Over 90% of companies have adopted test automation, completely or partially, as a part of their integrated QA approach. This rise can be attributed to a more competitive market with hundreds of companies jostling for space. There’s relentless pressure to develop faster and better. But without automation testing in place, companies are driven to make a trade-off between quality and speed.
Companies need to integrate test automation into their business processes and establish a shared responsibility culture to avoid this. Furthermore, they must be alert about the scope of test automation in the future. In light of that, here’s what the future of automation testing looks like.
What is the scope of test automation in the future?
While it’s clear that automation is transforming the quality assurance landscape, here are eight key trends that will define the future of automation testing.
Cloud to host automation testing
Cloud computing as a trend emerged in the last decade and has now infiltrated almost every sphere of software development. The flexibility offered by cloud infrastructure in terms of scalability, access, and set-up time has made it a primary choice of most organizations. Test automation will be fully hosted on cloud, ensuring 24/7 access and low maintenance overheads for organizations.
Automation will drive continuous testing
Quality must become a shared mindset amongst all team members to gain the most ROI from automation. With the entire team sharing responsibility for quality assurance, testing has become more of a continuous process than a siloed phase limited to the end of the cycle. Continuous testing reduces the risk by speeding up the feedback loop. With automation, companies can ensure tests are conducted after every compilation and reports are generated automatically.
Microservices is another key trend to look out for in software development. It helps with the compartmentalization of every small software component to increase flexibility and ease of reuse. As companies adopt this trend, even test automation will have to adapt and introduce products customized for microservices.
Agile adoption is at an all-time high, with 71% of companies using an agile-based approach to development. But many companies have issues with transitioning to an agile-ready environment due to a lack of automation. Agile requires testing to be a continuous process and an integral part of the CI/CD pipeline. Unfortunately, due to inadequate automation, companies usually relegate testing for the features developed in one sprint to the next sprint. This creates a divide that needs to be bridged. Developers can increase test coverage by introducing in-sprint test automation and plug the gaps.
Crowdsourced testing helps teams meet the skill and knowledge requirements of advanced automated testing. Instead of relying on a single, fixed team to do the testing, crowdsourcing offers a more scalable solution by dividing the testing tasks between different testers based on their skill sets and experiences. It’s a faster option and can bring quick results.
Scriptless test automation with NLP
Many new automation testing solutions have gone codeless. Scriptless solutions make testing more accessible to all. These automation solutions help remove knowledge barriers to testing and shorten the learning curve. Another addition to this factor is the introduction of Natural Language Processing. NLP enables testers to simply use natural language constructs and some backend configuration to ready the tests. NLP resembles no-code solutions in that it, too, does not require the writing of even a single line of code. However, as NLP is a new entrant to the market, teams seeking to transition to it will need to make heavy investments/changes to their existing infrastructure, unlike no-code solutions, which have a wider acceptance.
Artificial intelligence in testing has provided two advantages – codeless automation solutions and computer vision. Computer vision enables a computer to recognize images in the UI, much like humans. This allows testers to forego complex locator strings in the test scripts, making them less brittle.
Machine learning and predictive test selection
Predictive test selection addresses the most common problem with manual test selection, which chooses a static subset of tests from a larger suite. The problem with manual selection is that the chosen subset may not be the best set of tests, leading to erroneous conclusions about the code change. Predictive test selection relies on machine learning solutions to process changes and identifies the most critical tests to run. This can reduce a 5-hour test suite to less than 30 minutes.
Quality assurance managers today know that without AI and ML, their automation suites will become obsolete in the future. Opting for an automation solution with AI capabilities, helps your team be future-ready. Avo Assure is a codeless heterogeneous test automation solution with a pre-built 1400 keyword library to rapidly build test cases. It also offers parallel testing, cross-platform testing, an intuitive interface, and intelligent reporting capabilities. Avo Assure helped a large Fortune 500 Bank achieve 100% test coverage with end-to-end automation and a 63% reduction in associated costs.
Schedule a demo today to learn more about Avo can help you be ready for the future of automation testing.