What are the Advantages of AI in Testing?

To get maximally accurate test results

Humans are considered the apex of creation. However, we are all imperfect, and even the best experts make mistakes from time to time. In these terms, AI is more reliable, and AI-embedded testing tools offer an opportunity to eliminate human factors and achieve accurate test results. The more data that is collected and fed into deep learning models for training, the greater the accuracy you can expect.

To extend test coverage to the maximum providing indeed comprehensive testing

One of the main principles of software testing states that exhaustive testing is impossible, as it requires a significant amount of time and resources. However, the aim of a QA team is to achieve as high test coverage as possible. Considering the great opportunities offered by AI testing, there is no doubt that with its active and proper use, it will indeed be possible to thoroughly examine all aspects of an application under test.

To reveal root causes of bugs in a timely manner

Every tester knows that it is not enough to detect a bug; they must also reproduce it and reveal its root causes. In reality, this is not always possible. Certain bugs may appear only under specific conditions that a human might not recognize immediately. AI, on the other hand, can conduct a thorough analysis and provide information on the root causes of bugs in the blink of an eye.

To cut down testing expenses

According to statistics, software testing expenses can account for 40-50% of a total project's cost. Given that software testing is an essential part of software development, it is impossible to cut corners on it. Therefore, all efforts are being made to optimize it and reduce its cost. With autonomous and fast software testing conducted through AI, there is a possibility to make QA services more budget-friendly.

To provide more efficient software testing

Since AI-powered testing tools can provide better accuracy and higher test coverage, it is not surprising that they will help considerably enhance software testing efficiency overall and deliver software products of extremely high quality in record-breaking time.

Decision-making

AI algorithms can check the current test statuses, identify recent code modifications, code coverage, and other metrics. With this information, AI-powered test solutions can efficiently select the appropriate tests to run. In this way, thorough data analysis enables AI to replace a QA engineer in some decision-making processes.

API Testing

AI can be actively used in API testing, as it can recognize patterns and interconnections between various API calls during UI exercises. Based on this analysis, an AI-powered testing tool can generate a set of API calls with corresponding parameters for testing. By thoroughly analyzing end-users' behavior, AI can provide appropriate patterns and inputs for API testing. Tests created in this manner can address more complicated cases, ensuring a high quality level of the API.

Unit Testing

Unit testing is a prime candidate for AI-enabled automation testing, as such testing does not involve interactions with other app components. AI can perform a static analysis to detect parts of the code that have not yet been covered by unit tests. With this information, AI-powered testing tools can generate the necessary unit tests with corresponding inputs. However, these tools must be adept at identifying code sections that require stubs and mocks for proper isolation. To achieve this, the tool must undergo comprehensive training, requiring a significant amount of time to enable the AI system to understand how unit tests should be created. Once the AI tool can competently handle mock and stub creation, it will help a development team save a considerable amount of effort and time.

UI Testing

AI has found active use in UI testing due to its capabilities in pattern and image recognition. These properties of AI are utilized in various automated visual validation tools that verify the UI by checking UI elements in terms of size, color, shape, and position on a page. Such tools examine elements at the pixel level and, therefore, can detect visual defects that human eyes might not catch.

Test Data Generation

Quite often, the efficiency of software testing depends on the quality of the testing data. Collecting real-world data is an expensive and time-intensive process. Moreover, in most cases, such data cannot be used for testing or training due to privacy requirements.

AI models employ deep neural networks and specific privacy logic to generate a large amount of synthetic data that is highly realistic and complies with global data protection standards (e.g., GDPR, CCPA).