Software testing is an integral step in developing software, albeit a complicated one. Applications are getting more intricate by the day. There are many aspects to consider in the testing process; hence, more developers turn end-users into software testers because they often want to release quickly.
Manual testing methods have several limitations, including the following:
- Need for frameworks that are integrated well into the training of developers and test engineers
- They are unstable and flaky after some use as the app evolves
- Require a high skill in programming languages
However, technology has advanced enough for us to move away from manual tests gradually. Automation tools can help cut down the need for manual testing to improve speed without dimming the quality.
Here are a few developments we can look forward to in the future of test automation:
Developing a Culture of Continuous Quality
More teams will develop DevOps and automate tests; the focus will increasingly be on quality more than ever. Teams, therefore, have to be more deliberate about where their tests land. There are usually two sides to this. The first are those that increase testing in the pre-merge and post-merge phases to create new workflows.
On the other hand, shifting increased tests to the pre-release and post-deployment stage could speed up delivery where practices like canary development and chaos engineering will become crucial.
Moving large test automation systems to the initial stages is challenging because this could bring up long delays while tests run in the early stages of the process. All of these processes are driving towards one end goal – to ensure the companies provide continuous and consistent quality at all times. Ideally, companies find a balance between the two methods in a way that simultaneously ensures quick delivery and reduces errors.
Intelligent Test Automation
This process will not slow down anytime soon, and people will keep coming up with intelligent and efficient testing methods. Even though a considerable number of companies still engage in manual tests, this is poised to change as humans in an important path of the value chain will slow things down. To deliver fast and stay significant with their competition, they need to get updated with automated testing.
Manual test automation also has low success rates, partly because most agile feature teams don’t have the skills to create automation scripts in sprints. This opens up opportunities for business testers and data scientists. They know to use automation testing techniques to create in-depth test automation scripts for exploratory and functional testing through payback flows and simple records.
Agile and DevOps Testing
To meet the demands of shortened periods from the development phase to the delivery phase, organizations develop agile methods and use DevOps practices. These methods cloud the differences between development, testing, and operations to emphasize continuous testing, which leads to unending development. Normally, testing is usually put at the end of the development pipeline, but with DevOps testing, this will change. Continuous testing will be a part of every cross-functional team in product development cycles in the coming years.
This automation testing process will increase the testing coverage across all aspects of the product to create fewer defects in the system. It will greatly improve efficiency as long as the companies put efforts into planning the automation suite. Proper training will ensure their team members don’t work redundantly but focus on the areas with challenges. Results from the two methods should show an increase in quality at a lower cost and faster time.
AI and ML Testing
Artificial Intelligence and Machine Learning is the world’s future, with innovations such as AI-powered apps taking center stage to reduce repetitive tasks. While several automated testing tools still need people to set their functionality and test scenarios manually, AI and ML testing are different. Artificial Intelligence and Machine Learning can automatically predict the test script-specific web pages will need and act accordingly. Even though many test tools claim they have AI and ML capabilities today, they barely scratch the surface.
ML-testing methods are averagely six times faster than code-based testing techniques because they don’t require heavy maintenance, setup of prerequisites, or other hectic processes manual methods usually need. AI & ML testing are based on built-in self-healing algorithms. A risk, however, with this testing method is that it is less mature than code-based methods, meaning there is limited flexibility. As development improves and other innovations spring up, automation techniques will reduce this risk as the process becomes more efficient.
Codeless testing tools use object, data, model, and keyword methods to decrease the coding needed to formulate tests. These scripted testing tools help foster business user testing with user-friendly interfaces that aren’t difficult to comprehend. Although these methods need policies setup and code writing at the initial process before testing automation, it is still an upgrade from the manual methods. These tools need only complex programming in the beginning stages. Hereafter, users that aren’t technically inclined can use the framework to automate tests.
A great use case of codeless testing can identify and correct specific complex code-based test scripts. If your tests don’t use stable object locators, are inconsistent in running on different platforms, or don’t handle environment-related implications properly, you have flaky test scripts. You can use automated testing methods to play the flaky scripts back several times over across platforms on the automated testing’s continuous integration server or UI.
There will be other changes in test automation over the years that might not be apparent today. Teams will craft their methods and processes to determine when to use traditional code methods and automated methods depending on their workflow. A lot of these testing tools will get smarter and solve more complicated problems in the coming years.
As teams integrate more tests into their methods, the test execution time will become the real challenge. Companies will have to come to grips to manage automated methods and manual products to increase productivity and execute their software faster. In the coming years, organizations should begin accessing how they can implement these testing methods into their workflow.