Most software development teams believe they are not testing correctly. They understand that the effect of quality defects is great and they invest a lot in quality assurance, but they still don’t get the desired results. This is not due to lack of talent or dedication – software testing support technology is simply ineffective. The sector is underserved.
There can be no proper release until the software has been properly and thoroughly tested, and testing can sometimes require significant resources, given the amount of time and human effort required to do the job properly. This great need is only now beginning to be met.
Machine learning (ML), which has revolutionized and improved many industries, is beginning to adapt to software testing. Heads turn for one reason: the industry will never be the same again. As machine learning continues to grow and evolve, the software industry is using it more and more, and its impact is starting to change the way software testing is conducted as technology improves.
Some basic software testing
Software testing is the process of examining whether the software is working as expected. Functional Quality Assurance (QA) testing, the form of testing that ensures that nothing fundamentally breaks, is performed in three ways: unit, API, and end-to-end testing.
Unit testing is the process of ensuring that a block of code produces the correct output for each input. The API checks the calling interfaces between code modules to ensure they can communicate. These tests are small, discrete, and intended to guarantee the functionality of highly deterministic code.
What is machine learning?
While machine learning is often used synonymously with artificial intelligence, it’s not quite the same. Machine learning uses algorithms to make decisions and uses human input feedback to update algorithms.
A good example is computer vision. A computer vision application can identify something like a cat if it really is a dog. We fix ourselves (say “no, it’s a dog”) and create algorithms that decide if something is a cat or a dog based on the feedback. Machine learning is designed to make better decisions over time based on this ongoing feedback from testers and users.
Machine learning has struggled to catch up with the world of E2E testing due to a lack of data and feedback. E2E testing is often based on human intuition about what is important to test or which features seem important or risky. New applications use product analytics to inform and improve test automation and open the door to machine learning cycles to significantly accelerate test maintenance and build.
What is the future of software testing?
The future of software testing is faster testing, faster results, and most importantly, testing that learns what really matters to users. Ultimately, all tests are designed to ensure the user experience is optimal. If we can learn a machine that interests users, we can test it better than ever.
Testing conventionally slows down development, both in terms of speed and usability. Test automation is often a good place for technical teams. Machine learning can help you build strength.
What ML represents in the future of software testing is autonomy. Smart machines can configure, maintain, run and interpret tests without human intervention using usage data from current applications and past testing experiences.
Not all aspects of software development are likely to be automated. Given a long tradition of E2E testing, driven primarily by intuition and human labor, the industry as a whole may initially resist transferring the process to machines. In almost every industry, experts say that machines can never do their job. Those who resisted the rise of ML and doubled human labor are often left behind.
Autonomous End-to-End Tests
The main advantage of machine learning in E2E testing is the ability to use highly complex analytical product data to identify and meet user needs. ML-based testing can observe every user interaction in a web application, understand the common (and peripheral) paths that users take, and ensure that these use cases always work as intended.
If this machine tests many apps, it can learn from all apps to predict how new changes to an app will affect the user experience. With this data, machine learning-based tests can provide better and more meaningful tests than humans.
What about testers?
What about the people who are doing this work right now?
Quality engineers still play an important role in software development. The most effective way to ensure software quality is to incorporate quality control into code design and development. The test only exists because this process is not perfect.
The future looks bright
ML offers a simpler and more effective software testing process. It presents a better-equipped process for managing volume builds and creating the necessary specialized tests. Intelligent software testing means data-driven testing, accurate results, and state-of-the-art development.
We hope this article has helped you prepare for the future of software testing and the great things machine learning has to offer in our world.