Artificial Intelligence in Test Automation
Artificial Intelligence is going to play an increasingly important role in the future of test automation.
Evolution of Intelligence in Test Automation:-
How Intelligence has evolved over time in the field of test automation.
- Manual Testing.
Earlier there was only manual testing, like each and every step was written and executed manually, this was quite time consuming task and it leaves for a higher chance of error.
- Automation Testing.
To the rescue came automation testing, different paid, open source tool came into market. They help us to automate test cases and execute them and generate reports.
- Artificial Intelligence in Testing.
Now the third wave has struck in the world of software testing with advent of Artificial Intelligence.
So the artificial intelligence will make a fundamental difference in many fields including test automation. The technology has been applied in various applications such as web services, cloud services and mobile applications.
What is Artificial Intelligence:-
Artificial Intelligence refers to the capability of machines to perform functions that mimic a human mind, such as learning and problem-solving.
Any device or software that can perceive its surroundings and make the correct decisions to achieve its goal can be said to have some level of artificial intelligence.
For example, Google Maps uses AI to monitor traffic patterns and predict the best routes for you to take.
How AI Can Optimize Testing:-
- Accelerating Timelines.
Instead of having to go through thousands of lines of code, AI can quickly sort through log files, scan code in seconds and detect errors in much lesser time.
Additionally, AI does not get exhausted or make human errors, thus yielding more accurate results.
- Better Automation.
AI bots will be able to evolve with change in the code. They can adapt and learn to identify new functions.
When AI bots identify a modification to the code, they can be programmed to decide if it is a new feature or some defect emerging from changes to the code.
It is also possible to improve automated testing by utilizing a platform that is built for it.
- More clarity on client requirements.
AI can help study a large number of competing products to identify key selling points so that both developers and tester know what users want out of a particular kind of software.
Testers can use AI to detect common flaws in code that might hinder the flawless functioning of a software system.
By achieving a proper understanding of what the client wants, they can create test cases to ensure that the product does not break when it comes to achieving those particular goals.
Benefits of AI in Test Automation:-
- The amount of time spent on repeated jobs.
AI may be used to swiftly and automatically develop test scripts. AI tools may be taught based on past project inputs and results to automatically generate test scripts for comparable projects.
- Flaky Test.
Teams of testers spend hours assessing whether a failed test was caused by application bugs or poorly prepared test cases. These kinds of test failures are known as flaky tests.
AI can assist teams in overcoming the difficulty of flaky tests by developing more resilient test cases and detecting trends in random test failures to accelerate the process.
- Maintaining the test suites and test scripts.
Maintaining a large number of test scripts gets difficult as an application grows. AI tools may be used to maintain and extract the appropriate test scripts based on testing needs, allowing AI to be utilized to tackle this difficulty.
Why do we need AI in software testing:-
- Artificial Intelligence will be helpful to automate the processes in an efficient way instead of a human tester which unnecessarily inflates costs and effort.
- It would be a good practice if human intelligence as well as automation through AI to recognize the application issues by making exceptional and innovative test environments.
- Artificial Intelligence algorithms can be tremendously helpful in the testing industry in making a smarter and more productive software for the end-user.
- Having a smart algorithm can encourage the process, help testers to find the maximum number of bugs in less time and it will make the application more reliable and accurate.
Conclusion:-
Artificial intelligence in software testing is a reality and a requirement for coming industry demands. These next gen technologies can consume a large amount of information and make predictions, which can be used by test automation tools to build efficient and robust test frameworks.