10 Benefits Of Using AI In Software Testing



Software testing has always been and will always be one of the major stages in the software development life cycle. This stage decides whether the product will move forward in the direction of release or it would have to go back to the development team for correction.

It is very important to detect the weaknesses in the software at this stage to prevent the reporting of the defect by the users as much as possible after the product hits the market.

In this era of frequent technological development, the use of AI in software testing is raising as a better option than depending on the manual way of software testing. Manual testing is very time consuming and requires a lot of effort. Both of them are very valuable for the enterprises to meet the current software demand and standout in the market.

Artificial Intelligence is the ability of the machines to recognise the entities around them and make the plans to achieve their goals, just like a human.

Machine learning means that computers can learn on their own without the need for any explicit programming.

Artificial Intelligence Testing

Agile development methodology involves continuous cycles of development and deployment as per the changing requirements of the stakeholders.

Artificial intelligence with Machine learning and Natural language processing will let the computer automatically create and implement the test cases, analyse the obtained results and make the improvement accordingly all on its own.

ai in software testing

Image Source: Testim.io

It helps in providing quality software at a fast pace by adding intelligence and efficiency to the testing. Along with that, it is easy to write, analyse and maintain test scripts for automation testing.

Tests can be written in the plain English language. The machine will read and analyse them to create and implement tests on the software, hence will reduce the test scripting time and the time duration of a sprint in agile development.

AI framework will automatically find the reasons for the failure of scripts and suggest viable solutions for fixing it or may fix it on its own. Bots and trackers enabled with AI can be built. These machines will learn from the way a user operates the software and creates the test cases accordingly.

Benefits of AI in Software Testing

Artificial Intelligence can improve and make the Quality Assurance process much easier.

1. With the use of AI and Machine Learning test automation tools, the testing process can be accelerated significantly.

Since in artificial intelligence testing, the tests are created and implemented on their own and do not require continuous attention of the team. The programmed machines are always quicker in performing a repetitive task than human.

2. As the testing process speeds up, so it also reduces the testing overheads. The use of Artificial Intelligence in software testing eases the framework and reduces the extra expenses on testing of the product.

3. Even the AI also widen the test coverage. In this system of testing, the machine is given the starting point and endpoint of testing and the rest of the intermediate tests are automatically covered by it.

Image Source: QATestLab Blog

4. AI and ML, test the software more effectively than manual way and helps in the deployment of error-free software in the market. 

5. Since the testing duration gets reduced, so the release frequency of the software by the enterprise also increases.

6. The use of Artificial Intelligence for testing also improves the implementation of exploratory testing.

Exploratory testing is one of the important testings in Agile development methodology. In this testing, the tester explores the software without any specific plan or expectation to find out the defects that a user may encounter while operating the software.

Earlier, it was done by the tester manually but now enterprises are relying on AI and ML for better and fast exploratory testing.

7. It also helps in implementing the tests which are more realistic and accurate. The automation testing provides detailed data about the test and also validates its effectiveness.

This will also eliminate the unnecessary tests and hence optimize the testing.

8. AI and ML are self-learning technologies, so the machine learns on its own about the kind of new test cases required to be created to meet the software’s current testing requirements.

It enhances the capabilities of automation testing. This automation reduces the efforts of the developers by analysing the changes that have been made to the software and ‘how the updated software should be tested?’.

9. This way of testing also provides a better analysis of the defects detected during the test. It gives detailed data about the defect like its cause of occurrence and impacts on the rest of the software’s functionality and the ways to fix it.

10. Regression testing is a very important test after a defect has been fixed to check that the correction has not led to new defects in another part of the software.

AI helps in efficiently undertaking this testing. It can go back in the history of software and can find out that which part of the software might get affected by the correction made rather than wasting time on testing those parts that have nothing to do with the changes made

Automation Tools Using Artificial Intelligence for Testing

Image Source: CIO Bulletin

1. Applitools

Applitools is a next-generation test automation platform that claims to use visual AI to deliver quality products at a fast rate with reduced cost.

It also undertakes the cross-browser testing on a different combination of platforms effectively to accelerate functionality and visual testing.

2. Mabl

Mabl claims to create reliable automated UI testing. It also provides AI-driven insights about the application and analysis of the test failures and it’s coverage to test the user experience.

It can create new tests based on data collected to cover all reachable paths.

3. Testim

Testim is another AI-based automation testing tool that provides end to end test facility, either in code or codeless way, along with the diagnose of the failed test, to figure out the cause and way of correcting it.

It allows writing the test cases in the same IDE, which is being used for developing.

AI and Machine learning test automation tools are in trend to provide a quality product at reduced cost and time.