Artificial Intelligence is growing all around us. But there is considerable fear around it, and the capacity of AI to replace human’s who earn a living today by solving problems.
There are a lot of developing AI in test automation that will make the jobs of software developers better and smarter with more efficient results. Certainly, in the area of software testing, AI test automation tools can be incredibly useful.
AI is everywhere--from smartphones, smart devices (Alexa, Siri, etc.), smart apps (Cortana, Google Assistant, etc.) to automobiles that can drive with no hands on the wheel. Indeed, AI is being deployed across all fields such as Healthcare, Banking, FinTech, Insurance, transportation, mining, and agriculture: AI-powered assistants’ populate retail websites; AI-supported fraud prevention, AI-generated viewing recommendations on Netflix and Amazon; autonomous vehicles. Even the rapid development of mRNA vaccines has cast a spotlight on the potential of AI in healthcare.
AI driven test automation is taking up an increasing amount of space in the testing field--mainly due to the need to increase automation coverage and speed up processes in software development and quality assurance.
In the context of automated software testing, applying AI is just a matter of statistics-based machine-learning (ML). ML is a pattern-recognition technology, using patterns identified by ML algorithms to predict future trends and problems. ML tools consume vast amounts of information and data and find predictive patterns that would be very difficult for the human brain to detect, and then reveal the differences in the patterns that require analysis and action.
Another benefit of AI-based test automation is keeping costs down. Instead of hiring large teams to monitor and maintain automated tests, a small number of specialists can easily set AI automation testing AI testing tools run cloud-based software, which is more cost-effective than on-premises software because of the absence of maintenance costs since the software owners run maintenance, not the users.
Until recently, Exploratory Testing could be categorized as a non-automatable action. But some organizations have begun training testing bots with self-learning capabilities to observe the quality assurance engineer exploring a web application manually and noticing defects. The bots slowly learn from their observations, crawl the application and find unusual patterns in future runs. By definition, Exploratory testing requires human intelligence. AI, though, will accelerate the process. V2Soft has QA experts, and their work is made better and more efficient by deploying our AI automation testing tools and AI testing framework, just like a carpenter who learned the trade with hand-tools is made better and more productive and efficient when given power tools.
One of the most popular AI automation practices today is using machine learning to automatically write tests for applications by “spidering.” There are AI testing apps that automatically “crawl” the application. As the AI-based automation tools are “crawling,” it collects data by taking screenshots, downloading the HTML of every page, measuring load times, etc. It repeatedly runs the steps. Patterns and defects emerge, and then can be fixed rapidly.
One of the use cases of AI in test-automation is self-healing test scripts, which fuel the effectiveness of quality assurance managers. Any change in the user-interface requires the revamping of multiple test-automation scripts. Whenever an AI powered test fails after an update, AI software testing tools have the capability to update the script automatically since it can better differentiate between a ‘change’ and a ‘bug’ than human-led manual testing processes can.
Codeless AI testing is significantly faster than either manual testing or familiar automated solutions, as testers save time generating code. This allows companies to increase their ability to run tests and deploy solutions more quickly. Codeless tests also run in parallel and across browsers and devices, making them easier to scale. No-code testing technology can therefore boost time to market, which is key in today’s competitive market.
So, if AI is so effective, then why not do all the automated testing with AI?
It cannot be denied that test automation has revolutionized software testing. In today’s world of widely distributed, continuously updating services, competent software testing would be impossible without automated testing.
But handing off all an organization’s automated testing to AI is a bad idea. Why? If you close off the opportunity of good smart people to think deeply about how to integrate automation into testing efforts, you will ensure ensures failure. That is unfair to all the people (including customers) who depend on the success of the enterprise software.
There is a fad happening in software management today and that is the cry from the C-suite “that we just need more automation! More automation!.”
But hold on. We discussed the many ways AI is being used in every day lives above. Consider that autonomous cars have been crashing. And consider that AI bots that pop up on consumer and retail websites often leave customers baffled and frustrated because they are unable to properly help a large swath of customers with issues and problems. AI is a tool to be managed by humans with a sense of judgement, experience and the capacity to know when it makes sense to break a rule, a protocol or a standing policy in order to properly solve a customer problem.
Yes, there are great applications for AI-automation, and things it can do that manual testing can’t, or at least not as quickly and repeatedly. But the opposite is also true. There are things only manual testing can do better than automation, which is why you want to have the right mix of both.
Where manual excels is simply the human factor that is critical to the process. The benefit of having a human patiently doing deep exploratory testing can’t be duplicated in automated testing.
And we are just referring to finding bugs. It’s not that difficult to find bugs. Customers discover bugs all the time. An experienced quality-assurance pro has value in that he or she has developed an intuition for how the software can break, and how the software might be used by customers in ways it was never designed for.
Another advantage of manual testing by QA is that they can immediately engage in determining the scope and severity of a bug, narrowing down test contexts (OS’s, workflows, etc) where it specifically manifests, and those where it does not. Automated tests can’t do this very well, or at all.
V2Soft believes manual testing is usually preferred for the initial testing of new software features and capabilities. Automated testing is clearly better for continuous general regression, and for load and performance testing.
V2Soft believes that an over emphasis on AI-assisted automated testing can lead some organizations to hire the wrong people for quality assurance.
How so? Many organizations hire scripting experts who are boosters of automation and AI to QA positions. But the truth is that knowledge and being expert at using the automation tools tells you very little about their understanding of QA, which requires a broader skill and experience set well beyond scripting and automated testing. They get hired because they’re scripting wizards, not because they are skilled at designing a solid, smart diagnostic test.
You’ve heard of artisanal cheese? How about artisanal software? It’s not for a software engineer to tell you that they can’t fix a bug or other problem because they didn’t write the code where it manifests. It’s terribly frustrating to be faced with this; it’s not as if code written by another engineer is in a different language. It’s as if you ask a plumber to fix a leak, and they tell you that they have to re-pipe the house because it is not plumbed the way they would have plumbed it.
This scenario is also common in test automation engineering; engineers telling us they don’t know how to update a test automation process for a major product upgrade because they did not write it. And the engineer who did write it left the company. Before hiring a squad of QA engineers and empowering them to crank out automated test scripts by the hundreds, be sure you have first defined, and trained on, general standards of intelligibility. Require that all test are mutually intelligible to any of your QA engineers, so that you are not left in the situation of having to replumb the all of the testing process and protocols just because one team member left.
At V2Soft, we believe in the power and utility of AI-assisted automated testing tools. But we have also found in our work and travels that those tools must be managed by knowledgeable and experienced managers. The tools are not ready now, nor will they ever likely be, adequate to run all by themselves.