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AI's Role in Test Automation: Boosting Speed and Accuracy



It has now become obvious that AI is at the heart of software development and provides businesses worldwide great solutions to achieve a continuous delivery state. AI can not only help developers to simplify their tasks, make them smarter and more efficient, cut down on boring practices; it lets testers build better test cases since software tests are automated to precisely meet the requirements of governing regulations.

This article gives an elaborate overview on the benefits of AI in increasing efficiency during testing, and specifically test automation. It will also provide a high-level overview of different AI tools, which help testers perform their work in every step including the test design and its implementation, up to results interpretation and scripts maintenance.


Confronted with a large number of requirements at the initial stage, testers may be engaged in developing hundreds of test cases during software project. It is a time-consuming process that may need even more work due to the fact that interpreted requirements often change. This is a typical feature that underlies the software-development process. Taking the AI assistance into consideration would help to make requirement analysis and test case creation more organized.

AI can analyze requirements to determine if there are any dependencies, conflicts or ambiguities. In addition, upon feeding in elements such as business value, complexity and interdependencies; AI can help drive prioritizing these needs. This superiority plays a significant role in the efficient design of test cases.

For effective AI-driven test case generation, it is essential to provide a well-defined and prioritized set of requirements. This involves specifying which categories of requirements, especially those with a particular priority level should have test cases. To enhance the AI's efficiency in generating test cases and reduce the need for manual intervention or automation in test execution, a detailed template should be presented. This template should include specific guidelines, such as the preferred length of test titles, the number of steps per case, and any declarative limits (like steps per second). Additionally, it should incorporate commonly applied standards and practices in test case design. By doing so, we ensure that the AI system can create more accurate and relevant test cases, ultimately maintaining high performance in test execution

The AI can also help in identifying the order of importance for each test case and to conclude whether they are likely candidates for automation or manual testing. This method not only speeds up the process but at the same time, increases its quality and efficiency.


No matter how much careful work has already been done to construct all test cases in a very organized manner, including AI as an additional auditor is also going to be productive. AI can help to determine if your test ranges through complete domain of the requirements, ensures if well-defined and clearly communicated specifications for a range list tests, examines there is consistency in format across different cases. In addition, AI can analyze that new development product and figure out some test cases which are critical but may not be included in the current test set or requirements. 

AI has an important role to play in predicting where defects are most likely to occur within the application. This capability has allowed you to expend your testing resources in a more effective manner on the higher-risk modules. In the world of data-element driven testing, AI is very useful in analyzing test information to generate novel test cases especially those that have not yet been induced from developed established requirements.


One of the major use-cases in software testing involving AI is automated tests generation. In most cases, developing and sustaining test automation is a slow process. Even though one of the most important functions automated tests perform is in speeding up releases within continual delivery frameworks, they may prolong delays if testing becomes broken or unreliable. AI could help in the improvement of the procedure to a high level by coming up with automated test adapters and keeping them updated, especially during changes. In this way, the utilization of AI may speed up test development and provide automatic or semi-automatic updates to test scripts as a result of changes in the system under test. Here are a few ways AI can be leveraged for automated test generation:


Using generative AI chatbots: including ChatGPT, Google Bard AI, Microsoft Bing AI or ZenoChat. etc., to generate test scripts. With the help of these generative AI tools, it is sufficient to send them a prompt that describes the test case for them automatically generate an appropriate test script.

Most of these AI services provide also API support, enabling integration with test management systems. For instance, test case specifications can be fetched from the test management system and sent to chatbot API creating a related script. This method helps not only to simplify the process of testing but also increase efficiency and reliability in software design and delivery.

Below is an example prompt to ask AI Zeno Chat: (https://textcortex.com/templates/zeno-chat-gpt-alternative) generating a Robot Framework test cases and its response.

Using AI-powered test tools:

  • Testim.io: uses machine learning for the authoring, execution, and maintenance of automated test cases, offering self-healing capabilities and smart locators to reduce the effort required in maintaining test scripts.
  • Mabl (mabl.com): an intelligent test automation platform that integrates AI and machine learning for script maintenance, featuring self-healing tests, visual regression testing, and predictive test maintenance.
  • Applitools (applitools.com): known for AI-powered visual testing and monitoring tools, it uses Visual AI to automatically validate the appearance of UI component, useful for detecting visual regression and ensuring consistent user experience across devices and browsers.
  • Functionize (functionize.com): uses machine learning for test creation, maintenance and analytics, offers test repair to adapt to changes in the application and reduce maintenance workload.
  • AutonomoIQ (autonomiq.io): offers features like NLP to create test scripts, self-healing tests, and AI-based analysis
  • SeaLights (sealights.io): Sealights focuses on test analytics and provides insights into the quality of the testing. It uses AI to offer recommendations on test coverage and risk, helping in predictive maintenance and optimization

Running pre-trained AI models: it is recommended to expand the consideration of AI models for test automation beyond commercial tools and dive into open-source as well free solutions. Such resources provide many pre-trained models that are available and which may be very efficient. Llama 2 (https://llama.meta.com) is definitely one that should be pointed out for its abilities to analyze and understand code. Moreover, other models that deserve consideration can also be observed on sites such as gpt4all.io or huggingface.co/models. Each model is unique and presents different characteristics, which come in handy when conducting various code analysis tasks or any other undertakings carried out by using AI.


Automated test scripts, just like any other form of code, require thorough review to ensure their correctness, readability, and maintainability. This critical step, however, is often overlooked or inadequately performed, largely due to time constraints, particularly when testing becomes a bottleneck in a project. In such scenarios, leveraging AI for script review emerges as an ideal solution.

By utilizing AI for this task, you can simply submit your scripts and specify the aspects you need to be evaluated. The AI is capable of meticulously analyzing your scripts for code quality, which includes checking syntax, structure, and naming conventions. It can also identify and suggest the elimination of redundant components or propose optimizations to enhance efficiency. Furthermore, AI tools can ensure that your scripts align with both industry standards and your organization's specific coding guidelines.

To maximize the effectiveness of AI in this process, it's beneficial to provide as much contextual information as possible. This can include documentation of your testing framework, details about the environments where the tests are executed, and any specific requirements or constraints relevant to your project. The more comprehensive the information provided, the more accurately and effectively the AI can assist in refining and optimizing your automated test scripts.

Here are some examples of prompt for requesting script review to generative AI:

“These are my automated test scripts written in Robot Framework. Please review it for any syntax errors, logical errors, or areas of improvement?”

“Here are my complete automated tests script written in Cypress. Please review and provide feedback based on Cypress best practices, also suggestions for making the code more readable and maintainable”

“Compare my automated test script below with the example script in the attached file and suggest any changes required to comply with the script conventions in the template.”


One of the most critical aspects regulating various data types in modern software landscapes is an efficient process. Testing applications can be done effectively if there is an adequate amount of diverse data. AI provides speed and efficiency using its features like learning, reconfiguration, processing of large datasets. Due to the ability for rapid learning as well adaptation to different environments it has become a useful tool in data generation test procedure.

Key scenarios where AI enhances test data generation include:

- User Behavior Simulation: creating realistic users for testing.

- Localization Testing: manufacturing data in various linguistic and formatted arrangements.

- High-volume Data Testing: creating BigData required for stress and performance testing.

- Security and Vulnerability Testing: designing data with an underlying security threat.

- Complex Data Patterns: simulating complex designs through data patterns.


Failure of test report, in every critical area has become an increasingly more laborious process to diagnose its reasons. This lengthens the development feedback loop and also prolongs the release cycles. Furthermore, shifting test results as a consequence of shaky or ‘flaky’ tests are tremendous problems with regards to preserving steady model environment. Fortunately, such tedious tasks can be significantly relieved through AI-aided automation.

The following are key areas where AI excels in test report analysis: 

- Identification of Flaky Tests: AI systems detect tests that produce different results on repeat runs quite effectively. This capability allows recognizing problems and addressing them in terms of stabilization within the test bed.

- In-depth Root Cause Analysis: AI is good in looking for patterns of failure and print out the defects log, it may be possible to find many reasons why tests are failing.

- Tailored Reporting: Indeed, AI is capable of generating customized reports for all stakeholders in their efforts to assess project performance by focusing on metrics that are specific and particular interests. This includes developers targets through the design cycle feedback mechanism whereas software or human recourses managers seek information useful for them such as collective planning how they will utilize resources way ahead arduously which cannot survive without relevant This guarantees that every group gets appropriate information targeted at their requirement.


One of the most common misunderstandings for automated test scripts is once it has been developed, it will continue to work endlessly without change. In fact, given that software is constantly evolving and being refactored these automated scripts have to be kept in place. Such scripts should indeed be maintained regularly, not just to ensure their reliability and stability but also reduce execution rate by shortening the feedback loop.

In some cases, AI can act as a significant part in automating the process of these test scripts maintenance. Sometime a minor change in the software, for example, the id of a web element could break and fail the whole regression suite which will require another night for re-execution. Some AI tools can automatically detect minor changes as such and update the automated test scripts accordingly before executing and that will save a lot of time and resources.

AI might not always detect significant or less apparent changes, however, integration of AI into script maintenance is a promising step towards more resilient and efficient testing processes. 


Undoubtedly, AI contributes to how software testing is being done. It helps make it quicker, smarter and easier. It is pretty much like what can be described as a great assistant that from the start helps organize and prioritize duties through testing sequence. AI additionally takes some time off the testing structure, and naturally finds scope sickness in test works out and revamps the same audit content.

What is really interesting about AI technology that it can learn. This can be described as a continuous process of improving your automated tests over time while keeping in track with the new software changes being done for minimal maintenance gets performed. In conclusion, AI as an aid is quite transform into a need of paramount importance to the software development industry. In the era of AI evolution it will certainly become one more necessary tool, in a tester’s kit not so stressful and beneficial for testing.

This article was written by IsoSkills Test automation expert.

IsoSkills has a proven expertise in the testing are and it stays at the forefront of innovation in test automation sector. Many clients in Finland and around the world benefited of our test maturity assessment service and of our test automation experience.

Our automation platform we recently developed is based on Selenium, Robot Framework, Appium, Docker, Jenkins, Git, AWS, and it encompasses the possibility to utilize AI in creation of the testcases for web and mobile applications. If interested in having a such automation platform in your project, do not hesitate to contact us, we could offer it for being piloted for free.

Want to talk to us about our testing and automation services and consultancy, please contact our Director of Sourcing and Business Development, Jaana Vaananen.



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