With more than 20% of organizations deploying updates multiple times per day, according to my company’s study, the complexity of test authoring has grown significantly. Test authoring is the process of scripting and maintaining automated tests, which directly impacts speed, coverage and product quality.
As applications grow in complexity, companies are adopting technological advancements like AI, low-code platforms and AI-based testing agents. To stay competitive, organizations must adopt sophisticated approaches to test authoring to ensure scalability and quality.
The Growing Complexity Of Test Authoring
Automated testing frameworks like Selenium and Cypress laid the foundation for functional automation but traditionally required significant manual effort in test authoring. As architectures become more complex, the need for advanced authoring solutions has grown.
Today, AI-powered agents/assistants for test automation are being used to automatically generate tests, update scripts and analyze code for potential vulnerabilities. These tools speed up the process of test authoring and make it adaptive—allowing tests to evolve alongside codebases, ultimately accelerating the developer productivity and feedback.
Role Of AI In Test Authoring
The market for AI-enabled testing tools is expected to hit $2 billion by 2033. AI-driven tools are transforming test authoring by automating the testing process and actively creating and adapting tests.
Incorporating AI into test authoring is reshaping how software teams create, maintain and deploy tests. For instance, AI-powered test authoring offers options ranging from record-and-playback tools to natural language test creation, coding and user-story-driven test cases. Record-and-playback, the low-code approach, simplifies automation by recording user interactions—ideal for nontechnical teams needing reliable test coverage without deep coding skills.
Meanwhile, natural language processing allows tests to be authored in plain English, making it possible for teams across skill levels to contribute to test creation. Coding remains essential for highly customized scenarios, but AI assistants can generate code snippets, helping developers speed up scripting.
Observing real-world user behavior has also emerged as a promising method, identifying common usage patterns to create test cases that reflect actual user interactions. This provides coverage for high-impact areas but can lack in addressing new features due to limited data.
AI-powered test authoring equips teams to optimize software quality while keeping up with fast-paced development.
Best Practices For Modern Test Authoring
The following best practices ensure that test authoring remains efficient and reliable:
1. Design for reusability.
Reusability reduces redundancy and accelerates future test authoring efforts. Structuring tests in a modular manner—using shared components and libraries—enables tests to be reused across multiple environments and projects.
By avoiding duplication, organizations can reduce the time spent authoring new tests while increasing test coverage.
2. Implement data-driven testing.
Data-driven testing separates test logic from test data, enabling the same test to be executed with different datasets. This method increases test coverage without creating multiple redundant scripts, leading to more efficient and scalable test authoring.
For instance, test data can be stored in external files like Excel or databases, allowing tests to iterate over various input combinations without modifying the core script.
3. Leverage self-healing tests.
Self-healing tests—where AI detects changes in the application and automatically updates test scripts—are becoming critical. Implementing frameworks or tools that support such capabilities reduces test maintenance, especially when dealing with rapidly evolving codebases. Approximately 24.9% of companies are leveraging AI/ML in test-healing activities according to my company’s study.
4. Maintain clear test case documentation.
Ensure proper documentation of your test cases, including their purpose, steps involved, expected results and dependencies. Good documentation makes it easier for teams to update, review and optimize tests in the future.
5. Optimize for performance and reliability.
When your test suite becomes large, it often slows down your test execution speed. Optimize tests for performance by parallelizing tests. You can also consider running them in cloud-based environments for scalability. Execute tests across multiple browsers and OS in parallel to ensure faster results without compromising on coverage.
Tracking Key Metrics For Effective Test Authoring
While test coverage is one of the most important metrics to measure the success of your test authoring, here are the other four major metrics:
• Test case effectiveness measures the ability of tests to identify defects. If your automated tests are passing but issues are still discovered in production, it indicates a gap in test effectiveness.
• Test execution time captures how long it takes to run a test suite. Reducing execution time is critical, especially in fast-paced CI/CD environments.
• Pass/fail ratio tracks the ratio of test cases that pass versus those that fail. A consistently high failure rate might indicate underlying issues with the application or test stability.
• Test maintenance cost tells how much time and effort is spent maintaining tests, particularly those that are frequently updated and can help identify inefficiencies in the test authoring process.
Overcoming The Limitations
While AI-based test authoring delivers numerous benefits, it’s not without challenges:
• Initial setup challenges: AI tools often require setup, training and integration investment. Teams with legacy systems may struggle to adapt to the new workflows. To ease the transition, implement a phased approach—start with simpler tasks like test generation. Provide training and select tools with intuitive interfaces to ease adoption.
• Reliance on historical data: As AI generates test cases based on past data, it can lead to gaps in coverage when addressing new features or edge cases. This can be handled by supplementing AI with manual testing for new functionalities and edge cases. Also regularly update these AI models with fresh data to ensure better adaptability and accuracy.
• Difficulty with complex scenarios: AI tools excel at automating repetitive tasks but struggle with nonlinear or highly customized test cases, such as performance or security testing. Combine AI with manual expertise. While AI handles routine automation, human testers focus on designing scenarios that require creativity and critical thinking.
Test authoring is no longer a manual, code-heavy task. It is more integrated with real-world data, making tests smarter and more aligned with user behavior. I think we can also expect self-healing tests to become a standard feature in most testing frameworks, reducing the burden of test maintenance and increasing the reliability of automated tests.
https://www.forbes.com/councils/forbesbusinesscouncil/2025/01/07/best-practices-for-modern-test-authoring-to-deliver-scalable-and-efficient-automation/a>