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 Machine Learning Is Shaping the Future of Software Testing

Machine learning (ML), which has disrupted and boosted so many industries and sectors, is just beginning to pave its way into software testing. The heads are turning and, for a good reason: the industry will never be the same again. While machine learning is constantly growing and evolving, the software industry is using it more and more. Its impact is beginning to significantly change the way software tests will be performed as technology advances.

Let's discuss the current state of software testing, look at how machine learning has evolved, and then explore how its techniques are radically impacting the software testing industry.

Some Background on Software Testing

Software testing involves the procedure of determining whether the software operates the way it was intended. Functional quality assurance (QA), this type of test ensures nothing is structurally broken, it is implemented in three ways: unit testing, API, and end-to-end.

Unit testing is the process of ensuring that a code block provides the correct output for each input. API test calls the interfaces among code components to ensure they can communicate. These tests are small, unobtrusive, and designed to ensure the performance of highly deterministic bits of code.

End-to-end testing ensures that the whole application works when put together and works in the wild. E2E tests determine how the code works together and how the application works as a single product. Testers will communicate with the program as a consumer would through fundamental tests and edge tests. These tests detect when the application is not responding as a customer wants, allowing developers to make adjustments.

Conventional E2E tests can be manual or automatic. Manual tests require people to click on the application each time it is tested. It is time-consuming and prone to errors. Test automation entails writing scripts to substitute people, but these scripts tend to work inconsistently and require a huge amount of maintenance as the program evolves. Both methods rely heavily on human instincts to succeed and are expensive. The entire E2E test space is dysfunctional enough to prepare for interruptions by AI/ML methods.

Autonomous End-to-End Testing

The key advantage of Machine Learning in E2E testing is that it can use highly complex product analytics to identify and predict users’ needs. ML-based testing can track every user interaction on a web application, understand the typical and marginal journeys that users go through, and ensure that these scenarios always work as expected.

The tests created by ML-based automation are designed and maintained quicker and much less expensive than human-made test automation. Such testing results in much higher quality and faster implementations and is an advantage for any VP of Engineering budget.

What about the people doing these jobs currently?

Quality engineers still have a significant role in software development. The most effective way to ensure the software's quality is to incorporate quality control into creating and designing the code itself. Tests exist only because this process is not perfect.

Because ML takes on the task of testing E2E from test engineers, those engineers can use their expertise in collaboration with software engineers to develop high-quality code from scratch. 

The Future of Software Testing

Finally, all tests are designed to ensure that the user experience is amazing. If we can learn a machine about what users want, we can test it better than ever.

Conventionally, testing delays development, both in terms of utility and speed. Test automation is often a weak point for engineering teams. ML can help turn it into a strong point.

The Future Seems Promising

ML offers a more efficient software testing process. It creates a better-equipped process to manage the volume of developments and create the necessary specialized tests. Intelligent software testing means data-based testing, precise results, and innovative development.

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