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Thursday, January 21, 2021

How Machine Learning Will Impact the Future of Software Development and Testing

Machine learning (ML) and artificial intelligence (AI) are frequently imagined to be the gateways to a futuristic world in which robots interact with us like people and computers can become smarter than humans in every way. But of course, machine learning is being employed in millions of applications around the world—and it’s already needs to shape how exactly we live and work, frequently in ways that go unseen. And while these technologies have been likened to destructive bots or blamed for artificial panic-induction, they are helping in vast ways from software to biotech.

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Some of the “sexier” applications of machine learning are in emerging technologies like self-driving cars; thanks to ML, automated driving software will not only self-improve through millions of simulations, additionally, it may adapt on the fly if up against new circumstances while driving. But ML is potentially more essential in fields like pc software testing, which are universally employed and used for millions of other technologies.

So how exactly does machine learning affect the world of software development and testing, and what does the future of these interactions look like?

A Briefer on Machine Learning and Artificial Intelligence

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First, let’s explain the difference between ML and AI, since these technologies are related, but frequently confused with one another. Machine learning refers to something of algorithms that are designed to help a computer improve automatically through the course of experience. In other words, through machine learning, a function (like facial recognition, or driving, or speech-to-text) could possibly get better and better through ongoing testing and refinement; to the outside observer, the system looks like it’s learning.

AI is recognized as an intelligence demonstrated by way of a machine, and it frequently uses ML as its foundation. It’s possible to truly have a ML system without demonstrating AI, but it’s hard to have AI without ML.

The Importance of Software Testing

Now, let’s have a look at software testing—a crucial element of the software development process, and arguably, the most important. Software testing was created to make sure the product is functioning as intended, and in most cases, it’s a process that plays out many times over the course of development, before the product is really finished.

Through pc software testing, you are able to proactively identify bugs and other flaws before they become a real problem, and correct them. You also can evaluate a product’s capacity, using tests to evaluate its speed and performance under a variety of different situations. Ultimately, this results in a better, more reliable product—and lower maintenance costs over the product’s life time.

Attempting to deliver a software product without complete testing will be akin to creating a large structure devoid of a true foundation. In fact, it is estimated that the cost of post pc software delivery can 4-5x the overall cost of the project it self when proper testing will not be fully implemented. When it comes to pc software development, failing continually to test is failing to plan.

How Machine Learning Is Reshaping Software Testing

Here, we can combine the two. How is machine learning reshaping the world of software development and testing for the better?

The simple answer is that ML is already getting used by pc software testers to automate and improve the testing process. It’s on average used in combination with the agile methodology, which puts an focus on continuous delivery and incremental, iterative development—rather than building an entire product all at once. It’s one of the reasons, I have argued that the future of agile and scrum methodologies involve a good deal of machine learning and artificial intelligence.

Machine learning can improve pc software testing in lots of ways:

  • Faster and less effortful testing. Old-school testing methods relied almost exclusively on human intervention and manual effort; a group of software engineers and QA testers would run the software by hand and scout for any errors. But with ML technology, you can automate testing, conducting tests far faster, and without the need to spend hours of human time.
  • Continuous testing. Additionally, QA testers are only readily available for a portion of the time, and if you’re developing software continuously, this is untenable. A refined ML-based testing system can deploy continuous testing, constantly checking how your product performs under different conditions.
  • Consistent testing. If you conducted a test for the same product twice, have you been confident in your capability to conduct the test exactly the in an identical way, both times? Probably perhaps not; humans are notoriously inconsistent. But ML algorithms are designed and executed to repeat the same processes over and over, reliably; you’ll never have to worry about consistency with a ML-based testing script.
  • Higher detection acuity. Modern ML-based validation tools are capable of picking up on UI discrepancies or anomalies that human eyes might not be able to discern. Is this UI element the right color? Is it in the right position? Visual bugs are occasionally easy to notice, but a refined ML-based “eye” can provide you a lot more accuracy.
  • Multi-layer testing. ML testing also allows for multi-layer testing, without the dependence on a user interface. The right ML pc software testing system can be placed on application logs, including source code and production monitoring system logs.

While cognitive computing holds the promise of further automating a mundane, but hugely essential process, difficulties remain. We are nowhere near the level of process automation acuity necessary for full-blown automation. Even in today’s most useful software testing environments, machine learning helps with batch processing bundled code-sets, allowing for testing and resolving issues with large data without the need certainly to decouple, except in times when errors occur. And, even if errors do occur, the structured ML will alert the user who can mark the issue for future machine or human amendments and carry on its automated testing processes.

Already, ML-based pc software testing is improving consistency, reducing errors, saving time, and all the while, lowering costs. As it becomes heightened, it’s likely to reshape the field of software testing in new and a lot more innovative ways. But, the critical piece there is “going to.” While we have been not yet there, we expect the next decade will continue steadily to improve how software developers iterate toward a finished process in record time. It’s only 1 reason the future of software development will not be not exactly as custom as it used to be.

Nate Nead

Nate Nead is the CEO of SEO.co/; a full-service SEO company and DEV.co/; a custom web and pc software development business. For over a decade Nate had provided strategic assistance with technology and marketing solutions for some of the most well-known on the web brands. He and his team advise Fortune 500 and SMB clients on software, development and internet marketing. Nate and his team are located in Seattle, Washington and West Palm Beach, Florida.

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