Artificial Intelligence(AI) and Machine Learning(ML) are two hot buzzwords that have taken the tech world by storm. AI and ML are making great waves in every domain and the impact of this shake-up is wide and far-reaching.
When combined the technology has some exciting potential in the software domain. Already, BOT and AI-enabled test automation software is making great leaps in building a self-adaptive model of test automation. That is software learns to test itself, continuously improve based on past and real-time trends, predicting failures, tracking bugs and eliminating them.
Sounds far-fetched? QMetry clients have witnessed the power of such Intelligent Test Automation, that is software that is intelligent, data-driven, led by analytics, AI and ML.
But with every leap that technology makes, there is much speculation and some fear. What does this mean for the future of software development as we know it? Will Software Testing be redundant a couple of years from now?
To answer this, let’s look at the what, how and why of AI-enabled testing:
The Roots of Intelligent Test Automation
Ever since the advent of Agile practice and the adoption of DevOps, companies are looking to shorten their release cycles. And to provide better output both in terms of quality and quantity. Necessity, here is the invention of software test automation. Test automation allows businesses to automate laborious tedious testing tasks and execute them in minutes, increasing their coverage and allowing manual testers to focus on high-level testing.
But the sheer range, scope and complexity of testing software has intensified. There are layers of applications interacting through APIs, the use of legacy systems, IoT, CI/CD tools and other several project management software. As the 2016-2017 World Quality Report puts it: “We believe that the most important solution to overcome increasing QA and testing challenges will be the emerging introduction of machine-based intelligence. This will be the next big wave of change after the introduction of risk-based test strategies and test automation technologies.”
AI and ML-backed testing is the just the natural extension of automation. With rising volumes and complexity of test data, you need the BOTs to step in. Mature technology takes these gigabytes of information, performs automatic code reviews, perform risk assessment, identifies the biggest threats, categorizes errors, and predict failures with high accuracy.
Not only that, it will also suggest recommendations and improvements using analytics to improve all your subsequent test runs. Ultimately creating a continuously improving quality cycle. These systems thrive on data, the more information you feed, the better it delivers the feedback and outcomes.
Not The Death Knell For Manual Testing
And yet, there is no reason to fear. No apocalypse for conventional software testing. There is tremendous value in human input, right from exploratory testing to risk and security management, analyzing scalability and performance, process assurance and improvement.
Manual testing continues to have a critical role in the testing strategy, especially to see the big picture and to ensure quality user experience.
However, software development continues to evolve at a rapid pace. Culture, technology and processes need to shift shape to grow with the flow. The future of software testing certainly seems to favor self-learning test models powered by AI.
If there is one thing certain about the future of software testing, it is that there will be more reliance on automation and intelligent ways of testing. If anything, the demand for QA experts and QA tools will only rise.