Machine learning (ML), disrupting and improving many industries, is just beginning to enter software testing. Most software development teams believe they are not testing well. They understand that the impact of quality defects is significant and they invest heavily in quality assurance, but still don't get the results they want. This is not due to a lack of skill or effort - the technology that supports software testing is not effective.
The sector received insufficient service.
Until the software has been thoroughly and accurately tested, it cannot be a successful release, and testing can sometimes require significant resources, given the time and human effort required to get the job right. This space requirement is just beginning to be met.
Machine learning (ML), disrupting and improving many industries, is just beginning to enter software testing. Heads are turning, and for good reason: the industry will never be the same again. As machine learning continues to grow and evolve, the software industry is increasingly using it, and its influence is beginning to dramatically change the way software testing is done as technology evolves. Let's examine the current situation in software testing, examine how machine learning has evolved, and then examine how machine learning techniques are revolutionizing the software testing industry.
Some Background on Software Testing
Software testing is the process of examining whether software is performing as designed. Functional quality assurance (QA) testing, a form of testing that ensures that nothing fundamentally breaks down, is performed in three ways: unit, API, and end-to-end testing.
End-to-end (E2E) testing ensures that the entire application is put together and running in the wild. The E2E test tests how all code works together and how the
application performs as a single product. Testers will interact with the program as a consumer would do through core testing (where they test what's done over and
over) and edge testing (where they test unexpected interactions). These tests allow developers to make repairs by discovering when the application is not responding as the customer intended.
Traditional E2E testing can be manual or automated. Manual testing requires people to click the app each time it's tested. It is time consuming and error prone. Test automation involves writing scripts to replace humans, but these scripts tend to run inconsistently and require a huge waste of time as the app develops.The entire E2E test area is sufficiently dysfunctional not to be interrupted by AI / ML techniques.
So What is the Future of Software Testing?
The future of software testing is faster testing, faster results, and most importantly, testing learning what really matters to users. After all, all tests are
designed to make sure the user experience is great. If we can teach a machine what users care about, we can test it better than ever.
Traditionally, testing delays development in both speed and benefit. Test automation is often a weak point for engineering teams. Machine learning can help empower it.
For the future of software testing, machine learning means autonomy. Using data from current application usage and past testing experience, smart machines can create, maintain, execute and interpret tests without human input.
