Today's engineering and QA teams are navigating a complex array of AI-powered test authoring tools, each promising to accelerate testing and enhance quality–but the abundance of options can make it difficult to know which approach is the right fit.
This comprehensive guide will help to cut through the noise and demystify the various AI-powered test authoring methods. We'll explain the basics to each method, illustrate how AI and generative AI are supercharging the approach, then explore its benefits and drawbacks for empowering teams to deliver high-quality software faster. As pioneers in intelligent test automation, mabl is committed to simplifying and streamlining the testing authoring process. We hope our insights will help you choose the right tools and strategies to achieve your quality objectives, ensuring your software remains robust and reliable in the face of ever-changing user expectations and market demands.
Navigating the AI-Powered Test Authoring Landscape
The diverse landscape of AI-powered test authoring tools can be overwhelming. To guide you through the options, we'll examine each approach in detail, outlining how AI is enhancing these methods and their associated pros and cons.
Recording Actions: The Intuitive Path to Test Automation
Quick Overview:
Recording actions is the cornerstone of low-code test automation. By capturing a user's interactions with an application—clicks, text inputs, navigation, etc.—tests are created that mimic real user journeys. This is the most direct way to simulate how a real user interacts with your application, thus making it great for ensuring a seamless user experience. This approach is intuitive and accessible, requiring minimal technical expertise.
Tools & Platforms Employing this Approach:
mabl, Testim.io, Katalon Studio, Leapwork
How AI is Supercharging This Approach:
AI-powered platforms, like mabl, elevate recording actions by capturing contextual information about the elements being interacted with, making tests far less brittle than traditional record-and-playback solutions. AI can also predict flakiness in advance, and request clarification from the user before a flaky test is even created, making tests robust from the start. These tools enable testing across a wide range of applications, including PDFs, emails, and more, without requiring in-depth knowledge of the underlying technology. AI-powered auto-healing capabilities also help to streamline maintenance by automatically updating tests to accommodate changes in the application's UI.
Pros and Cons:
The primary advantage of recording actions is the ease of use, speed of test creation, and alignment with the user experience. It's ideal for teams with limited coding skills and for quickly establishing and extending test coverage. However, it may not be able to handle the most complex or edge-case test scenarios, unless the platform being used has sufficient extensibility for adding custom code. Modern solutions like mabl pack a lot of functionality into its test editor, maximizing the scenarios that can be covered with low-code, while also extending its native functionality with code when needed. When intelligent, AI-powered capabilities are integrated into the platform, this approach can be very powerful and serve as a long-term testing solution.
Is This the Right Approach for Your Team?
Recording actions is an excellent starting point for teams who are new to test automation or for those who need to quickly validate critical user flows. And when AI is powering the core solution, it helps reduce flakiness and improve overall test quality which, with the right tool, can be extended with code to handle complex scenarios and edge cases, making it a great choice for teams where both developers and non-technical testers are responsible for quality.
Natural Language Descriptions: Bridging the Gap Between Business and Testing
Quick Overview:
NL descriptions involve writing test cases in plain English, outlining behavior you would like to see in the application. This intuitive approach lowers the barrier to entry for non-technical users, helping to shift testing to the left and facilitate collaboration between technical and non-technical team members.
Tools & Platforms Employing this Approach:
testRigor, Functionize
How AI is Supercharging This Approach:
GenAI models are revolutionizing NL test authoring. They can transform natural language instructions into executable test scripts, which drastically reduces the time and effort it requires to create tests.
Pros and Cons:
Using NL to describe the behavior of automated tests can be intuitive and easy for non-technical users to get started. However, the success of this approach hinges on mature AI models and clear, unambiguous instructions. Currently, AI models generally require a significant amount of human oversight to validate the output. Additionally, coming up with effective descriptions for tests can be challenging, especially in complex scenarios, where it might be faster and more accurate to interact with the application directly – which is also more representative of the actual user’s experience. Lasty, this approach may inadvertently produce fragile, low-quality tests that require substantial maintenance if they are translated into a traditional testing framework without the presence of AI-powered test maintenance.
Is This the Right Approach for Your Team?
As AI models advance in sophistication and reliability, writing and utilizing natural language prompts effectively will become an indispensable skill that will help teams to quickly write up a set of automated tests. In its current state, though, it hasn’t matured enough to meet the majority of testing requirements. mabl is actively developing this capability, with the end goal being that teams can generate high-quality, reliable automated tests using natural language as the primary input, while also ensuring the tests are durable for the long term, minimizing future maintenance costs.
Scripts/Coding: The Power of Precision and Customization
Quick Overview:
Scripting or coding involves writing test cases using programming languages and frameworks like Selenium, Playwright, or Appium. It’s the way tests are traditionally written and provides maximum flexibility and control, which allows testers to craft highly customized tests for complex scenarios.
Tools & Platforms Employing this Approach:
Selenium, Playwright, Cypress, Appium
How AI is Supercharging This Approach:
AI-powered coding assistants are transforming script-based test automation. These tools can suggest code snippets, identify potential errors in your tests, and even generate entire test scripts. The result is a significant boost to developer productivity and test code quality.
Pros and Cons:
Scripting is great for highly-tailored tests that target intricate scenarios. When it’s combined with AI, it offers a major productivity boost for developers. It’s highly effective for authoring unit tests and learning how to write test cases in specific frameworks. However, it still requires specialized coding skills, expertise with the end-user’s workflow, and, as applications evolve, will almost certainly result in escalating test maintenance over time. The initial investment in learning a testing framework can also be a barrier for some teams. Finally, because this approach relies on static element locators, it will lead to the creation of fragile, flaky tests that fail often, potentially leading to a lack of trust in automated tests.
Is This the Right Approach for Your Team?
These tools can be immensely beneficial for those already proficient in test frameworks like Playwright and Selenium. However, they are not a replacement for the experience of a Quality Engineer and may not be a good approach for those with limited experience in writing tests. Furthermore, you’ll face a big lift to deploy and maintain the framework itself, typically having to dedicate engineers to it. mabl’s platform is designed to give you the best of both worlds: a low-code platform that helps you create scalable testing plans, with support for scripting that helps to target complex scenarios and edge cases.
mabl’s platform supports recording actions with its low-code editor, as well as extending capabilities with code as needed with genAI-powered script generation.
User Stories & Acceptance Criteria: Aligning Tests with Requirements
Quick Overview:
This approach directly links automated tests to user stories and acceptance criteria that are defined during development using a specific syntax like Gherkin. Because these tests are defined at the outset, they are great for helping to validate that when a feature has been implemented it is meeting the specified requirements.
Tools & Platforms Employing this Approach:
Cucumber (BDD framework that uses Gherkin syntax to define user stories and acceptance criteria), TestSigma
How AI is Supercharging This Approach:
AI can translate user stories and acceptance criteria into test cases, automatically generating test scripts from the descriptions and identifying additional scenarios that may help round out your test plan.
Pros and Cons:
The key advantage is a strong focus on a feature’s requirements, since it’s defined by user stories and acceptance criteria. This ensures the feature was implemented correctly and, in theory, is aligned with the needs of its users. However, given that the underlying tests rely on scripts, this approach has the same downsides of script-based test automation: it requires coding expertise and on-going maintenance as applications evolve. AI can assist with test script generation, but human verification and involvement is often necessary to ensure the test is created and is executing as expected. Additionally, because each individual test is limited in scope and defined as a requirement, it may not cover all end-to-end user scenarios.
Is This the Right Approach for Your Team?
This approach is valuable insofar as it ensures the implementation of a feature meets the requirements defined by user stories and acceptance criteria. However, it doesn’t account for the nuances of the end-to-end user experience journeys that are present in modern, complex applications. We recommend that this approach is paired with another approach such as Recording Actions that focuses on the end-user experience.
Observed Real User Actions: Testing Based on Real-World Behavior
Quick Overview:
Observed user actions analyze real user behavior within an application to identify common usage patterns, potential bottlenecks, and areas prone to errors. Tests are then created to replicate these real-world interactions.
Tools & Platforms Employing this Approach:
Checksum
How AI is Supercharging This Approach:
AI-powered analytics and observability tools can sift through vast amounts of user data, automatically identifying key user flows and flagging potential issues. This enables teams to prioritize test creation for areas that have the greatest impact on user experience.
Pros and Cons:
The strength of this approach lies in its focus on actual user behavior. It ensures that tests reflect real-world usage patterns and can help uncover issues that might not be apparent through other testing methods. However, it's challenging to cover all edge cases and less effective for testing new functionality that has yet to be released to users, as there isn’t sufficient data to guide how the tests should be created. Further, capturing user data in a production environment may not always be possible due to technology, security, or compliance restraints.
Is This the Right Approach for Your Team?:
Observing real user actions is a valuable addition to any testing strategy, but it shouldn't be the sole method. It's best used to augment other test creation techniques. This is a great approach to help identify gaps in your existing test coverage, at which point you could use a tool like mabl to create robust, scalable tests that address those gaps and ensure a seamless user experience.
Embracing the AI-Powered Future of Testing
The future of software quality is undeniably intertwined with the ongoing advancement of AI in the testing space. There is a wide variety of AI-powered test authoring tools available today offering a diverse toolkit for teams to tailor their testing strategies to their specific needs and challenges. From the simplicity of recording actions to the power of natural language descriptions and the precision of scripting, AI is revolutionizing the way we create, maintain, and execute tests.
At mabl, our vision is to empower every team to deliver high-quality software with confidence and speed. We're committed to leading the charge in AI-driven test automation, constantly innovating to provide you with the most intelligent, efficient, and user-friendly tools available. We believe that by embracing AI, you can unlock new levels of quality, accelerate your development cycles, and deliver exceptional user experiences.
Ready to experience the power of AI-powered testing firsthand? Explore mabl's intelligent test automation platform today and discover how it can transform your testing process. Start your free trial today and see the difference that AI-driven testing can make for your team.
