Artificial intelligence has permeated virtually every industry. Its applications are revolutionizing the way businesses work and demonstrating the true power of machines. The software development world is no exception - AI has taken it over as well. Automatic code generation, software design, and requirement gathering are all benefitting from smart algorithms. But what about AI in testing services? Does it hold any potential at this stage? Let’s find out. The Current State of AI in Software Quality Assurance Within the realm of software testing, artificial intelligence provides a combo of cognitive automation, reasoning, machine learning, natural language processing, and analytics. Although AI hasn’t yet reached its maximum implementation in Software Testing, here are some areas where we’ve seen the breakthrough: AI-enabled testing — building AI tools to test software. Testing AI systems — creating approaches to test AI systems. Self-testing systems — designing software that has self-testing and self-healing functionality. We’ll focus on the first one in this article, which is AI-fueled testing practice since it’s gaining more momentum among all. What Are the Benefits of Intelligent Systems in Software Testing? Software testing is a procedure that is as important as software development itself. Quality assurance guarantees bug-free products and checks whether the final software product matches requirements. However, the traditional testing approaches have limitations linked with human capacity. Thus, developers are unable to use all possible data combinations for testing since it’s time-consuming. And here’s when artificial intelligence comes on stage. Automation is most applicable to complex applications with a large functional part. Thus, it is especially popular for testing hard-to-reach areas of an application, validation forms, basic operations, and frequently used functionality. Let’s have a closer look at the benefits of AI in software testing. Better accuracy Manual software testing is an error-prone environment where even the most established professionals can slip up. Therefore, automated testing takes over the repetitive grind of performing the same number of steps over and over again and putting down accurate results. 2. Cost efficiency Manual testing is expensive, both in terms of time, resources, and money. Therefore, frequent execution of the same set of test cases is better off with AI-enabled testing. Also, automated tests minimize developer involvement during testing, thus saving labor hours. However, the cost-efficiency of automated systems in testing is a double-edged sword, hence their affordability is relevant in a limited number of cases. 3. Breaking the limits of manual testing Automation testing also provides more coverage and better visibility into the software under test. It uncovers the new ways of testing that would be impossible for manual testing or produce thousands of events and sequences. These tests can even unearth bugs in code already 100% covered. 4. It goes beyond human capabilities (obviously) Around-the-clock testing is something unheard of for developers. With automated tests, you can start testing when you leave the office and come back in the morning to see the results. Also, companies don’t need to hire dozens of manual testers. Instead, they only need a test automation engineer to write scripts. 5. Better test coverage and product quality Since test automation allow you to run tests on thousands of mobile devices, you get higher-quality software. The latter will then be released earlier, with fewer problems and fewer resources involved. Automation Is No Silver Bullet You cannot count on automation to take all your testing pains away. Although it streamlines the testing process, it cannot address any specific issues that users may face. Also, AI-enabled testing is suitable only for a limited number of scenarios. The primary goal of automation is to detect problems in routine tasks like entering into an application, creating an account, or sending an email when a user forgets their password. Application bugs discovered in unusual conditions must still be manually tested. Machines, as we all know, have incredible powers, but they are unable to think (yet). Automation, among other things, cannot perform specific parts of usability testing of the design, such as button placement and overall usability. How To Use AI in Your Test Automation? Enough with limitations. Despite its nascent state, artificial intelligence is already successfully used in quality assurance. Automate visual validation Image-based testing is a quite popular application of automated tools. Visual UI testing helps developers verify that the UI meets the expectations of users. Most often,  it’s the testing of the right color, shape, position of an element. Looking for visual bugs is not something we do when running traditional automation tests, hence AI capabilities would help. Testim and Applitools are just some examples of software that leverages AI for UI testing. 2. Test APIs Traditional API testing poses unique challenges to testers. Whether it’s updating the API schema or validating parameters, testers spend a significant amount of time and skills on testing the interface. AI, on the other hand, can provide scriptless API test scenarios and the ability to shift testing efforts left by converting manual UI tests into automated API tests that scale. 3. Hand over test data generation to AI Another trending application of AI algorithms is the creation of test data. Machine learning can build data sets, including profile images and personal data, utilizing existing production datasets and ready ML models. This test data turns out close to production data, making it perfect for software testing. 4. Create and update unit tests The generation of unit tests is one of the most basic AI use cases. Static analysis of software can aid in determining which sections of the code unit tests do not cover. The tool may then use this data to produce unit tests for the uncovered code. Moreover, AI-generated unit tests are significantly easier to develop since they don't require any setup or interactions with other components. Artificial intelligence will be able to update unit tests whenever the source code changes, allowing it to maintain unit tests. The Bottom Line Most of the time, testers burn their time by executing manual tests or fixing automated ones. Delayed releases,  last-minute changes to requirements, and tight deadlines don’t make things easier. Artificial intelligence in software testing is focused on making the software development lifecycle easier. It helps uncover bugs earlier, hand over repetitive tests, and predict failure points. Although its potential is yet to be discovered, AI-enabled tools already help testers with test data generation, unit tests, and UI testing.

Routine

The 4 Pillars Of Automation

SDET vs Test Automation Engineer: Main Differences

Nominated for 2022 - HackerNoon Contributor of the Year - Quality Assurance

Nominated for 2022 - HackerNoon Contributor of the Year - Software Testing

Nominated for 2022 - HackerNoon Contributor of the Year - Testing

Too Long; Didn't Read

Get Free Unlimited IP Geo Data With IPinfo Lite

AI in Software Testing: Automation or Automagic?

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

11 Best Automation Testing Tools to Try in 2021

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

11 Best Automation Testing Tools to Try in 2021

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps