13 Testing Trends for 2023
Some trends are just the flash in the pan. In theory, they are full of promise but come real-case tasks, they often reveal their limitations and impracticality. We’ve hand-picked test automation trends for 2023 that seem both realistic to implement and have the potential to truly make a difference in how we assure quality.
QAOps Is Gaining Popularity
QAOps is a booming trend in 2023. As the name suggests, this methodology combines a Quality Assurance (QA) approach and IT operations to speed up testing. In other words, it integrates quality assurance and testing into the DevOps process, introducing thorough testing at every stage of the software development cycle, not just at the end.
To elaborate, QAOps framework allows developers and testers to work together. Through this close-knit collaboration, companies can pinpoint the different scenarios that real users might encounter when they start interacting with the app, thereby improving the testing process.
Some companies have given this approach their own twist. They organize so-called “testing parties”, inviting all interested team members to take part in testing. By doing so, they not only ensure there’s no bias in testing, but they also get the opportunity to test their products from different perspectives, allowing them to ultimately achieve better outcomes.
It’s like building a house where developers build the foundation, walls, and the roof, and testers ensure from the beginning that all of these components are made strong. Later, when the house is ready for exploitation, no major rework will have to be done.
- Improved collaboration between testers, developers, and IT operations teams.
- Boosted productivity of the team through ongoing knowledge sharing.
- Faster release of new products and features due to the quick detection of bugs.
- Enhanced customer experience achieved by delivering software products of a high-quality standard.
Extensive Adoption of Robotic Process Automation
Another trend that is growing in popularity in test automation is the use of Robotic Process Automation (RPA). RPA, also known as software robotics, has the ability to mirror testers’ interactions with software applications. By recording actions performed by testers and learning testing sequences, it can imitate the same process, saving you lots of time performing repetitive tasks.
RPA has become widely popular due to the ease and speed of its implementation. Compared to traditional automation, which requires specialized hardware and software to automate repetitive tasks, RPA uses bots. Often run by humans, these bots can be easily programmed using off-the-shelf software and trained in a matter of hours or a couple of days, making RPA a more cost-effective testing solution for most companies.
RPA testing techniques are being used across various industries and will become even more widespread in the near future. According to Statista, the RPA market will grow to $13.39 billion by 2030, which is a huge jump from $3.17 billion in 2022.
Moreover, RPA will become much more intelligent thanks to the advances in artificial intelligence and machine learning soon. This means that RPA will not only copy human actions but also understand information, learn from experience, and proactively address issues. Ultimately, this will allow it to execute more complex decision-making processes without much guidance on the tester’s part.
- Reduced labor costs due to the automation of repetitive test scenarios.
- Less of a risk for human error, as RPA accurately replicates testers’ interactions.
- Significant time and resource savings due to RPA bots’ ability to execute tests across multiple systems and applications.
- Scalability that allows organizations to perform small, large, or even enterprise-level tests on demand.
Active Use of AI and ML Testing
AI and ML tools are making a splash in the testing industry. With their ability to automate virtually every aspect of testing, from test case creation and test execution to test maintenance, they come indispensable for QA teams.
The practical applications of AI and ML in automated testing are numerous. From identifying features for testing, creating test cases without manual test scripts, and running thousands of tests virtually in minutes, it can do all that and much more without human support.
As we progress in the future, the capabilities of AI and ML are expected to only evolve. In the near term, it will be possible to digitize testing processes by creating AI-powered avatars of famous testers, such as Bret Pettichord, Cem Karner, Tariq, you name it. By imparting their knowledge and expertise to bots, organizations will be able to create strong virtual teams of testers that can execute all types of testing and adequately measure the quality of a project based on input derivable.
Other examples of using AI and ML in testing include:
- Natural language processing. NLP algorithms can extract and analyze requirements from natural language documents, helping with test case creation and ensuring that tests align with the project’s goals.
- Visual testing with computer vision. AI-driven computer vision systems can automatically compare UI elements and screens to identify visual defects or inconsistencies in applications.
- Automated bug triaging. ML models can help prioritize and categorize incoming bug reports.
- Behavior-driven testing with AI. AI can understand user behavior patterns and generate test scenarios that mimic real-world user interactions, enabling QA teams to cover more test cases.
- Security testing. AI can simulate cyberattacks and identify vulnerabilities in software.
Aside from that, AI excels at predicting outcomes. By analyzing historical data and patterns, it can identify with a high probability what bugs and issues are likely to occur and help prevent them before they become major issues.