Artificial intelligence(AI) and machine learning(ML) are currently the most revolutionary technologies and are believed to transform aspects of various industries and services shortly too. On the other hand, software testing industries started adopting automated testing agendas with the help of frameworks such as Selenium, Appium, Playwright, and many more. as they consider it to be an uprising and elevating their outputs predominantly. What would be the effect if these two revolutionizing technologies were mended together? Can AI and ML come together to leg up the efficacy of automation testing?
Yes. This article chiefly contains insights into the numerous ways in which the power of AI and ML can be harnessed in automation testing and how advantageous this amalgamation can be.
Comprehending Core Terms
Let us now dive into the underlying concepts in the automation testing field equipped with Artificial Intelligence and Machine Learning.
Understanding AI and ML– Artificial intelligence involves multiple computer programs supplied with human intelligence to mimic human behavior. Its abilities range from learning, interpreting, and planning to speech recognition, problem-solving, computer vision, and higher-order tasks.
Machine learning, on the other hand, is a subset of AI that aims to design and develop algorithms and statistical models that can set up an environment where tasks are performed without manual interruption.
Understanding automation testing- Before presenting any software application to the users, the developers put it through a series of tests that evaluate its reliability, consistency, and quality. Automation testing is one such technique in which the tests are automated with minimum to zero manual interruption. This technique is believed to reduce the process’s verbosity when performed manually.
Ways To Embed AI And ML Into Automation Testing
Here are some possible ways to harness the power of Artificial intelligence and machine learning in automation testing:
Effective test case generation and maintenance
- In traditional testing techniques, the testers must write the test cases, which is often a prolonged task. In automation testing, it can be automated with the help of AI and ML technologies. Machine learning models must be generously trained to create appropriate test cases for the testing process.
The training of ML models includes feeding them with application-appropriate and organizational datasets, test cases, logs, ML algorithms, and application interfaces. The models must be introduced to natural language processing (NLP) techniques to understand the natural language documentation and their requirements. This helps in knowing the functionality of the software and the generation of test cases in compliance with it.
- The creation of test data used to train the ML model can also be generated using separate ML models to train the ML model. Some AI-assisted automation testing tools also support generating dynamic test data covering various input combinations and edge cases.
- The AI-based test case generation tends to cope with the constant changes and updates in the application by intelligently accommodating its test cases with the newly updated changes.
Detection of defects using ML
Machine learning models can analyze data and find problematic defect-prone areas. Training the ML model to detect defects includes various combinations of historical data and machine learning algorithms. The ML model should be able to identify any troublesome attributes like historical defect density, module dependencies, insufficiency in the input data, and other factors that may contribute to any defect. It would be highly appreciated if the ML model could analyze the trends in past testing data, their patterns, and their relationships to predict any possible defect-prone area.
Visual testing assistance
One of the highlights of automation testing is visual testing techniques. Here, machine learning models verify the visual elements of an application’s user interface. Here are some of its core features.
- ML models set up a baseline for the desired appearance of the UI. During the training phase, they are provided with the actual right screenshots of the program, and in later assessments, they compare fresh screenshots to this baseline. The comparison of the newly generated screenshots against the baseline leads to the identification of various visual discrepancies that include missing elements or attributes like images or buttons, color variations or layout shiftings.
- Applications with dynamic content or responsive design can adapt to automation testing tools that support dynamic element recognition. Machine learning models can identify and recognize UI elements even if their positions are changed. In case of any changes in the UI, they can be retrained to adjust to the application’s new UI design.
- ML checks out the accessibility of the UI elements. This involves testing the UI elements’ readability and operability and degree of adherence to web standards and guidelines. They also confirm if the UI elements can accommodate all the potential users in various scenarios. ML models can be infused with fail-safe mechanisms by training them on advanced algorithms to distinguish between intentional design changes and actual defects, leading to accurate test results.
- ML-based testing techniques are great add-ons to cross-browser testing. The ML models can be trained on many browsers and device configurations for effective and comprehensive cross-browser automation testing.
You can always leverage cloud-based tools like LambdaTest to effective automate cross-browser testing at scale. LambdaTest is an AI-powered test orchestration and execution platform to run manual and automated tests at scale. The platform allows you to perform both real-time and automation testing across 3000+ environments and real mobile devices.
LambdaTest leverages the power of Artificial Intelligence (AI) and Machine Learning (ML) to enhance automation testing capabilities, providing users with intelligent insights and efficient testing processes. Integrating AI and ML technologies within the LambdaTest platform improves test efficiency, quicker issue identification, and enhanced testing accuracy.
Performance testing
- The application’s performance is tested against real-world user behavior scenarios stimulated by AI and ML models. AI dynamically creates test scenarios based on various usage patterns and their evolution. It also introduces ways to check test scenarios against diverse user conditions, including device types, geographic locations, and network conditions. This dynamic approach to performance testing helps reduce any disparities that the arousal of different unfamiliar conditions may cause.
- AI and models can be trained to predict the behavior of the application under different real-world conditions, the probability of potential issues, and proactive measures to address them. This can be called the AI’s ability to do predictive analysis.
- Many recommendations related to the optimization of the application’s performance that depend on the data collected during testing are given by AI models. They suggest ways for code optimization, infrastructure adjustment ideas, database tuning plans, and many such strategies to enhance the efficacy of the system’s performance.
Coping with massive data
- Along with the test case generations that were discussed previously, AI and ML models can also perform data masking and anonymization.
Data anonymization is the process of eliminating personal or sensitive data from the presented data. In contrast, data masking is modifying or creating fake data that resembles the original data. This is done to maintain the confidentiality of the real data while providing a functional alternative that is operable by software systems and other authorized parties where real data is not necessary.
Data anonymization and masking are applied to the massive amounts of data fed into ML models to ensure adherence to data privacy regulations and guidelines. This approach enables the AI to preserve the sensitive structure and relationships within the data set.
- AI and ML models also have a knack for knowing and predicting the data set on which the upcoming tests are dependent. They preload the data into the test environment, reducing the time spent on data loading during test execution.
- The picture-type data can be altered to check the application’s behavior in different scenarios. The image alterations include changing the lighting, scale, and color. This is usually done using ML and AI models under the guise of “data augmentation.”.
Miscellaneous features
Apart from the benefits already mentioned, there are a few more reasons why AI and ML need to support automation testing. They are as follows.
- Low time consumption- As AI models and ML algorithms automate many laborious and time-consuming tasks like test case and test data generation, test case prioritization, log analysis, anomaly detection, intelligent parallel execution of tests, and many such workloads, the total time consumed becomes comparatively less, and the total overhead expenses are also reduced.
- Feedback loops- AI models can be employed with feedback loops to understand the application’s health, get feedback from the test results, and improve the quality and relevance of the test data.
- High resource utilization- AI enables environment-specific allocation of tests, considering various parameters like current workload, availability of resources, and more. It also balances the load on different servers and testing environments by carefully distributing it. These approaches ensure the proper and optimal use of testing infrastructures.
- Capacity and scalability calculation- Machine learning algorithms can intelligently calculate the capacity limits and scalability of the application under the testing process. It can predict future workload patterns and give suggestions on resource provisioning, dynamic scaling, and more. The software developers also get a chance to understand the capacity and scalability of their applications and can work on ways to elevate them.
- Execution of parallel tests- The manual testers may sometimes miss out on the fact that some tests depend on each other for data, and they run parallelly, which stops the testing execution midway, causing an error. But, by using AI, the tests that are independent of each other for data can be predicted. In this way, errors can be reduced, and the execution of tests can be performed quickly.
Summing it up
Though AI and ML models are said to elevate the outputs of automation testing techniques, they come up with their own set of challenges.
Machine learning models are sometimes not input with high-quality and relevant training data. This ends up producing undesirable outcomes. Training the AI models to generate only fair results is crucial to eradicating bias. Once the ML models make the decisions, sometimes it becomes tough. The engineers dealing with the whole process are expected to bring out an AI-ML model that is ethical, reliable, and scalable.
This article has focused on the major areas where artificial intelligence and machine learning can be combined with automation testing for accurate test results, increased speed, and an effective testing process. This would always be a good idea for an organization, as it ultimately leads to higher software quality and developmental results.
