Or how to continuously release a high-quality product that delights your customers.
Photo by Markus Spiske.
In today’s fast-evolving markets, a tech company that can deliver quality products to market faster than its competitors has a significant competitive advantage. At the same time, the complexity — and therefore proneness to error — of technical products has massively increased. Thus, Quality Assurance (QA) must evolve to meet the constant demands of speed to market and ensure great customer experience.
Interestingly, in a time of increased application complexity, QA can create a bottleneck to success as the majority of agile testing is still geared towards manual testing and labor-intensive creation of “automated” test scripts. Therefore, most testing activities have to focus on a small number of applications — a dilemma faced in even the most prestigious companies.
Overcoming the test-automation trap
Testing takes time and by the time a test code does its job, the requirements of consumers change, the application must progress, and test code needs to be adapted. Often, QA teams will find themselves in a test-automation trap, unable to complete the test-failure triage from a preceding automated test run before building the next testing code. Furthermore, a great opportunity for human error exists in manual testing, which can even cost more time and money — not something any company needs or should want.
Artificial Intelligence (AI) can be leveraged to solve this dilemma and accelerate manual testing. For example, test cases can be created based on existing test cases and test logs — at a much faster pace and with more correctness. Moreover, as AI agents can learn and develop themselves throughout the process, they will evolve after changes in the code base, identify code changes and find new application functions without human intervention.
From QA Engineer to QA Manager
An AI can easily work twenty-four hours a day, seven days a week, thus, tests can be executed as often as required. Most importantly, all of this can take place in real time, in the background, quickly and with a greater likelihood of correctness. QA Engineers then see the full picture and subsequently prioritize further investigations and corrections based on feature relevance to the customer. They will be able to analyze results faster and communicate results to stakeholders more efficiently. The results: customers remain happy and will spend their money, making companies delighted in profit instead of expense and wasted time.
Yes, actual people need to monitor the AI bots and its codebase to ensure its own successful functionality, but overall, the AI bots have ultimately full autonomy to do their job. But AI’s do not exist only in the world of science fiction. Currently, they actively test some of the most prestigious apps available on the market. In several areas, they even outperform world class QA teams and leading testing platforms:
Clearly, the use of AI bots produces many positive results, including a decrease in overall costs, early detection of high-risk areas for regression test, quicker time to market, increased customer satisfaction and increased profitability.
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In the next article of this series, we will focus on selected application examples.