The majority of software development teams believe they don’t test well. They understand that the effect of quality defects is substantial, and they invest heavily in quality assurance, but they still aren’t getting the results they want. This is not due to a lack of talent or effort — the technology supporting software testing is simply not effective. The industry has been underserved.
There can’t be a successful release until the software has been properly and thoroughly tested, and testing can sometimes take significant resources considering the amount of time and human effort required to get the job done right. This gaping need is just beginning to be filled.
Machine learning (ML), which has disrupted and improved so many industries, is just starting to make its way into software testing. Heads are turning, and for good reason: the industry is never going to be the same again. While machine learning is still growing and evolving, the software industry is employing it more and more, and its impact is starting to significantly change the way software testing will be done as the technology improves.
Let’s delve into the current state of affairs in software testing, review how machine learning has developed, and then explore how ML techniques are radically changing the software testing industry.
Software testing is the process of examining whether the software performs the way it was designed to. Functional quality assurance (QA) testing, the form of testing that ensures nothing is fundamentally broken, is executed in three ways: unit, API, and end-to-end testing.
Unit testing is the process of making sure a block of code gives the correct output to each input. API tests call interfaces between code modules to make sure they can communicate. These tests are small, discrete, and meant to ensure the functionality of highly deterministic pieces of code.
Conventional E2E testing can be manual or automated. Manual testing requires humans to click through the application every time it’s tested. It’s time-consuming and error-prone. Test automation involves writing scripts to replace the humans, but these scripts tend to function inconsistently and require a huge time sink of maintenance as the application evolves. Both methods are expensive and rely heavily on human intuition to succeed. The entire E2E testing space is sufficiently dysfunctional that it is ripe for disruption by AI/ML techniques.
The future of software testing is faster tests, faster results, and most importantly, tests that learn what matters to users. Ultimately, all testing is designed to make sure the user experience is wonderful. If we can teach a machine what users care about, we can test better than ever before.
Conventionally, testing lags development, both in speed and utility. Test automation is often a weak spot for engineering teams. ML can help to make it a strength.
What ML means for the future of software testing is autonomy. Smart machines will be able to, using data from current application usage and past testing experience, build, maintain, execute, and interpret tests without human input.
Not all aspects of software development should likely be automated. Given a long tradition of E2E testing being driven primarily by human intuition and manpower, the industry as a whole may initially resist handing the process over to machines. Across practically every industry, insiders contend that machines could never do a human’s job. Those who have resisted the rise of ML and doubled down on human labor often find themselves left behind.
A familiar story is unfolding in the world of testing: ML-driven test automation is in its infancy today, but it is likely only a few years away from taking over the industry.