Benefits of AI in Software Testing

With more challenges in the process of software testing, artificial intelligence is offering aid in testing tools to increase the efficiency and effectiveness of testing.

FREMONT, CA:With more than two million mobile applications released in the previous year, the number of software developers is likely to increase exponentially in the coming years. To ensure that software releases perform adequately in terms of functionality, performance, and security, they should be tested rigorously.

However, testing is time-consuming, and throwing more testers at it is not the most efficient strategy. Artificial intelligence (AI) helps in the development of testing tools to increase the efficiency and effectiveness of testing.

Smart Test Selection

AI can determine the essential tests and remove the redundant ones that create noise because AI can collect and analyse information automatically and analyse the results of tests. For instance, a deep exploit is an AI-based penetration testing tool using reinforcement learning, meaning that as testers use it more, it becomes more accurate. Examples of this tool’s capabilities are intelligence gathering, threat modelling, exploitation, and reporting.

Machine Learning

Machine learning (ML) can benefit software testing automatically. This includes generating test cases, auditing the current tests for coverage, performance, and completeness, and running tests. An ML model should be primarily trained using the dataset of an organisation. The dataset should include codes, logs, test cases, and documents. After supplying the training data to the ML model, its accuracy should be measured to ensure that it works adequately. ML models need to be continuously updated using new data to improve their accuracy.

Computer Vision Bots

A machine vision bot can be used on labelled code elements to detect changes and apply the tests. Labelled code elements include a shopping cart, login box, and other common objects. Deep neural network techniques can help in training bots, allowing them to maintain the test even if the code changes and recognise the pieces.

Benefits of AI in Software Testing

Faster Test Execution

AI can reduce test execution time by nullifying or eliminating duplicate tests and improving test data management flow by ensuring high-quality data reaches test cases.

Better Test Maintenance

Software codes change a lot, and they should be updated again to ensure compatibility with the new source code. This causes a significant increase in the time and cost of test maintenance. However, AI can decrease the effort and time of test maintenance using computer vision bots and smart test selection.

Increase Test Coverage

AI can examine the conducted exploratory tests to build new tests to improve the test coverage. Coupled with software testing metrics, it identifies the probability and severity of breaks in different parts of the software.

Test Data Generation

Machine learning creates test data similar to production data, such as a profile picture, age, and weight. The test data helps in training ML models for testing or directly testing the software. These benefits indicate that using AI in testing tools eliminates the challenges experienced in traditional procedures.