Tosca tester
Artificial intelligence – AI in software testing
In this article, we’ll take a look at what artificial intelligence (AI) is, how it works, its uses in today’s world, and most importantly, its significant benefits in software testing. We will talk about the current state of AI and its impact on society, as well as its future prospects.
AI – Artificial Intelligence
Nowadays, we are witnessing rapid technological advances and their impact on all aspects of our lives. One of the most fascinating and fastest growing areas is artificial intelligence – AI. AI has become a key factor for innovation in many sectors, from medicine and industry to research and everyday life. Its ability to analyse data, recognise patterns and learn from them opens up new possibilities and creates different perspectives.
AI artificial intelligence – definition
AI – Artificial Intelligence is a field of computer science and machine learning that deals with the creation and development of systems and algorithms that have the ability to simulate and mimic human intelligence. The goal of artificial intelligence is to create machines and software systems that can perform tasks that humans would normally perform, such as image recognition, language processing, decision making, learning and problem solving.
Artificial intelligence relies on the principles of machine learning, where machine algorithms and models are trained on large volume of data and examples to learn to identify patterns and make decisions without explicit programming. These systems can be designed to improve themselves and adapt to new situations and conditions.
How does artificial intelligence (AI) work?
AI works based on algorithms that are programmed to solve certain tasks. These algorithms are trained on a large volume of data called training data. The more training data is available, the more accurately the AI will solve the given task. AI is able to learn and improve its performance by interacting with its environment.
Software testing using AI
Software testing is an integral part of software application development. Ensures that the software works reliably, is error-free and meets the required specifications. Nowadays, as the complexity and volume of software applications grow rapidly, traditional testing methods often run into limitations. This is where Artificial Intelligence (AI) comes in as a powerful tool that brings new opportunities to optimise and automate testing processes.
Optimization of test scenarios and coverage: artificial intelligence can be used to analyze software code and data from previous tests, allowing critical areas to be identified and test scenarios to be prioritized. AI algorithms are able to analyse data, recognise patterns and detect potential problems. This improves the efficiency of the testing process and increases testing coverage.
Test data generation: the use of artificial intelligence allows the generation of diverse test data that simulates different inputs and scenarios. Generating test data based on machine learning algorithms enables better coverage of possible inputs and ensures that hidden bugs and software instabilities are uncovered.
Error detection and correction: artificial intelligence can be used to detect errors and problems in software. For example, AI systems can analyse logs and monitor software behaviour in real time to identify anomalies and predict potential bugs. This enables fast response and bug fixing, which contributes to a significant improvement in the quality of the software.
Test process automation: artificial intelligence is a key factor in test process automation. AI systems can be trained to automatically execute tests, find and fix bugs, generate correct test reports, and monitor software performance. In this way, it is possible to reduce the manual work of test teams and increase the efficiency and accuracy of testing. Test process automation also allows for repeated and systematic execution of tests, which significantly reduces the time-consuming testing cycle.
Predictive testing: artificial intelligence can be used to predict potential problems and bugs in software before they occur. By analysing historical data and patterns, critical points can be identified and preventive measures can be proposed to minimise the risks of errors occurring. Predictive testing enables developers and test teams to take proactive measures and ensure higher software quality.
Performance testing: artificial intelligence can simulate real user behaviour and test system performance under different load conditions. By generating realistic test data and running simulations, for example, AI can identify situation, when business requirements exceed developer capabilities, scalability issues and resource constraints, enabling proactive performance optimization.
Continuous testing and DevOps: AI can support continuous testing and integration through seamless integration with DevOps tools and workflows. It can provide real-time feedback on testing results, quality metrics and performance, enabling teams to deliver high-quality software at a rapid pace.
Intelligent test prioritization: artificial intelligence algorithms can analyze code changes, historical defect data, and test coverage to prioritize tests based on their impact and risk. This will ensure that critical functions and high-risk areas are thoroughly tested, saving time and effort by focusing on the most important tests.
Adaptive test maintenance: as software evolves, test scripts need to be updated to adapt to changes in the application. Artificial intelligence can analyze code changes and automatically modify test scripts, reducing the manual effort required for test maintenance and increasing the resilience of test automation.
Natural Language Processing (NLP): artificial intelligence NLP capabilities can be used to automate generation of test cases in natural language queries. By understanding and interpreting text requirements, AI algorithms can generate test scenarios and ensure proper test coverage, bridging the gap between business requirements and testing.
Exploratory testing assistance: AI can assist testers during exploratory testing sessions by providing insights, suggesting testing ideas, and identifying potential areas of interest. By analyzing user interactions in real time, AI can offer guidance and recommendations, increasing the effectiveness of exploratory testing.
Virtual test environments: AI-based virtualization technologies enable the creation of virtual test environments that replicate complex and diverse real-world scenarios. These virtual environments enable scalable and cost-effective testing of applications in a variety of network conditions, device configurations, and user contexts.
The collaboration between artificial intelligence and human experts creates a powerful combination that strengthens the ability to detect bugs and ensure high software quality. However, despite the benefits of AI in software testing, it’s still important to have experienced testing experts on the team who can provide context, expert guidance, and a final check of the software from the end-user’s perspective.
Popular AI tools for software testing
The success of an AI-based software testing tool can be determined by how much it simplifies the tester’s tasks thanks to things like :
- Features and functionality: it should support your automation requirements for application development and quality assurance.
- Value: The price should be based realistically based on the features, capabilities and use cases it provides. It should generate a faster return of investment by assisting in rapid product development.
- Usability: it should require optimal programming skills and should be easy to learn. The best tools have good technical support, user support, tutorials and courses.
- User Interface (UI): must be clean and intuitive.
- Integrations: the best tools can be easily linked to other tools and plugins. Look for tools with pre-built integrations that integrate with bug tracking tools (e.g. JIRA) and support different operating systems and browsers (e.g. Internet Explorer, Google Chrome, Safari, Firefox).
- Documentation: they must have a clear and reliable documentation process with easily retrievable and exportable test results.
- Scripting languages: the tool should support a variety of popular scripting languages or specialize and go fairly deep in one scripting language.
- Version control: software testing must be performed on multiple files at the same time. The tool should store and display a history of the data so that local changes can be reverted to if necessary.
- Test commands: The tool should support multiple test commands and allow users to add, modify and test commands as needed.
- Test scenarios: the software should support a variety of test scenarios, including performance testing.
The technology space is flooded with a variety of different AI-based quality assurance tools – all with their own unique features. Here’s a brief overview of the four most popular AI tools used in software quality assurance.
TestCraft
TestCraft is an AI-based test automation platform for regression and continuous testing using Selenium. It is also used to monitor web applications. QA testers can visually create automated Selenium-based tests using a drag and drop interface and run them in multiple browsers and work environments simultaneously. This test model makes it easy to reuse test steps and apply changes as needed.
Any change to a test step is automatically applied to all other scenarios that use it, thus significantly reducing test maintenance time. It also allows the platform to overcome changes, such as an enlarged registration button or a form that has moved to a different location, and prevent unnecessary test interruptions. This helps to significantly reduce test maintenance time.
Testim
Testim is an AI-based software testing platform that allows users to create robust end-to-end tests that are either coded, not coded or both.
The success and popularity of Testim lies in its original cyclic model. Smart locators allow multiple users/teams to work on a single application by allowing users to select and change a specific item. So if an element changes but still works, Smart Locators will find it and prevent the test from failing.
Testim also includes dynamic locators that use multiple attributes of an element to locate it on the page. In this way, an element can be successfully located even if one attribute is changed, using the other attributes that the AI has already extracted from the DOM. Steps can be grouped and reused. It is possible to define the configuration of a step or group of steps to be run (or skipped) if certain conditions are met (or not met).
Aplitools
Applitools is the best tool for applications involving visual testing. It looks for visual errors in applications and ensures that no visual elements overlap, nor they are invisible or off-page, or that no new unexpected elements appear. Traditional functional tests cannot perform these tasks.
Applitools Eyes scans app screens and analyzes them like a human, but with the power of a machine. It captures essential differences and identifies them or ignores those that are not different. This is especially helpful in the areas of DevOps, digital transformation, manual quality control, engineering, and more. Its Visual AI technology can automatically detect visual differences and user interface inconsistencies across platforms, browsers and screen sizes, enabling effective visual testing of web and mobile applications.
Functionize
Functionize is a comprehensive test creation, execution, maintenance and analysis tool with features for functional, visual, performance and stress testing. Machine learning and artificial intelligence accelerate test creation, diagnosis and maintenance. Once the tests are created, ML models update them as the application changes, saving prolonged updating/maintaining scripts for minor UI or style changes.
The tool is fast, efficient and easy to use. It runs thousands of tests from a mobile or desktop browser in minutes. Using keywords such as ‘open URL’, ‘insert text’, ‘verify’, etc., the tool uses “automated test creation techniques” and manually created tests to identify inputs. CV-based visual testing is facilitated using convolutional neural networks (CNNs) and analytics to report relevant changes. It provides autonomous test execution, self-correcting tests and advanced analytics for efficient test management.
These tools demonstrate how artificial intelligence (AI) is being applied to automate various aspects of software testing, improving test coverage and increasing testing efficiency. It is important to assess each tool against the specific testing requirements and consider their compatibility with the software development environment.
Artificial intelligence and programming
One of the new technologies in this ‘AI in programming’ area is GitHub Copilot. It’s an AI-based software development tool from OpenAI, which also owns the web app we’re all familiar with – Chatgpt. GitHub Copilot can automatically suggest additional code based on the code you write. To do this, it uses large volume of training data and neural networks, which are able to learn to code in a similar way to a human. Read more in the article: Artificial Intelligence and Programming – Github Copilot.
When programming with use of artificial intelligence, it is important to have an understanding of machine learning algorithms, neural networks, decision trees and other techniques, as well as an understanding of the principles of data processing and analysis. The ability to design and implement models, perform training on data and evaluate their performance is required. In addition, AI programming also involves working with libraries and frameworks.
