5 ways how AI can improve your business's software testing
Technology is evolving much faster than ever, and with the rise of Artificial Intelligence, Machine Learning and the Internet of Things, one can only expect it to progress even faster. AI, in particular, seems to be the hot buzz for quite some time now, as it seems to revolutionize industry after industry. When it comes to software testing, AI is improving and simplifying the life of developers and testers everywhere, by automating processes and allowing testers to provide more accurate results and bug finds. But how exactly is AI changing the software testing industry for the better?
Human error is not uncommon, even to the best of testers, especially when performing repetitive and dull tasks. When you have to do the same thing all over again, while it becomes an easy job, it leaves you more prone to mistakes, as you start paying less attention to it. This is where automation comes to help. By automating monotonous tasks, you can ensure more accurate results each and every time, avoiding the possibility to overlook important documentation or errors. This way, testers have more time to focus on more important things, such as improving automated tests and analyzing more complex situations. “Testers are already using some automation tools, but they do have limitations. This is where AI can help them improve. Automation tools can’t decide what tests to run, so they simply run all of them or some predefined sets. With the integration of AI, a bot can preview tests, acknowledge code changes and run only the necessary ones, delivering much better results.” says Mark Russel, QA engineer at Trust My Paper, a paper writing service.
Performing simulated tests for applications that have over 1000 users is close to impossible even for the best testing departments. With AI integration, tests can be simulated to include thousands of virtual users to see how the application holds up, no matter the type of application. The same thing goes for test coverage. Now, manual tests, although quite automated, can’t cover as much ground as AI integrated software would.
AI can go as deep as performing tests on memory and file contents, data tables and even internal program states, all in one test, to really give a better idea on how the software is behaving in certain situations. With every test run, AI integration can help perform over 1000 test cases in one run, something that is nowhere near possible for manual testing.
Because of changes in the source code or other features, tests have to be performed multiple times, manually, which can turn out to burn quite a big hole in the pockets of software development companies. With automated tests, once they are up and running, they can perform those tests every time it is necessary, without additional costs. Doris Hamill, software tester at Pick the Writer, explains: “When an application is changed or updated, testers have to update the code to fit the new product, which means higher costs. When AI is integrated into testing, everything changes. AI can analyze code changes and adapt the test to fit those changes exactly, without the need for it to be manually revised. This cuts down on costs and labor, allowing the company to redistribute capital towards more important business processes.”
More reliable bug finding
Sometimes even if the QA engineer does everything right, some bugs can go by unnoticed. This leads to them having to find out why and how that happened, which takes time and effort. AI can deliver answers to those questions in a matter of minutes, providing testers with information about issues in the testing processes or in the application itself. Afterward, testers can decide whether or not it is a persistent problem and they need to make adjustments to the testing software or if it is simply a question of poor approach. AI can be programmed to conduct those types of tests continuously and offer solutions for better bug finding.
With AI, you will never have a situation where a bug gets overlooked. Once it’s up and running, the software will spot and report every existing bug, even if the tester overlooked it at first. In return, this leads to much better applications, improving company reputation.
Coding test lines is extremely time-consuming and nerve-wracking for testers. Overall, for an application to pass a test may take days, or even weeks and when the deadline is getting closer and closer, manual testing simply becomes overpowered. AI can help write scripts, as well as analyze large amounts of data much faster, not to mention deliver much more accurate results, which lets the QA team know exactly what needs to be done.
This way, the final product is wrapped up much faster and ready to hit the market. Delivering a finished, fully-functional application to clients can take months, and even if they want to, development companies can’t speed up the process too much with manual testing. When AI comes into question, the products are not only delivered much faster, but their quality is ten times higher. Everyone knows that delivering high-quality products is the best marketing strategy.
Improved team communication
Not once have companies reported that the relationship between the development and testing department is not quite a friendly one. When testers deliver more accurate and fast results, the communication between them and the development team becomes much more efficient.
Testers can send out results much faster and more accurate, allowing the development team to tackle the issues. If a QA engineer overlooks bugs and issues, be it due to human error or other reasons, the development team can’t make the necessary adjustment to have the application up and running at its full capacity. If this happens and the product gets released full of bugs, the blame will be passed from one to another without coming to an end result.
The purpose of integrating AI into software testing is to make the process more efficient, increase the quality of end-products and cut down on costs. This is precisely why the integration of AI is much more than a computer taking on repetitive tasks, but rather improve them and perform more complex tests, to generate more accurate results and allow testers to focus on more human-centered processes.
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