AI in test automation: 10 "then" and "now" transformations shaping the industry

While test automation revolutionized testing a decade ago, in recent years we have witnessed the next level of domain development. In a similar way to how test automation improved and sped up the testing workflow, AI is now doing the same. It performs routine tasks, simplifies complicated ones, assists in creating and maintaining test scripts, and executes many additional tasks and processes for test automation. AI-based testing tools are useful for automating reporting, data processing, and other data-related tasks. Today, at Zebrunner, we delve into how AI is currently transforming the landscape of test automation!

AI/ML in testing: hype vs. reality

AI enthusiasts and advocates promote the idea that AI can fundamentally transform the testing domain. They argue that AI reduces the time and effort required for creating and maintaining test scripts, especially when using testing tools with embedded AI/ML features. Similarly, they claim that AI enhances accuracy, improves test coverage, and increases precision, thereby reducing the likelihood of missed defects.

However, in reality, we have not yet achieved this desirable feature. Now AI serves primarily as a feature that just simplifies testers' routine work. For example, AI cannot achieve 100% accuracy because AI models are only as effective as the data on which they are trained and require continuous refinement. In addition, AI cannot detect every possible defect. While it enhances detection capabilities, it cannot guarantee the discovery of all defects, particularly those that require human intuition.

And what is the situation in reality in detail?

According to the report “State of Continuous Testing 2024” conducted by Perfecto and BlazeMeter, AI and ML are gaining popularity in the testing field. However, despite organizations' hopes to adopt AI and ML to enhance their testing workflows, implementation is still lagging. While 48% of respondents expressed interest in AI but have not yet started any initiatives, only 11% are currently implementing AI techniques. Interest remains strong, with 40% of respondents either already implementing or actively researching and planning to adopt AI. 

As we see, AI and ML are not yet shaping the entire test automation landscape or defining all the trends in the field. AI remains just one of the trends.

How AI works in test automation

Traditionally, the incorporation of an AI tool or approach begins with training a machine learning (ML) model on specific organizational datasets. These datasets include the codebase, application interface, logs, and test scripts & test cases. The efficiency of the algorithm relies heavily on the quantity of data: the more data you provide to train your ML model, the more precise and effective it becomes.

Some tools offer pre-trained models that are continuously updated through learning for specific applications, such as UI testing. This allows for generalized learning to be applied to a specific organization.

Depending on the use case, the model can generate test scripts, evaluate existing test cases and scripts for code coverage, completeness, and accuracy, and even execute tests. However, a tester must review the generated output to validate and ensure its usability.

Additionally, AI/ML excels at analyzing test automation results. At the Zebrunner automation reporting, AI/ML functions as collective intelligence, trained by competent specialists through a voting mechanism. This helps identify bugs quickly and immediately determine who is responsible for each detected failure. Moreover, the Zebrunner AI/ML model streamlines work with automated tests by prioritizing potential bugs. The technology provides results based on qualitative indicators rather than quantitative ones. In the results of the automated tests, you can see the status of each test as well as the most likely reason for any failures.

10 "then" and "now" in test automation concerning AI

There are two possible ways to apply AI in your project, which can also be used for test automation.

The first is by expanding a custom model that needs to be trained on your data. This requires the model to memorize specific context and respond to user prompts accurately.

The second approach is using general, cloud-based, pre-trained models like ChatGPT. In this case, you provide context with each prompt, but the AI isn't specifically trained on your data.

Below is a detailed look at how AI in test automation works.

Traditionally, these tasks were labor-intensive, requiring significant effort from automation QA engineers. However, with the advent of AI and machine learning technologies, these processes have become more efficient and automated.

Then

In the past, QA engineers meticulously designed test scripts based on application requirements, user stories, and use cases. This involved:

Requirement analysis. Engineers would analyze the application requirements and specifications to identify test scenarios.

Test scripts design. Based on the analysis, they would create detailed test scripts, ensuring coverage of all functional and non-functional requirements.

Execution and maintenance. Test scripts were executed, and any defects found were reported. 

Optimization. Identifying redundant or flaky test scripts required inspection and analysis, often leading to bloated and inefficient test suites.

Now

With the integration of AI in test automation, the processes of test script generation and optimization have undergone a transformative change. AI-powered tools now automate these tasks, significantly reducing the manual effort required and increasing the efficiency and accuracy of testing.

AI assists in automatic test script creation. It can analyze application requirements and user stories to generate relevant test scripts automatically. ML algorithms learn from existing test scripts and user interactions to create new ones that cover more scenarios.

Moreover, AI optimizes the test suite by identifying and removing redundant or obsolete test scripts, ensuring that the test suite remains lean and efficient.

Even minor changes in the application’s UI could break the scripts. So, maintaining test scripts is a time-consuming process. 

Then

In the past, test script maintenance required extensive manual effort. QA engineers had to manually update test scripts whenever there were changes in the application’s UI or functionality. This involved:

Identifying changes. Engineers needed to manually identify changes in the application's UI elements and workflows.

Script updating. Based on the identified changes, they would update the test scripts to reflect the new UI or functionality.

Error handling. Handling and fixing test script errors caused by UI changes was a repetitive and time-consuming task.

Continuous monitoring. Constant monitoring of the test scripts was necessary to ensure they remained functional with every new application update.

Now

AI-driven features such as self-healing scripts and dynamic locators have revolutionized test script maintenance.

AI can detect changes in the application’s UI and automatically update test scripts to accommodate those changes. AI algorithms monitor the application’s UI and detect changes in real-time. When changes are detected, the AI automatically updates the test scripts, ensuring they remain functional without manual intervention.

AI reduces maintenance effort required for keeping test scripts up-to-date, allowing QA teams to focus on more critical tasks.

Also, AI algorithms use machine learning to identify and adapt to changes in UI element locators. When the locators of UI elements change, AI dynamically adapts the test scripts to the new locators, preventing test failures.

Furthermore, AI ensures that automated tests are more robust and less prone to failures due to changes in the application. By reducing test failures caused by UI changes, dynamic locators enhance the stability and reliability of automated testing.

AI systems continuously learn and improve from previous maintenance tasks, making future script maintenance even more efficient. AI algorithms learn from past updates and changes, optimizing the maintenance process over time.

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With the advent of AI, test execution has become smarter, faster, and more strategic. 

Then

In the past, test execution was manual and linear, facing several challenges such as sequential execution, limited prioritization, and manual management. 

Now

AI-driven smart test execution represents a leap forward in test automation. By prioritizing test scripts based on risk, recent code changes, and historical data, and by enabling parallel execution, AI enhances the efficiency, speed, and effectiveness of the testing process. This ensures that critical defects are identified early, test coverage is maximized, and the overall quality of the software is improved, leading to faster and more reliable releases.

With AI-driven smart test execution, running test scripts is now more intelligent and dynamic thanks to:

Prioritization. AI prioritizes test scripts based on risk, code changes, and historical data, ensuring early detection of critical defects.

Parallel execution. AI manages parallel test execution across multiple environments, optimizing resources and speeding up the testing process.

Continuous improvement. AI continuously learns from test data to refine execution strategies and improve efficiency.

Enhanced test management. AI dynamically adjusts test plans in real-time, reduces manual intervention and allows QA teams to focus on strategic tasks.

Unit testing involves testing individual components of code, typically the smallest functional units, to verify that each part performs as expected. This practice is important for maintaining code quality and is a fundamental aspect of software development. By breaking down software into small, manageable units and writing corresponding tests for each, developers can catch errors early, ensure functionality, and adhere to best practices in coding. AI, together with test automation, significantly simplifies unit testing.

Then 

Unit testing involved manual effort from developers, who spent considerable time crafting and maintaining test suites. This process often took away from valuable time that could have been spent on actual application development.

Now

AI-driven tools can automatically generate comprehensive unit tests, reducing the burden on developers and allowing them to focus more on coding. These AI-based solutions analyze code structures and behaviors to produce efficient and effective tests, streamlining the testing process and improving overall software quality. Developers just need to swiftly configure the unit regression suite by simply adjusting and modifying tests as needed.

In medicine, the saying “An ounce of prevention is worth a pound of cure” is true. And it ideally applies to defects in the software development life cycle. It is widely recognized that the cost of addressing a defect increases significantly the later it is discovered in the process. Identifying and resolving issues during development is approximately 80-100 times cheaper and 50 times faster than addressing them after the software has been released to the market.

Then 

Before the emergence of AI as a trend in software testing, defect prediction and prevention relied heavily on manual efforts and rudimentary automation tools. Testers would analyze past defect data and manually prioritize tests based on their perceived risk. This process was often time-consuming and prone to human error. Moreover, the tools available for root cause analysis were limited, making it challenging to identify the underlying issues causing defects.

Now 

With the integration of AI into test automation, defect prediction and prevention have reached new heights of efficiency and accuracy. AI is transforming these processes through:

Predictive analytics. AI algorithms analyze vast amounts of historical test data to discern patterns and trends. By identifying potential defects early on, teams can take proactive measures to address them before they escalate into critical issues.

Root cause analysis. AI-powered tools excel in root cause analysis by leveraging advanced algorithms to scrutinize logs, stack traces, and test results. This enables developers to pinpoint the underlying causes of defects swiftly and accurately, leading to more efficient bug resolution and enhanced software stability.

As technology continues to evolve, particularly with the integration of AI, the methodologies and tools for test coverage analysis have undergone significant advancements, revolutionizing the quality assurance process.

Then

In the past, testers would manually create test scripts based on their understanding of the application's requirements and functionalities. However, this approach often resulted in incomplete coverage, as it was challenging to anticipate all possible scenarios and edge scripts. Additionally, without sophisticated tools for analysis, it was difficult to identify areas of the application that were under-tested or neglected.

Now 

With AI, test coverage analysis has become more precise. Here are some ways AI is enhancing test coverage analysis:

Gap analysis. AI-powered tools can analyze the test suite and compare it against the application's features and user behavior. By identifying gaps in test coverage, AI can suggest additional test scripts to ensure comprehensive testing of all critical functionalities.

Heat maps. AI algorithms generate heat maps that visually depict areas of the application that are frequently tested versus those that are under-tested or neglected. These heat maps provide valuable insights into the effectiveness of test coverage strategies, enabling testers to prioritize efforts and allocate resources efficiently.

Adaptive test strategies. AI-driven test coverage analysis tools continuously learn from test results and feedback to adapt and refine test strategies dynamically. This adaptive approach ensures that test coverage remains robust and relevant, even as the application evolves over time.

Test data management involves the strategic planning, creation, and upkeep of datasets utilized in testing to ensure they align correctly with respective test scripts and test cases, maintain appropriate formats, and are accessible when needed.

Test data comprises input values utilized during the testing phase of applications (such as software, web, mobile applications, APIs, etc.). These values simulate user interactions in real-world scenarios. Testers typically develop scripts to automatically and dynamically determine suitable input values, observing how the system responds to such data.

Then 

In the past, organizations typically relied on manual methods and basic tools to manage test data. Testers would often spend significant time and effort creating and curating datasets manually, which could lead to inconsistencies and inaccuracies. Moreover, data privacy concerns were challenging to address, as manual approaches to data masking were time-consuming and prone to errors.

Now

With the integration of AI into test data management, organizations benefit from advanced capabilities that enhance efficiency and data privacy compliance:

Synthetic data generation. AI-driven tools can generate synthetic test data that closely resembles real-world data based on patterns learned from existing datasets. This approach ensures that test scenarios have access to diverse and realistic datasets, enabling more comprehensive testing while protecting sensitive information.

Data masking. AI algorithms can automatically anonymize sensitive data within test datasets while preserving the integrity and usefulness of the data for testing purposes. This automated data masking process ensures compliance with data privacy regulations, such as GDPR and HIPAA, without sacrificing the quality of test data.

A test automation report is a detailed document that outlines the execution and outcomes of automated test cases. It presents metrics such as test coverage, pass/fail statuses, error logs, and performance indicators. This report provides insight into the software's status, identifies areas for enhancement, and supports informed decision-making to optimize testing strategies and improve software quality.

An interesting statistic: despite the diversity of test automation reporting tools, some of which have AI/ML capabilities, test failure analysis remains a time-consuming process for 47% of respondents in the "State of Continuous Testing 2024" research.

Then 

In the past, reporting and analysis in software testing relied primarily on manual methods and basic tools. Testers would generate reports based on raw data collected during testing, which often lacked depth and context. Analyzing test results manually was time-consuming and prone to human error, making it challenging to identify trends or prioritize areas for improvement accurately.

Now 

With the integration of AI into reporting and analysis processes, software testing has entered a new era of efficiency and precision. Here are some ways AI is transforming reporting and analysis:

Advanced analytics: AI-powered tools offer sophisticated analytics capabilities, providing detailed insights into test results. By analyzing vast amounts of data, AI can identify trends, patterns, and anomalies, helping testers make more informed decisions and prioritize areas that require attention.

Visual reporting: AI enhances reporting by incorporating visual aids such as charts, graphs, and heat maps. These visual representations make it easier for stakeholders to interpret and understand complex test outcomes, enabling faster and more effective decision-making. Additionally, visual reporting enhances communication by presenting information in a more intuitive and digestible format.

In CI/CD testing, automated test suites run continuously, executing a series of tests with each code change to evaluate the application's integrity. This automation enhances testing speed and efficiency, enabling early detection of issues.

Then 

In the past, continuous testing in CI/CD pipelines often relied on manual intervention and basic automation tools. Test execution was typically triggered manually or scheduled at predetermined intervals, leading to delays in feedback and potential bottlenecks in the deployment process. Additionally, analyzing test results and identifying issues required manual effort, hindering the agility and responsiveness of development teams.

Now 

With the integration of AI into continuous testing practices, software development teams benefit from enhanced automation and real-time feedback mechanisms:

Integration with CI/CD pipelines: AI-driven test automation seamlessly integrates with CI/CD pipelines, automating the execution of tests with each code change. This ensures that testing is performed continuously throughout the development process, reducing the risk of introducing defects and streamlining the deployment pipeline.

Real-time feedback: AI provides instantaneous feedback on the quality of builds, enabling development teams to make informed decisions promptly. By analyzing test results in real-time, AI identifies issues early in the development cycle, empowering teams to address them proactively and maintain a high level of code quality. This rapid feedback loop accelerates the pace of development and improves overall software reliability.

UI testing involves verifying the functionality of an application's user interface. It encompasses validating logic, UI workflows, navigation, transitions, calculations, and ensuring the functionality of all buttons, among other elements.

Then 

In the past, testers would manually inspect the application's user interface to identify elements and verify functionality, a time-consuming and error-prone process. Automated UI testing tools lacked the sophistication to effectively navigate through the application's UI elements and verify their properties accurately.

Now 

This is an area where AI is beginning to shine. In AI-based UI testing, test automation tools leverage advanced algorithms to parse the Document Object Model and related code, extracting object properties with precision. Moreover, AI employs image recognition techniques to navigate through the application and visually verify UI objects and elements, enabling the creation of robust UI tests.

Additionally, AI test systems utilize exploratory testing methodologies to uncover bugs or variations in the application UI dynamically. They generate screenshots for later verification by a QA engineer, facilitating comprehensive UI testing. Furthermore, AI-powered tools can verify visual aspects such as layout, size, and color of the System Under Test with remarkable accuracy.

Also, automated UI testing driven by AI brings several benefits:

Increased code coverage. AI-driven UI testing enables thorough exploration of the application's UI, leading to increased code coverage and improved test effectiveness.

Robustness to minor UI deviations. Minor changes or variations in the UI do not cause the test suite to fail when powered by AI. AI models are equipped to handle such deviations intelligently, maintaining test stability and reliability.

Conclusions

While AI enthusiasts often claim that AI has already revolutionized the testing domain, the reality is that we haven't yet reached the level of true "smart" features in testing. For now, AI primarily serves to simplify routine tasks for testers.

However, ignoring AI’s potential is not a wise approach. There are many ways to apply AI in test automation – choose the most suitable one for your project and implement it. Zebrunner’s AI features, like issue clarification and test case creation, demonstrate how you can easily integrate AI into your testing process without significant effort.

If you’re ready to dive deeper and explore AI’s full potential, remember there are two main ways AI can enhance your project. The first is by training a custom model on your specific data, allowing it to understand context and respond accurately. The second is by using general, cloud-based, pre-trained models like ChatGPT, where context is provided with each prompt, but the AI isn’t trained on your specific data.