Codeless Test Automation: Being a software-driven business, you can never afford to have flawed applications. Such applications drive your hard-earned customers away, shut the possible doors for potential customers, and make your QA teams work harder.
Are you having flashbacks of such scenarios in your organization? Well, not anymore! With AI-powered automation testing, you can accurately check if your software is good enough while saving time and resources.
AI testing makes it easier for teams to create test cases, execute them, and maintain them while providing faster and more reliable feedback and bug identification. It is why the market size for AI-enabled testing tools is bound to reach a valuation of US $426.1 million in 2023, and it is projected to rise to US $2030.75 by 2033.
Clearly, AI in automation testing is here to stay. But remember that not all automation testing tools can keep up with today’s consumer demands, application complexity, and frequency of software releases. It is why choosing the best AI-powered codeless testing tools for your testing project has become important.
Keeping all this in mind, we have curated this blog post for you to discuss AI in codeless testing in detail and introduce you to one of the most trusted and efficient AI testing platforms, TestGrid. So, keep reading until the last if you want to fight the challenges of scalability, complexity, and time and resource constraints with the precision of AI-driven codeless testing.
Challenges Faced By QA Teams in Testing Process:
In a testing project, manual and automation are equally important to get the desired end results. These processes can inherit some complexities that prove to be hurdles while performing testing. These challenges include:
1. Selecting The Right Team Of Experts
The first challenge with manual and codeless test automation is that it requires companies to build testing teams with high proficiency, skill set, and experience. Finding someone that aligns with the company’s vision and requirements can be tiresome and time-consuming.
Business leaders must vet the candidates carefully, communicate with them effectively, and demonstrate a high level of patience during the process to finalize the apt candidate. Because of these reasons, companies prefer AI-based codeless testing tools that can assist them while they expand their team.
2. Increased Dependence on Manual Effort
In automation testing, each test case has to be manually encoded as a script. While this requires a team of professionals with coding experience, it also requires research on the company’s processes for designing test steps, getting input data, and ensuring output expectations.
Moreover, the reusable components of test cases cannot be fully utilized as it is for similar projects. The testers are required to write more code manually as the new project demands.
3. Extra Time For Maintenance of Test Scripts
Whenever there are changes in the UI, it becomes important for companies to align their visual components with the functionality of their application. However, these changes impact the already existing test scripts and require modification.
As a result, this hampers the productivity of the testing teams as they have to allocate additional time for maintenance and monitoring of test cases to ensure they remain effective in the long run.
With the advent of AI-powered codeless test automation solutions, companies can now overcome these challenges and streamline their testing process. By using the capabilities of AI in codeless testing, you can enable automated detection and adjustment of test scripts, thereby reducing the overall time spent by QA teams earlier.
Advantages of AI in Codeless Test Automation:
Now that you have a basic idea about the need for AI in codeless test automation, it is time to delve deeper into the benefits it offers to companies looking to accelerate their software releases:
- Improved Accessibility
With AI-powered codeless testing tools’ simple-to-use interface, project managers and stakeholders can collaborate without extensive technical knowledge. All users need is a basic understanding of HTML and CSS to run, edit, and maintain test cases.
Moreover, with the currently available AI codeless test automation tools, users get a wide range of features like recording and playback, codeless test maintenance, reporting, and built-in execution. As a result, testing teams get an additional benefit to speed up the testing process.
- Self-Healing of Test Scripts
Earlier, when a test script broke due to an unidentified object property change, testers would have to put everything on hold and search for the object. Then, they would be required to update the test script and test everything again.
With each of these steps, the time for completing automation testing increases. However, QA teams take a sigh of relief now with AI codeless test automation tools as they offer self-healing capabilities.
The self-healing features of codeless testing tools launch a full-scale investigation to analyze the problem and take corrective action without relying on manual efforts. Moreover, it can identify patterns, learn from past executions, and apply the same knowledge in the future if something similar comes up.
- Enhanced Learning Curve
With automation and manual testing, companies generally face a steeper learning curve as it requires the presence of people with the right technical skills. Testers must learn programming languages, understand automation frameworks, and require extensive training before undertaking any significant project.
On the other hand, with AI-powered codeless test automation tools, there is reduced dependability on people with coding skills. They are designed to be user-friendly so that all team members can use them regardless of their backgrounds.
Conclusion:
With significant changes coming up in software applications as consumers demand additional features and updates, organizations can only stay ahead of the competition by leveraging AI to improve testing processes. It will allow testers without coding knowledge to initiate test automation and develop efficient test scripts.
To reap these benefits to the fullest, it is crucial to choose an AI-powered codeless testing tool that can understand the changing technological landscape and complexities of the application to fulfill all your business needs.
This blog is originally published at TestGrid