The majority of software development teams think they don’t test well. They understand that the effect of quality defects is substantial and invest heavily in quality assurance, but they still don’t get the results they want. This is not due to a lack of talent or effort – the technology that supports software testing is simply not efficient. The industry has been underserved.

There can be no successful release until the software has been properly and thoroughly tested, and testing can sometimes be resource intensive given the time and human effort required to do the job properly. This gaping need is only just beginning to be filled.

Machine learning (ML), which has disrupted and improved so many industries, is just beginning to make its way into 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 impact is starting to dramatically change how software testing will be done as technology matures. improved.

Let’s dive into the current state of software testing, examine how machine learning developed, and then explore how ML techniques are radically changing the software testing industry.

Some information about software testing

Software testing involves examining whether the software works as it was designed. Functional Quality Assurance (QA) testing, the form of testing that ensures nothing is fundamentally broken, is performed in three ways: unit, API, and end-to-end testing.

Unit testing is all about making sure that a block of code gives the correct output on every input. API tests call interfaces between code modules to ensure that they can communicate. These tests are small, unobtrusive, and intended to ensure the functionality of highly deterministic pieces of code.

End-to-end (E2E) testing ensures that the entire application works when fully assembled and working in the wild. E2E tests test how all the code works together and how the application works as a single product. Testers will interact with the program as a consumer would through core tests (where they test what is done repeatedly) and edge tests (where they test unexpected interactions). These tests discover when the app isn’t responding the way a customer would expect, allowing developers to make fixes.

Conventional E2E testing can be manual or automated. Manual testing requires humans to click on the app each time it is tested. It is time consuming and error prone. Test automation involves writing scripts to replace humans, but these scripts tend to work inconsistently and require huge maintenance time as the application evolves. Both methods are expensive and rely heavily on human intuition to succeed. The entire E2E test space is dysfunctional enough to be ready to be disrupted by AI/ML techniques.

What is Machine Learning?

Although machine learning is often used as a synonym for AI, it is not strictly the same thing. Machine learning uses algorithms to make decisions, and it uses feedback from human input to update those algorithms.

A good example is machine vision. A machine vision app can identify something as a cat when it’s actually a dog. A human corrects it (saying “no, it’s a dog”) and the set of algorithms that decide if something is a cat or a dog are updated based on that feedback. Machine learning is designed to make better decisions over time based on this continuous feedback from testers and users.

Machine learning has struggled to reach the world of E2E testing due to lack of data and feedback. E2E testing is usually built from human intuition about what is important to test, or which features seem important or risky. New applications are using product analytics data to inform and improve test automation, opening the door to machine learning cycles to dramatically speed up test maintenance and construction.

So 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 that the user experience is wonderful. If we can teach a machine what users care about, we can test better than ever.

Conventionally, testing lags development in both speed and usefulness. Test automation is often a weak point for engineering teams. ML can help make it a strength.

What ML means for the future of software testing is autonomy. Smart machines will be able, using data from current application usage and past testing experience, to create, maintain, run and interpret tests without human intervention.

It is likely that not all aspects of software development need to be automated. Given a long tradition of E2E testing driven primarily by human intuition and labor, the industry as a whole may initially be resistant to entrusting the process to machines. In virtually every industry, insiders argue 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 playing out in the world of testing: ML-based test automation is in its infancy today, but it’s likely only a few years away from taking over the industry.

End-to-end standalone testing

The primary benefit of Machine Learning in E2E testing is being able to leverage highly complex product analytics data to identify and anticipate user needs. ML-driven testing is able to monitor every user interaction on a web application, understand the common (and peripheral) journeys that users take, and ensure that those use cases always work as expected.

If this machine tests many applications, it can learn from all these applications to anticipate the impact of new changes made to an application on the user experience. ML-based testing can already create better and more meaningful tests than humans with this data.

Tests developed by ML-driven automation are built and maintained faster and much cheaper than test automation built by humans. Such testing leads to much faster (and higher quality) deployments and is a boon to any VP Engineering’s budget.

What about testers?

What about the people currently doing this work?

Quality engineers still have a major role to play in software development. The most effective way to ensure software quality is to build quality control into the design and development of the code itself. Testing only exists because this process is imperfect.

While ML supports the E2E testing load of test engineers, these engineers can use their expertise in concert with software engineers to create high-quality code from scratch. From our own talks on the subject, it seems that most QA engineers would much prefer this to all-day test maintenance.

The future looks bright

ML offers a more streamlined and efficient software testing process. It establishes a process that is better equipped to handle the volume of developments and create the specialized tests needed. Intelligent software testing means data-driven testing, accurate results and innovative industrial development.

We hope this article has helped prepare you for the future of software testing and the amazing things machine learning has in store for our world.


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