on the human mind and eyes.
Automating tests means that they are run through a predefined script, ensuring that the test is always run consistently and to the same degree of completeness each time.
This removes a lot of the risk created when a human is in the loop and test results therefore change each time the tests are run. This allows defects to be caught early in the development lifecycle when they will be easier to fix.
While the initial set-up of automation tools and the creation of automated test scripts add upfront costs, the long-term savings are significant. The reliance on labor is reduced, freeing up resources for more effective application.
Once created, automated tests can be rerun as many times as necessary at no additional cost, resulting in ongoing savings compared with each manual retest.
With automated testing, there are a lot more possible test cases that can be run in a lot more possible environments, configurations, data sets, and so on. That means you can cover aspects of the software, more than when it is managed with manual testing alone.
You can run complex, time-consuming, or tedious types of tests that would be impossible for humans to achieve.
When automated testing is done as part of the continuous integration and continuous delivery (CI/CD) pipeline, testing can be done for changes as soon as they are made. Automated testing can immediately show developers what an update did and if it broke anything (known as ‘broken build’).
The cycle is shorter, meaning that the overall time it takes to develop each update can be shorter, which can enable more frequent releases.
Many of the best QA automation testing services use artificial intelligence (AI) to enhance their capabilities. B2B SaaS businesses are catching on to how much smoother and faster things can go when AI steps in to help.
Supporting this trend, research has shown that the automation testing market is expected to reach $166.91 billion by 2033.
But why is AI increasingly integrated into QA automation?
AI in software testing can create test cases that are further derived from user stories, requirements, and code changes for automated execution. It can also update existing test cases over time as the application changes—test cases remain accurate and relevant without a substantial amount of manual intervention.
AI can find areas of code where bugs are more likely to be present in the future and identify issues that are still latent in the codebase. This might include testing that hasn’t run over certain files or passing tests that have caused failures to go undetected, based on patterns in the codebase and historical test results.
In this way, AI can uncover problems before they become an issue in the software.
Various machine-learning-based visual testing tools can check the visual render of an application by comparing it, at resolution, with the expected output. Deviations become visible without any previous knowledge of what to look for.
Even though specific image elements would not be described in the mobile testing documentation since they are independent of the underlying application model or business logic, For example, layout issues, color mismatches, and other rendering glitches might go undetected in more traditional testing approaches but be caught through visual testing.
Keeping various test scripts for automation updated turns out to be quite a challenge, as their frequent breakages are caused by evolving UIs and application logic. Self-healing is one of the ways to fight back by automatically adapting test scripts to changes, re-training, and reconfiguring the intelligence to handle each breaking script in the automated self-repair mode, thus easing the pressure of maintaining them.
Developers can use some of the top AI automation testing tools to identify and localize defects more accurately and to provide rich and accurate information about exactly why a test failed, which can dramatically reduce debugging time and the time to resolution.
The power of machine learning comes into play better yet with natural-language processing, where we’re using AI to improve how we interact with people.
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Here are some of the top 9 QA automation services to try:
QA.tech is an AI-driven autonomous testing platform that allows you to configure complex testing case scenarios without coding. This service stands out for its ability to customize your test cases with your actual user data.
Qualitest is the world’s second-largest pure-play QA and software testing company and the largest independent software testing company in the US. They have completed numerous test automation projects for several Fortune 500 companies and SMBs.
QualiTest pricing information is not publicly available. Contact the customer support team to learn more about pricing.
Testlio is the best QA automation company to scale your testing coverage across 500k+ devices, 800+ payment methods, 150+ countries, and 100+ languages. Testlio offers fused software testing (a combination of manual and automatic processes), which provides the flexibility needed for different aspects of building a product.
Testlio’s pricing is not publicly available. You’ll have to contact the sales team and discuss your needs before you can get a quote.
QA Wolf boasts the ability to “build automated end-to-end tests for 80% of your user flows in just 4 months, maintain them 24 hours a day, and provide unlimited parallel test runs on your infrastructure.”
Check out the pricing page for a more detailed breakdown.
TestMatick is an excellent service for verifying the functionality and usability of your software on different desktop browsers and mobile platforms. Offering a wide range of QA automation testing services, you’ll not need to look for another QA automation company.
The costs of these plans vary depending on your needs, so contact the support team to find out more.
Netdata is an open-source platform for monitoring and troubleshooting, providing high-resolution metrics, real-time visualizations, and detailed journal logs. This platform collects metrics per second and presents them in beautiful low-latency dashboards.
Mobot, short for Mobile Robot, is a versatile automation testing tool designed specifically for mobile applications. It can simulate user interactions such as clicking, dragging, and turning devices to interact with them just as an end user would.
DeviQA is a leading QA automation company offering quality assurance outsourcing and outstaffing cooperation models. In other words, they give you access to a pool of skilled QA and automation engineers for on-demand support.
DeviQA offers customized pricing quotes based on specific project requirements.
ScienceSoft brings over 30 years of experience across more than 30 industries. It provides QA automation services geared towards SaaS, web and mobile apps, and data warehouses (DWH).
Pricing is customized based on the specific requirements of each project. ScienceSoft offers personalized pricing quotes tailored to client needs.
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Using the right automation QA service is crucial to the success of your SaaS products. When used properly, these services can make your QA workflows more efficient and help you launch high-quality software faster than your competitors.
An easy-to-use QA service, like QA.tech, gives you the simplicity of running tests without coding and with the best results. It also allows you to configure tests with your real business data.
Try QA.tech’s autonomous QA solution for your business.