The Complete Guide to AI For Software Testing


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AI is being used in those domains that dominate humans' roles and are equally or more dominant than humans now. There is barely a sector where AI is not used these days. It is used in the health and pharma fields, technology, and many more. AI for software testing is also being preferred by many organizations these days.

AI for software testing provides a tester with tools that ease the process by enabling the machine or the software to make a few decisions by itself. Using AI for software testing aims to make the software testing process easier, faster, relevant, and yield fantastic outcomes. It enables testers with a planned platform that helps them take full advantage of AI and use it to perform testing tasks and generate superb results.

The concept of using AI for software also adds to Automation testing, QA testing, and many more. AI and ML are used for this purpose. Using their algorithm in the testing process makes it easier and faster because such algorithms train the machine or the software to make decisions through logical decision-making and time-efficient problem-solving methods.

Software testing has evolved over the past years. The techniques and methods for testing have updated tremendously. Automated tools like Selenium are in high usage and demand so far. Automation testing has no code testing, making it easier for the non-programmers software testers. But to keep up with such technological changes and evolution, it’s important to choose those methodologies for testing that are time-efficient and yield faster results. This is where AI comes into the picture. 

AI is a study of the human brain, and it understands and copies what the human brain does in different scenarios and takes actions accordingly. ML is a branch of AI that helps the hardware learn quickly without any intervention from humans. Both AI and ML have algorithms that identify patterns from data and make decisions accordingly. It extracts the previously performed test cases on the projects and decides which test cases to be chosen, considering that they are efficient and time-saving for particular testing.

Also read: AI Testing in Software Testing

Advantages of Using AI For Software Testing

The advantages of using AI for software testing are:

  1. Better accuracy: The human-prone errors that are more prominent in manual testing and less prominent in automation testing can be minimized effectively with AI. Since these errors occur in repetitive tasks, AI handles such tasks more effectively and minimizes such errors.
  2. Limited defects: AI identifies bugs in the early stages of software development and tests them, which helps the end-users and testers.
  3. Time and cost-efficient: Whenever there is a change in the source code, testing needs to be performed again to ensure that the changes do not affect the existing code. So, AI handles the repetitive tasks effortlessly and lessens the time and effort of testing.
  4. Improved coverage of tests: AI in software testing also checks for the internal states of tests. It checks the delivery of test cases and checks if the code does what it is supposed to do.

Also read: Role of AI and Bots in Software Testing

Popular AI Tools For Software Testing

Since AI is used in software testing, some popular AI tools are used too. They are:

  1. Testim: It falls under the self-healing tool category. It helps in overcoming the issues of flakiness and maintenance in automation testing. It uses algorithms of ML and AI to fix these issues.
  2. Applitools: It is a visual AI-testing tool. It has less maintenance. It works to keep up with the cross-browser and cross-device to provide an amazing UI. The continuous upgrading of the screens and devices makes it harder to design several different user interfaces. This tool speeds up the visual testing by 25 times.
  3. UIPath test suite: it is a declarative tool. It is used in ensuring high-quality automation. It consists of various tools and interfaces that help organizations and businesses in performing 360-degree testing. It also contributes to the launching of resilient robots.
  4. Launchable: It is a differentiable tool. It uses algorithms of ML. It keeps track of the test cases run so far so that those test cases are not used which are slow or are likely to fail.
  5. Mabl: This is also a self-helping AI testing tool. It is a leading tool built for CI/CD platforms. It speeds up tasks like execution and maintenance. It also performs tasks like the deduction of defects.
  6. Google OSS-Fuzz makes open-source software and tools more secure, stable, and reliable. It supports Python, C, C++. It is also a declarative AI-testing tool.

Also read: Wonders of AI in Software Testing

Those, as mentioned above, were a few powerful AI-testing tools. These tools come under the following four categories:

  • Self-healing tools
  • Declarative tools
  • Differentiable tool
  • Visual AI-based testing tool

AI is not just paving its way in technology but has also started its path in the testing field. AI for software testing is like adding a new revolution in the testing stream as it can be performed in an easy and time-efficient way. If there is one more fascinating question than anything, imagine where the expansion of technology would end, and we do not think anyone has an answer for that. 

Also read: Benefits of AI in Software Testing

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