Artificial Intelligence in Software Testing: An Overview of Tools

During software development, errors in the code, defects that disrupt the usual operation of the application often occur. Software testing allows you to detect discrepancies with the expected results in the system's behavior, thanks to which resource users rarely encounter so-called bugs. However, errors on the part of testing are not excluded due to the inattention or insufficient training of the tester. In this case, auxiliary tools based on artificial intelligence (AI) come to the rescue, which have been rapidly developing in recent years. In this article, we will highlight certain pros and cons of some of them and try to find the best and most demanded for software testing. AI allows a computer program to learn, reason, and act independently. In other words, it is the ability of a device to function similarly to human intelligence and perform tasks that people could perform. AI improves our thinking and helps us in everyday tasks.

In this article, AI tools implemented based on neural networks will be considered. A neural network is a mathematical model consisting of nodes (neurons) and variable connections between them (neural connection weights). Neurons use mathematical functions to decide whether to "fire" and send information to another level of neurons. The architecture of a neural network is similar to the human brain, where neurons generate impulses and transmit information through connections between different neurons. Neural networks can be used to solve complex problems: from generating data and even images to finding objects in an image.

In fact, a neural network is a special way of defining a function. To work with it, the following work needs to be done:

• Train the network. This process is quite simple. First, a training set is created, consisting of pairs of input and output vectors for which the correct values are known in advance. The elements of this set have the property of independence, which allows the set to be expanded and the order of its elements to be changed without loss of quality. Then the training set passes through the neural network, as a result of which the weights of the neural connections are adjusted so that the network can perform the function corresponding to this set. Obviously, the efficiency of the neural network directly depends on the quality of its training.

• Check the training result. The test set is formed similarly to the training set, but using other values. If the test results are unsatisfactory, it is recommended to improve the network training process or change its architecture.

A well-trained neural network is able to predict the result based on a set of input values. Along with this, it additionally solves the classification problem if the classes in the input set are predefined, or the clustering problem, that is, the identification of classes in the set.

Let's return to software testing. A neural network in the context of testing can effectively solve both sub-tasks thanks to the mechanism of predicting results. However, there are still several technical issues:

• The volume of generated test coverage for unit testing: in the simplest case, it is necessary to create all possible combinations of input values.

• The degree of randomness and methods of its assignment for simulating user interaction: using a random set of actions is not an optimal approach to testing.

• The uniqueness of the neural network structure for each task: this creates obstacles for its widespread application.

Given the above, neural network-based technologies combined with common testing methods can significantly improve the quality control efficiency of complex software systems.

In the context of software testing, neural networks can be used for test automation, defect detection, log analysis, and many other tasks. The most effective neural networks include:

• Deep Neural Networks (DNN) – used for test automation and defect detection, learning from large amounts of data.

• Convolutional Neural Networks (CNN) – effective for image and graphical interface analysis, which is useful in testing visual components.

• Recurrent Neural Networks (RNN) – suitable for processing sequential data, such as logs, which helps in anomaly detection.

These models can significantly improve the quality and speed of testing.

Let's consider the most effective AI tools and try to choose the best ones for software testing.

Selenium

Selenium – represents an open-source software tool for testing websites and applications. It is mainly used for regression, functional testing, and performance testing.

Features:

• Portability: The tool can be used for testing both websites and application software on various operating systems such as Windows and Linux.

• Multilingual: Several programming languages are supported, including Ruby and Python, making Selenium accessible to a wide audience of developers.

• Ease of use: An intuitive interface and numerous libraries are offered, simplifying the process of writing tests.

Advantages:

• Open-source, allowing users to make changes and adapt the tool to their needs.

• Extension support: Selenium provides the ability to integrate with other tools and libraries to enhance functionality.

• Community: A large community of users and developers ensures access to extensive documentation and support.

• Cost: Selenium is a free tool, making it accessible to everyone. Users can download it without any costs, which is a significant advantage compared to commercial solutions.

• Demand: Selenium is one of the most in-demand AI-based tools for software testing.

Disadvantages:

• Limited iteration support: there are problems with handling iterations and conditional operations, which can complicate the creation of complex tests.

• Execution speed: the testing process can be slow, especially when running a large number of tests simultaneously.

• Lack of error handling methods: Selenium does not offer built-in methods for automatically handling errors that occur during testing.

LambdaTest

LambdaTest is a cloud platform for automated and manual testing that supports scalability and integration with various tools. It offers test orchestration and root cause analysis features, which help quickly resolve issues and improve test stability.

Features:

• Wide device coverage: LambdaTest provides access to more than 3000 browser and operating system combinations, allowing comprehensive testing of web applications on various devices.

• Real-time testing: users can monitor test actions in real-time, which helps quickly identify and fix issues.

• Automatic recovery: the platform includes automatic recovery features during testing, which enhances reliability.

• Parallel testing: LambdaTest supports the simultaneous execution of multiple tests, significantly reducing testing cycles and speeding up time-to-market.

Advantages:

• Wide platform support: LambdaTest offers a variety of application testing capabilities, making it versatile for different project needs.

• The platform seamlessly integrates with continuous integration/continuous deployment (CI/CD) tools, simplifying the testing process.

• Responsive technical support: the platform is known for its efficient and fast technical support service.

Disadvantages:

• Cost: prices can be significant for small teams or projects with a limited budget, which may limit accessibility.

• Performance issues: some users have reported occasional performance issues during test sessions.

• Reliability of results: there are instances where test results may not be entirely reliable, requiring additional verification.

Cucumber

Cucumber supports behavior-driven development (BDD) methodology. It allows development and testing teams to collaborate effectively using a readable language to describe requirements and tests.

Features:

• Multilingualism: the tool supports multiple programming languages, including Java, JavaScript, and Ruby, making it a versatile tool for various projects.

• Integration with other tools: Cucumber easily combines with various testing tools, such as Selenium for web application automation.

• Readability: tests are written in Gherkin language, which is understandable to both technical and non-technical team members, facilitating communication.

Advantages:

• Team involvement: Cucumber encourages behavior-driven development, allowing all stakeholders to participate in the development and testing process.

• Documentation: since tests are written in natural language, documentation becomes more accessible and understandable.

• Automation: the tool makes it easy to create and reuse test cases, reducing testing costs.

Disadvantages:

• Time and resource costs: setting up and maintaining Cucumber can require significant effort and resources.

• Debugging issues: Cucumber has some limitations in debugging tests, which can complicate the error detection process.

• Lack of versatility: this tool is not suitable for all types of testing; its effectiveness may decrease in certain scenarios.

• Cost: to use all the resources and tools available in Cucumber, it is necessary to unlock the maximum paid version.

Parasoft

Parasoft — a software automated testing tool that covers functional and unit testing. It ensures reliability, efficiency, and security of applications, and also offers test data management and static code analysis.

Features:

• Security and compliance: code is checked for security and compliance flaws.

• Testing accuracy: data processing to ensure accurate testing.

• IDE compatibility: easy integration with popular development environments.

Advantages:

• API functional testing: Parasoft is one of the best tools for this task.

• Efficient management system simplifies the testing process, allowing for quick and easy testing.

• Intuitive interface: the usage process is simple and clear.

Disadvantages:

• Limited UI testing capabilities: graphical interface testing capabilities are limited.

• Server management issues: sometimes there are difficulties in managing server components.

• Knowledge requirement: requires thorough knowledge of DTP, CTP, and other related components.

testRigor

testRigor is used for test automation, the tool uses generative AI to simplify the test creation process. It allows users to formulate test scenarios in plain English, making it accessible to both technical and non-technical specialists.

Features:

• Generative testing: testRigor automatically creates and executes tests based on application behavior, allowing for the identification of errors and suggesting fixes.

• Self-healing: the tool adapts test scenarios to changes in the user interface, minimizing the need for manual intervention.

• CI/CD integration: testRigor easily integrates into continuous integration and delivery pipelines, speeding up the development and testing process.

• Cross-platform testing: supports testing of web applications, mobile applications (both native and hybrid), and desktop applications.

Advantages:

• Simplification of the testing process: users can create tests without the need for programming, using simple commands in English.

• Reduced maintenance costs: automatic test updates when the application changes reduce maintenance costs.

• Increased test coverage: generating test cases based on application behavior analysis ensures more comprehensive coverage.

• Fast feedback: efficient test execution and quick adaptation to changes allow for faster feedback, which is critical for modern Agile processes.

Disadvantages:

• Limited support for certain types of testing: for example, testRigor is not suitable for testing games or complex graphical interfaces.

• Dependence on AI quality: the effectiveness of automatic test generation depends on AI algorithms, which can lead to errors if the algorithms do not handle certain scenarios.

• Need for user training: despite its ease of use, users may need time to master all the features and capabilities of the tool.

Roost.ai

To automate the software testing process, Roost.ai uses generative testing with language models such as GPT-4. It offers many features, as well as its own advantages and disadvantages.

Features:

• Generative testing: these technologies allow Roost.ai to create tests based on code analysis and its changes.

• Adaptation to changes: the tool automatically updates the library of unit tests when changes are made to the code, ensuring the relevance of the tests.

• Full test coverage: Roost.ai strives for full code coverage with tests, allowing errors to be detected at early stages of development.

Advantages:

• Automation: reducing manual labor through automatic creation and updating of tests allows developers to focus on more important tasks.

• Increased software quality: full test coverage helps improve software quality as more scenarios are checked automatically.

• Development speed: quick test updates in response to code changes speed up the development process and reduce time to market.

Disadvantages:

• Dependence on LLM: the effectiveness of Roost.ai largely depends on the quality and accuracy of the language model used. Errors in test generation can lead to missed bugs in the code.

• Configuration complexity: significant time may be required to set up and integrate the tool into existing development processes for effective use.

• Potential costs: implementing and using such technologies may require additional financial investments, especially for small companies.

This is how the integration of AI into software quality assurance processes opens up new horizons for improving the efficiency and reliability of testing. However, it is important to remember that despite all the advantages, the role of humans in the process remains critically important for quality control and final decision-making. The real power of AI lies in its ability to complement the work of specialists, not completely replace them. The tools listed above demonstrate how artificial intelligence can improve software quality, reduce testing time, and increase the efficiency of QA teams.