Choosing the Right AI Testing Tool

Jul 8, 2022

Software testing is crucial in ensuring customer satisfaction while safeguarding your application against potential failures. It also involves assessing and analyzing an application under certain conditions to ensure it meets specific standards. With the software development process getting more complex, quality assurance (QA) and software testers have not hesitated to leverage the benefits of Artificial Intelligence (AI). Research shows that 90% of QA teams want to integrate AI testing into their organizations.

AI brings new and creative intelligence irrespective of where it’s implemented. It emulates human intelligence and machine learning (ML), allowing machines to learn and adapt naturally without human intervention. So, why should you consider AI when choosing an optimal testing tool? How does it help in testing? Let’s help you understand what AI for software testing entails and how you can leverage its benefits.

What is AI Testing?

AI testing is a software testing approach that uses tools that leverage AI and Machine learning for automated software testing. These tools can overcome common problems and make the testing process more innovative and highly effective. Technically, AI driven testing ensures more accurate testing, which leads to the delivery of higher quality software. Since AI

powered test automation use algorithms that allow them to automatically test and improve software without human intervention, it reduces the hurdle of manual testing and other non-testing activities like test maintenance, leading to better results.

How has AI Testing Evolved?

In the early days of software testing, it mainly focused on the software’s functionality. However, that’s no longer the case. Testing has, instead, become a crucial activity in assessing an application’s quality. This created the need for more accurate and effective testing methodologies to catch up with the fast-paced IT world. Further, the emergence of IT-based testing tools proved to be the most effective way of ensuring high-volume testing and improving the application’s quality.

Integrating Intelligence in software testing tools aims to simplify the software development lifecycle. AI and ML can help develop algorithms that access data, learn from it, and use it to make predictions that streamline the software testing process. With AI making its way into the software development lifecycle, many organizations are adopting tools to help automate the tedious tasks in development and testing. Therefore, as technology advances and machines get trained to perform higher-order tasks, AI-based testing will likely take over the software testing industry.

AI Driven Testing Benefits

As more organizations are moving towards implementing automation tools, most of them are turning towards AI. The use of AI in software development has helped address the limitations of software test automation tools and provided numerous benefits to businesses. Here are the benefits of leveraging Artificial Intelligence in software testing.

1. High Level of Accuracy

Humans tend to make mistakes, especially when it pertains to doing the same tasks repetitively. Therefore, manual tests are prone to human errors, leading to inaccurate results. AI powered test automation helps in not only handling monotonous tasks but also in capturing, recording, and analyzing data with greater efficiency. Therefore, AI ensures that results are recorded more accurately by removing the slightest chance of errors.

2. Increase in Overall Test Coverage

AI powered test automation can perform various test cases in every test run. This delivers adequate test coverage, resulting in general improvement of software quality. AI testing can look deeper into internal program statistics, data tables, memories, and file contents to determine whether the software is performing as expected. Typically, these tools go beyond the limitations of manual testing by increasing the overall scope and depth of tests.

3. Saves on Time, Money, and Efforts

Every time the source code is modified, a new test project arises. This means that manual testing involves recurring work, which can be time-consuming and extremely costly. However automated tests can be executed repeatedly without any additional costs at a much faster rate. This significantly cuts the amount of time and money spent on repeated jobs.

4. Faster Time to Market

With rapid deployment needs, you’ll not want to run tests that take forever to complete. Therefore, by integrating AI in software testing, you can reduce the overall testing timespan from days to a few hours. Outcome:

  • Determining only the few of tests needed to be executed after a change in the source code
  • Optimizing your test data management strategy to ensure that quality data reaches the team faster
  • Identifying and forgoing the execution of unnecessary or duplicate test cases

This improves developer velocity and helps teams hit tight release schedules faster and more confidently.

5. Defect Tracking

In manual deployment testing, bugs or errors can go unnoticed for quite a long time, leading to devastating consequences. This is because as the software and data grow, the number of vulnerabilities also increases, creating the need for early and fast bug identification. AI driven testing can help identify bugs and errors in seconds. It also allows the test automation tool to predict the defects early on, then alert developers and tester. This then enables you to make a reliable, bug-free product for the end-users.

6. Allows Visual Testing

With image and pattern recognition capabilities, AI-based testing systems can perform visual testing on applications to detect visual bugs. AI can identify the various dynamic UI controls and analyze them at a pixel level. Therefore, AI-based testing can help ensure that all visual elements interact and function correctly, irrespective of their shape and size.

An isometric image of a woman and a machine using AI testing software.
AI working and testing faster than a human.

How is AI Testing used?

There are different AI driven testing tools with varying steps in their testing lifecycle. However, all these tools aim to optimize automated deployment testing by eliminating bottlenecks hindering their efficiency. The common problems they address include:

  • Slow test execution
  • Duplicate efforts in testing
  • Excessive test maintenance
  • Creation of high-quality test cases
  • Lastly, insufficient test coverage

Typically, AI and ML can help you at various stages of software testing. There are four major approaches to AI implementation in software testing, including:

1. Visual Testing

This involves testing the look and feel of an application by utilizing image-based processing and screen comparison algorithms. Visual testing then ensures that the UI of an application appears to the end-user as per the requirements, improving the user experience (UX).

2. Differential Testing

Differential testing is a technique that involves classifying differences and comparing the versions of an application after every build. This is achieved by applying the same input to similar applications and taking note of the differences. Therefore, AI-based systems can help identify code-related issues, regressions, and security vulnerabilities faster and earlier.

3. Declarative Testing

This involves enhancing test automation productivity and stability by focusing on the intent of testing. AI powered test automation tools perform the test in a natural or domain-specific language, allowing you to compare the app’s final state to its expected state. These tools can, indeed, eliminate tedious, error-prone, and repetitive tasks.

4. Self-Healing Automation

As the term suggests, this encompasses automatically updating element changes or any other modifications in the environment of an application. AI testing tools track these changes and accordingly update the test cases. This then allows tests to automatically update without the need for human intervention.

AI Testing with Appsurify, Inc

Artificial intelligence is the next big thing to help solve the persistent problems in software development and testing, including slow execution of tests, excessive test maintenance, and insufficient test coverage. AI takes over the repetitive tasks and reduces the direct involvement of QA testers. Therefore, by using AI powered tools, you can increase the business value of your organization by testing smartly and more efficiently while saving on costs and resources.

Whether you would like to implement an efficient QA automation tool or eliminate flaky tests, Appsurify is here to help you with all your QA needs. Contact us today to get help in streamlining your QA process and produce greater testing rewards for less money with our AI powered test automation tools.