Learn how machine learning and artificial intelligence are expected to augment established test automation practices.
As product dealines are shrinking and software is growing only more and more sophisticated, companies are forced to introduce viable innovations into their SDLC to keep in step with market requirements and competitors. Quality assurance is a process rather notorious for being high-load, repetitive, and time-consuming but, at the same time, hugely important for the application’s overall success. Obviously, this is a primary candidate for streamlining with automation.
Today, QA automation is accorded high priority by software development teams. According to the recent ResearchAndMarket report, the global market for automated testing services and solutions is forecasted to grow from $12.6 billion in 2019 to $28.8 billion by 2024, with functional QA being the segment with the highest investment over the projected period. The increased demand has also set the stage for unprecedented quality improvements in test automation, as companies are turning to the emerging technologies to revamp their practices.
Among all the budding software testing innovations, machine learning (ML), a subset of AI, appears to be the most promising. The technology can train itself on historical data to create and maintain test cases, offering to go beyond the conventional rule-based automation that partially relies on the testing engineer’s manual efforts. Let’s explore what’s on the horizon for ML in software testing, touching upon the technology’s proven use cases and its transformative effect on the QA and software delivery.
An algorithm might be unable to turn a canvas into a masterpiece, but it is already advanced enough to detect a masterpiece’s flaws — all due to computer vision. The technology that trains computers to properly understand and interpret visual objects can be successfully retrofitted to detect bugs, inconsistencies, and excessiveness in a user interface design.
To produce results, an ML testing algorithm first needs to learn from large sets of interface imagery where page elements are labeled, and good practices are distinguished from bad ones. As the system matures, it will be able to independently seek out faults in the software. To allow it to run end-to-end evaluation without constant supervision, such a computer vision-based solution is commonly equipped with a script that helps it move from page to page.
Although only nascent, computer vision QA automation is currently the focus of intense research, and standalone ML-powered solutions (Applitools is a prime example) already start appearing on the market.
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Another key area of QA automation is functional testing. It is unsophisticated yet high-volume and repetitive, that’s why it’s rather taxing when performed manually. The commonly used coding automation substantially accelerates test execution but, on the other hand, requires continuous maintenance, such as for debugging and rewriting test cases that fail, tailoring them to altered conditions. Meanwhile, machine learning has all the prerequisites to resolve this limitation and fine-tune functional testing even further.
Predictive maintenance is an ML-enabled technique that recognizes failure patterns based on historical data. When implemented in an automated functional testing workflow, it will run an ongoing evaluation of the environment and identify changes that may destabilize test execution before it happens. Apart from this, artificial intelligence can power self-healing mechanisms by detecting the root issue of a failed test and promptly fixing it. This way, the recovery takes much less time than when done by a testing engineer, allowing for almost uninterrupted testing.
Performance testing is an essential step in preparing for software release, as it helps ensure unfaltering operation at all times. Since it would require much effort to create an appropriate environment manually, partial automation has become a viable option for this testing type. A wealth of dedicated tools on the market allow generating real-life and extreme conditions and measure application performance. But machine learning can change the game here.
For one thing, it can prove extremely useful for test case creation. Geared towards pattern recognition, the algorithm runs an end-to-end system analysis, identifies weak points, and models test cases to address these deficiencies. Moreover, machine learning can take over the task of creating test scripts. Driven by Natural Language Processing, the algorithm can not only generate code but also fix any occurring issues, thus allowing software creators to fully focus on more creative tasks.
Today, more and more performance testing and monitoring tools are being retrofitted with AI/ML technologies, with such well-reputable solutions as AIMS, BMC Software APM, and Dynatrace among them.
With the rapid growth of smartphone usage over the last decade, the mobile application market is forecasted to have a staggering CAGR of 19.2% in the next three years, as reported by Allied Market Research. In the context of such a high demand, development companies began relying on up-and-coming solutions to accelerate their SLDC, and machine learning became the top choice for many.
In an average mobile app QA project, planning and creating individual test scenarios take up a lot of time, yet involving machine learning and artificial intelligence is poised to automate these strategic steps. Analyzing an app from end to end, the algorithm can come up with the most suitable test cases and even execute the easiest ones.
Beyond that, ML can automate assessment against the Human Interface Guidelines (HIG) — the process that takes days for human reviewers. Trained on the HIG requirements, the tool can ‘walk’ through an application’s pages and seek out violations, helping the team meet their release deadlines.
Machine learning has only begun making a difference in the software testing field, but the potential it holds for QA automation and DevOps is already promising. Implemented into the CI/CD pipeline, the technology will allow testing teams to not only accelerate bug detection and risk assessment but also make these processes smarter, error-free, and future-proof.
The growing interest in ML-QA integration has been lately raising the testing community’s concerns, as some fear being displaced by the more high-performing algorithms. Although the chances are high that the popularization of ML-powered testing will entail the establishment of higher standards for testing qualifications, in the foreseeable future, the technology is unlikely to become autonomous and self-sufficient enough to make the job disappear completely.