Top 9 Benefits of integrating AI in Software Testing

The popularity of AI has created high hopes, and of course, QA and software tests too have not remained exempt from the charms of Artificial Intelligence. AI brings new and creative Intelligence to everything it touches by using the vast ocean of data at hand. Tech giants like Google, FB, Amazon, Microsoft, and the like spent huge money on their AI initiatives. Influential voices also started speaking about the paradigm alteration that this technology would bring to software development. From independent tests to continuous tests, Artificial Intelligence has transformed the software testing and development industry. AI is the key to streamlining software development and testing and making it smoother and more efficient.

Statistic Scenario

• Gartner report says, by 2020, Artificial Intelligence technologies will be universal in almost every new service and product and will also be the investment’s prime concern for CIO’s. Even 2018 were all about AI.
• Some 21 per cent of IT leaders surveyed stated they are putting Artificial Intelligence trials or proofs of the model in place, as per the 2020–21 World Quality Report. Speaking to extended-term trends, only two per cent of respondents said Artificial Intelligence has no role in their upcoming plans.
• Software testing and QA remain significant cost for software firms (it took more than a quarter (26 per cent) of IT expenses the previous year) — so there is an enormous incentive to spend on automated testing platforms that are AI-powered.

What does Artificial Intelligence (AI) mean for Software Testers?

It is a true statement that Artificial Intelligence shows enormous potential to detect test bugs or glitches rapidly with zero human intervention. Just as automation reduces monotonous manual work for software engineers, Artificial Intelligence also aims to minimize tedious work with extra intelligence. It seeks to find solutions to issues in the future by learning the answers to the same issues faced in the past. Software engineers have to perform what they are doing continuously. Yet, they also know AI-based automated testing tools and use them to improve their efficiency.

With Artificial Intelligence, QA testers will transform from being a software tests team to an automated tests team because Artificial Intelligence will make QA tests extra efficient. With around 70 % of testing being recurring, AI can rapidly occupy the space, and several things will change in the tests field. Hence, it can considerably increase the overall quality quotient of an item to be launched in significantly little time if software testers efficiently use AI tools.

IBM’s Systems Sciences Institute stated that the price to fix a bug observed after product release was 4–5 times more costly than one uncovered at the time of designing — and 100 times more than an error detected in the maintenance stage.

How costly can bugs turn out to be if they are not identified earlier?

• USD 10,000 — Production

• USD 1,500 — QA testing phase

• USD 100 — Gathering Requirements phase

Games developer Ubisoft has launched an AI-based tool that can alert software developers to possible bugs when they type code. Bug fixes can take up 70 per cent of the development budget of Ubisoft for a game. Thus, Artificial Intelligence stands to proffer remarkable financial rewards for their business.

9 Advantages of using Artificial Intelligence in QA and Software Testing

1) High level of Accuracy

Manual tests are accurate but not error-free. Sometimes, the possibility of errors or bugs remains unnoticed by QA engineers. Test automation helps in performing the same series of actions without missing the details. The QA engineers use automated software to complete the recurring test.

2) Better Defect Tracking

In old tests methods, bugs or errors remain ignored for a long. These unnoticed bugs become a nuisance later on. Artificial Intelligence can catch flaws in seconds. Artificial Intelligence analyses these bugs. As test data grows, so do the no. of errors or bugs. Artificial Intelligence automates procedures, so codes are corrected automatically, and software test teams would smartly perform bug tracking. Artificial Intelligence takes fingerprints of failures on debugging logs and detects duplicate errors.

3) Better Flexibility

Even the most accessible modifications in an app can lead to testing failures in automated testing tools as traditional tests scenarios consider a sole path or selector. Thus, such kind of test approaches is someway rigid. ML and Artificial Intelligence allow for a highly flexible tests procedure, knowledge relationships between several documentation elements. Such systems can adapt automatically to any alterations in real-time, being both reliable and flexible.

4) Controls Tests Time

Artificial Intelligence in QA testing minimizes the time spent on manual tests. QA teams can easily apply their efforts to highly complicated jobs that require human interpretation. QA staff and Developers would require using small attempt to design, prioritize, write, and manage E2E tests. Hence, it will accelerate timelines for release and free up resources to develop new products rather than tests a fresh release.

5) Enhanced Regression Tests

With progressively more rapid deployment, there is an accelerated requirement for regression tests, to the point where humans can’t convincingly keep up. Organizations can use Artificial Intelligence for few tedious regression tests tasks and use Machine Learning to create test scripts. For instance, in case of a User Interface change, one can use Artificial Intelligence /Machine Learning to scan for size, shape, color, or overlap. These would otherwise be manual testing; we can also utilize Artificial Intelligence to validate the alterations that a software tester may miss.

6) Consistency in Testing

While QA testers are good at detecting and addressing complicated issues and proving testing scenarios, they are still human. Bugs can occur in tests, particularly from burnout syndrome of finishing monotonous processing. Artificial Intelligence isn’t affected by duplicate tests and hence yields more reliable and accurate outcomes. Often, grudges happen between QA analysts and developers, chiefly under time constraints or the results found during tests. AI/ ML can remove such human interactions that may lead to holdups in the tests procedure by giving objective results.

7) Enhances Test Coverage

The well-detailed nature of test automation enhances the software tests procedure. Artificial Intelligence helps QA engineers to check memory, the internal program states, files storage and content, and data tables. AI finds patterns and examines the database behaviour as per expected results. AI in software testing can carry out various test cases in single testing, providing a comprehensive coverage set.

8) Release software in small timelines-Faster Time to Market

Automated tests assist in reducing the development and tests timelines. In test automation, a test gets implemented after each source code alteration. It decreases any extra cost involved to run recurring test cases. Artificial Intelligence integrated software test proves to be accurate and time-saving. In the QA and software test industry, time is straight proportional to money.

9) Executing Visual Tests

Image & pattern recognition enables Artificial Intelligence technology to identify visual bugs by executing apps’ visual tests. Artificial Intelligence can separate dynamic User Interface controls despite their shape and size, assessing them at the pixel level.

Top 5 AI-based Automated Tests Tool

#1 Functionize

It is a cloud-based tool which is beneficial for function, performance, and load tests purpose. This test tool is a one-stop solution to every problem and uses AI and ML to speed up test creation, analysis & management. The good thing is that you need to type what you wish in English, and natural language processing will generate the functional test cases automatically. Surprisingly, it implements 100s of tests in few minutes from every desktop as well as mobile browsers.

#2 Testim

Testim tool uses ML technology to write, execute, and direct automation testing. It highlights user interface, functional tests and E2E (end-to-end) tests. Testim will become clever with more runs and accelerates the test suits constancy. QA testers can use programming languages such as JavaScript and HTML to write complicated logic.

#3 Appvance

It is the best tool that uses Artificial Intelligence technology to generate test cases depending on consumer behavior. The testing portfolio explains exactly what real systems will perform on the production systems. Thus, this testing tool makes it 100 per cent client-centric.

#4 Testcraft

It is one of the perfect automated test platforms for regression and continuous tests on selenium. Testcraft can also be used to scrutinize website apps. Artificial Intelligence plays a vital role in avoiding time and management cost by automatically defeating the app’s moderations.

#5 Applitools

It is one of the best tools for visual User Interface tests, software monitoring, and visual management. This test tool scans app screens and examines them just like the humans’ brain; however, with ML. DevOps, automated testing, manual QA, and digital transformation specialist’s teams can easily use AppliTools.

Conclusion

Artificial Intelligence is the next big thing; however, it would not replace humans. Artificial Intelligence will execute various tests rapidly. But humans will still manage the result of the testing, as Artificial Intelligence can only perform definitive tests, whereas latent and implicit testing has to be executed by Human testers. QA testers will observe the more exciting parts of QA tests by working in harmony with Artificial Intelligence. The QA engineers working hand-in-hand with Artificial Intelligence and can revolutionize the way we test now.

SMEs could easily benefit from software testing using Artificial Intelligence/ Machine Learning to meet the tests team’s crucial challenges. While AI and ML are not substitutes for human testers, they could be an add-on to the testing tactic.