In case you haven’t noticed, we’re in the midst of an artificial intelligence (AI) revolution — but not the scary Hollywood-movie kind, of course. Rather, AI is emerging with disruptive technologies like self-driving cars and mobile assistants, quickly leaving the realm of science fiction and entering into our everyday lives. Such disruptions already extend to the software development sphere and, as developers continue implementing and innovating with AI and lightning-fast software, the time will surely come for testers and developers alike to adapt.
When we think of software testing, we tend to picture a rigorous and often mind-numbing process hard on quality assurance (QA) professionals’ fingertips — and even harder on developers’ wallets. According to a report, researchers estimate developers still perform 90% of testing manually at a price tag of $70 billion, requiring two billion human-hours. And many test automation tools implemented throughout the last decade rely on virtually the same outdated workflows as manual testing without any substantial gains.
Software testing is known to take up significant time and resources. And successful advances with AI and machine learning in other industries make automation and utilization in the testing domain a no-brainer.
AI-driven testing (AIDT) development and implementation allows users to leverage machine learning and smart algorithms to rapidly generate and run thousands of test scripts to report functional, performance, and security-related results. Since early 2017, leaders of several large companies have incorporated AIDT into their testing workflows. They’ve made vast improvements compared to the work traditional QA engineers produce.
Some notable strides include the following:
The goal for those developing — and using — AIDT is to reduce false-positive rates, improve efficiency, lower costs, and increase productivity. By outsourcing testing efforts to AI software, QA professionals and developers alike have more time to focus on analyzing and improving product quality. The financial savings are equally far-reaching. According to the eBook, IBM report researchers estimated bugs QA professionals discover can cost $1,500, which can rise to over $10,000 or more plus damage to company reputations if end-users discover them, compared to only $100 for bugs found early in development. AIDT productivity gains could result in millions or even billions in savings, depending on company size.
Previous automated testing generations laid the groundwork for today’s AIDT systems; however, their script-by-script workflows have gone mostly unchanged over decades. This means QA engineers still create and debug scripts at slow rates relative to applications’ increasing complexities.
Combine that with human error and you have workflows that simply cannot compete with AIDT in terms of accuracy and speed. Those who continue to incorporate AI into their products, services, and systems will need to change their operations to match.
Consider the current user analytics approach. QA testers traditionally mimic user behavior based more on business analysts’ assumptions than on real user data. A thorough user behavior analysis would require immense humanpower and countless permutations — making AIDT even more attractive. AI-driven software utilizes different metrics than traditional software QA engineers might use. Deep neural networks in many systems allow software to assess vast quantities of real user data and identify potential errors based on the low-level features their algorithms detect. In other words, when it comes to representing real users, AIDT software can better predict as well as outwit and outperform existing systems with impressive accuracy by using self-learning algorithms.
AI-driven software testing may be new to many, but its benefits are already noteworthy. It’s proven to attain high test coverage with ease while simultaneously driving agile development operations. Its efficiency and accuracy — thanks in large part to its machine learning implementation — make it a cost- and time-saving measure that disrupts dated operations.