QA has become a critical enabler for businesses that are in the digital space. To achieve digital transformation businesses should embrace the latest technologies in their software development process and build a strong data engineering foundation to fuel innovation. In this article, let’s look at how Artificial Intelligence (AI) can be leveraged in various aspects of Quality Assurance (QA) and Quality Engineering (QE), to increase speed in software development and ultimately help businesses achieve digital transformation.
According to the latest World Quality Report 2018-19 AI is going to be among the biggest trends in QA & testing for the next decade, and organizations will need to develop a strategy around it.
Adopting Artificial Intelligence and automating software testing has become inevitable, specially to get up to speed on the state of QA.
Write tests – faster, better and cheaper
Developing patterns and test cases to test how an application performs, is time consuming if it is done by a human. AI-based tools can automatically write tests for an application or system by spidering, i.e., collect data, capture screenshots and do more. Hence, AI-based testing cut costs and save time.
Requirements gathering – better than the best human
The challenge that we commonly see in the software development and testing process is our human inability to fully understand and review the requirements. The intelligent assistants understand software requirements and limitations of complex systems, which would support in better requirements gathering than a human in most cases. AI also helps in defining test requirements based on the latest trends and marketing competitiveness.
Example: To develop an eCommerce site, AI can help collect and review requirements based on competition.
Exploratory testing made easy
Since AI-based testing is trained on the collective knowledge of all people that work in the team, it helps in identifying various scenarios effortlessly. The intelligent assistants are not just used to perform testing, but also used as background tools that capture test data, user behaviours by navigating through an application or system and record default test cases.
Find system errors and new patterns of failure
When it comes to analyzing logs, AI-based analysis is already here. With AI, data-mining logs for errors and performance, and identifying the root cause of problems are made easy. Each call can have multiple sub-calls, where leveraging AI can seamlessly track and identify which part is consuming more time. Al-based tools conduct performance analysis as well as ensure security of an application or system as they identify any potential attempts of unauthorized access to the system.
Re-use test cases
AI-based automaton helps in creating well-written test cases and reuse these test cases much faster and better since the artificial intelligence or ML-based tool crawls system or an application, collects its crucial data by capturing screenshots, measuring load time, analysing basic UI elements and more.
Faster decision making
In this DevOps world, most test decisions, such as what tests need to be performed and the impact-analysis decision need to be taken very quickly. It means people need to think faster, better and smarter. Leveraging AI-based tools, instantaneous and effective decisions can be made and applications can be tested faster.
UI regression – visual UI testing and monitoring
To make sure the recent code changes have no effect on the existing features is a painful thing. The functionality of an application plus user satisfaction plays a crucial role, because for a user the backend API doesn’t matter. What matters the most is the User Connect. Machines can be accurate than humans here, and analyze outcomes of regression testing more effectively and effortlessly.
Greatest code coverage with limited time
AI-based tools help identify gaps in requirement coverage and code coverage, accordingly more tests can be planned to bridge the gaps. These tests could be based on user flows, real-time interaction, keyword and data-driven approach. Ultimately, helping businesses achieve 100% code coverage.
Conclusion:
While AI-based testing is going to be smarter as it automates software testing, there is still a need for progress in implementing the AI-assisted testing effectively for a business. AI today merely automates testing activities based on the data provided by human testers. So, human testers need to understand the significance of their ideal role, to be the Automator and leverage AI-based automation techniques that will shape the future of software testing in various or all aspects of the testing.
