Are you a tester implementing automation testing in the current development environment? You must have already realized that automation testing has now become an inseparable part of the modern development and testing process.
Have you wondered what the future holds in the segment of automation testing? If not, don’t worry. We have got you covered! Our article will scour through all the advancements in automation testing including artificial intelligence, machine learning, and cloud computing. All these inclusions are aimed at making automation testing more sophisticated, efficient, and robust.
Emerging Trends and Technologies for Automation Testing
Multiple factors will help shape the future of automation testing. We have shortlisted some of the most important ones that will drastically change how you execute automation testing. We also believe that by incorporating these parameters within your testing environment, you can massively outperform your competition and gain a lot of traction.
- AI and Machine Learning in Test Automation
Let us now divert our attention towards how artificial intelligence and machine learning will benefit the execution of automation testing in the near future.
Intelligent Test Automation
Artificial intelligence and machine learning are transforming almost every segment of the modern generation, including automation testing. The incorporation of these parameters will make the test scripts more intelligent and adaptive.
Compared to traditional automation frameworks, which rely on predefined test scripts, this inclusion can enhance these frameworks by learning from past test executions and adapting test cases based on the changes in the application.
For example, AI power tools can automatically generate test cases and prioritize them based on the risk. These tools can even predict the potential areas of failures that might arise within the main architecture of an application. The AI algorithms will analyze historical test data to identify patterns and suggest optimal testing scenarios.
The inclusion of these processes will overall help reduce the time and effort required to create and execute the test scripts.
Self-Healing Test Scripts
Currently, one of the major challenges of automation testing is maintaining the test scripts when there are frequent changes in the application that is being tested. By incorporating self-healing test automation, the developers can address this issue. This approach dynamically identifies and updates the broken test scripts to ensure that they are reusable.
At its core, self-healing scripts use artificial intelligence algorithms to recognize changes within the application’s user interface elements. These elements mainly consist of button names or field IDs. These scripts will also automatically update the scripts to reflect these changes as per the progression of the application.
- Shift-Left Testing and Continuous Testing
Let us now shed some light on Shift-left testing and continuous testing. These are two of the most unique and advanced parameters that are starting to have a significant influence on the execution of automation test scripts.
Shift-Left Testing
Shift-left testing helps you focus on testing at the earlier stages in the software development life cycle. This approach is mainly controlled by the need to identify and address defects as early as possible. It will help you to reduce the cost and effort associated with fixing them later.
The working mechanism of this process is to integrate the testing process into the development life cycle. Here, the developers and testers will work closely to write the test cases concurrently within the code.
Automation will also play a very important role in this entire architecture to enable continuous testing. This is because the tests are run automatically whenever code is committed into the code repository.
Continuous Testing
We can identify continuous testing as a natural extension of Shift-left testing. This process involves implementing the testing process throughout the development cycle, starting from the initial stages of development all the way to post-deployment.
This approach is mainly controlled by DevOps practices where automation is important to ensure continuous integration and continuous deployment of modern applications.
Continuous testing is also heavily reliant on automation tools that can execute the test rapidly and provide real-time feedback to the test developers. So, by integrating artificial intelligence and machine learning, these tools can prioritize the test cases, identify areas of high risk, and also optimize the overall execution process.
- Test Automation On The Cloud
Cloud-based automation testing has been gaining rapid traction due to its massive set of advantages. Let us now divert our attention to how this trend will continue to shape the future of automation testing.
Cloud-Based Testing Platforms
Cloud computing has completely changed how organizations develop, deploy, and manage modern applications. Cloud-based testing platforms offer various advantages like scalability, cost efficiency, and flexibility. These platforms also allow the teams to run the test cases on multiple real devices through remote servers.
To further improve our knowledge of cloud-based testing platforms, let us use LambdaTest as our primary reference point.
LambdaTest is an AI-powered test orchestration and execution platform that lets you perform manual and automation testing at scale with over 3000 real devices, browsers, and OS combinations.
It allows you to use various frameworks simultaneously, such as Selenium Java, Selenium Python, TestNG, JUnit, and more. This capability also ensures you can perform automated cross-browser testing in a parallel configuration.
This platform has various additional features like live test activity logging and native bug tracking. Moreover, after executing the test cases, LambdaTest will generate a comprehensive test report consisting of multiple videos and screenshots to easily perform the debugging process.
Testing as a Service (TaaS)
Testing as a service is rapidly climbing the popularity ladder by invoking a cloud infrastructure to offer on-demand testing services. This unique service offers a range of testing services, including performance, functional, security, and compatibility testing. So, organizations can utilize these services to augment their in-house capabilities, accelerate their time to market, and reduce overall testing costs.
TaaS is very beneficial for organizations with fluctuating testing needs or limited testing resources. This is because it will enable them to scale their testing efforts up or down based on their current requirements. To implement this process, the owner does not have to go through any significant upfront investments in terms of hardware or software.
- Low-Code and No-Code Test Automation
Let us now divert our focus to how automation testing will become more welcoming to newcomers with the inclusion of low-code and no-code test automation practices. This inclusion will also reduce the learning curve for anyone who is new to test automation.
Low-Code Automation
Low-code automation tools allow the testers to create automation test scripts using visual interfaces and drag-and-drop components. Due to this intriguing test script development process, we do not require any extensive coding knowledge to use them. So, these tools will significantly reduce the learning curve and enable manual testers and business analysts to participate in automation efforts.
Low-code platforms accelerate test automation and maintenance, making them ideal for agile teams. This is because agile teams require rapid test development processes. Using low-code automation tools, the testers can also promote collaboration between the other stakeholders of the company. This is because the test cases become more accessible and easier to understand.
No-Code Automation
This is a very amazing technology, as you can use it to create automation test scripts without writing even a single line of code. To perform this process, These tools rely on natural language processing and artificial intelligence to understand user inputs and generate test scripts automatically.
The main goal of implementing no-code automation testing is to make automation testing available within the entire organization. These tools can be very useful for testing simple workflows or regression tests and executing repetitive tasks within the testing cycle. Developers can also use no-code automation to automate tests quickly and efficiently, which will help free up resources for more complex and innovative testing activities.
- Robotic Process Automation in Testing
Robotic process automation was traditionally used to automate repetitive business processes. However, it is also gaining traction in terms of software development and testing. RPA tools can mimic various human actions like interacting with the user interface elements, performing calculations, and entering data. This ability makes it ideal for automating end-to-end testing scenarios.
Robotic process automation can also be useful for automatic regression tests, data validation, and testing complex workflows. This is because all these processes generally involve multiple systems and integrations.
So, by combining robotic process automation and artificial intelligence along with machine learning, organizations can create more intelligent test automation frameworks that can keep up with changes and optimize the testing process along the way.
- Test Data Management and Synthetic Data Generation
Test data management is a very important part of automation testing. This is because accurate and comprehensive test data is very important for executing meaningful tests and validating the behavior of the applications. However, it can become very challenging to actually manage these test data especially when you’re working with complex environments that consist of multiple data sources.
Various new test data management tools use artificial intelligence and machine learning to automate the test data generation, masking, and management process. The testers can also use these tools to create synthetic data that mimics real-world testing scenarios.
Never heard of synthetic data generation? It is basically creating artificial data that resembles real-world data. The testers can use these data to simulate various scenarios. These scenarios can include edge cases, rare events, and boundary conditions that can be difficult to achieve with real data.
The implementation of all these processes will ensure the tests are comprehensive and accurate while complying with various data privacy regulations. These regulations are very important to ensure the proper protection of sensitive customer data.
The Bottom Line
Based on all the factors that we discussed in this article, we can safely predict that the future of automation testing will be controlled by a combination of new technologies and trends. These new additions will change automated testing from a bunch of scripted procedures to a more intelligent and adaptive process.
As developing organizations continue to deliver high quality software, the adoption of these technologies and trends will become increasingly critical to complete this process more quickly and efficiently.
Finally, by embracing the future of automation testing, testers can enhance their testing abilities, reduce costs, and accelerate the time to market for all modern applications. The ultimate goal for this process is to deliver better software products to users and enhance a positive reputation.
