The majority of the advancement in application testing has been from manual testing
to the incorporation of automation testing. The practice of automating test case generation is crucial for achieving comprehensive coverage of the software application and ensuring quality assurance. Organisations and developers emphasise moving to automated testing because it offers quicker and more efficient results than manual testing.
However, due to the changing application needs and the expanding number of devices, testers sometimes face challenges in traditional automation testing. Achieving acceptable test coverage within a short timeframe becomes increasingly challenging. This is where the need for advanced AI technologies arises. By leveraging AI test tools, testers can easily scale up testing from local environments and remove environment-related schedule delays. AI technologies offer better results and greatly improve the QA experience, and provide higher-quality software.
In this article, automating test case generation for comprehensive coverage will be discussed, including the benefits of automated test case generation, key approaches, along some strategies to maximise the test coverage. Let’s start with what automated test case generation is.
Automated test case generation
Before automation testing, developers and testers had to spend countless hours manually testing a web application, running hundreds of test case scenarios on all widely used browsers, pointing out issues, and attempting to identify the root cause. This was done to ensure that the application worked both inside and outside of the local development environment.
An end-to-end test system may require more time to complete its course depending on the size of the manual testing team. Automation testing enables developers and testers to finish tasks rapidly, making it possible to test applications quickly and effectively, which is essential for today’s development cycle to achieve higher quality.
AI-powered test case generation is a powerful software testing technique that changes the game in the software world. It is used to check the extent to which a software application is tested. It leverages a machine learning algorithm to automatically generate test cases for comprehensive coverage and ensure that the developers know all the features and functionalities of the applications are thoroughly tested.
Benefits of automated test case generation
Increases test coverage
Automatic test case generation significantly enhances test coverage by identifying and testing edge cases, handling complex testing scenarios, and reducing gaps in testing. It can accomplish thousands of test cases, edge cases, and ensure a more thorough assessment of the software. Manual testing might overlook some scenarios due to human limitations for oversight, but automation can systematically cover all possible test cases.
Increase efficiency and reduce cost
Automatic test case generation increases the efficiency of tests and reduces the cost by automating the repetitive and time-consuming tasks. It enables QA teams to focus on quality assurance and generate faster tests, providing more consistent software applications with comprehensive coverage.
Faster time to market
Automation testing enables testers to fulfil the demand of this competitive world by finishing tasks in considerably less time to achieve higher quality and a faster time to market. Automating the test case generation results in optimised resource allocation, detects bugs earlier, and enables human testers to focus on more complex and strategic testing activities. By accelerating the testing process, it delivers high-quality software faster, which increases revenue and organisations’ growth, enabling them to stay competitive and improve user satisfaction.
Enhance accuracy and reliability
Automated test case generation increases the accuracy and reliability of software testing. It finds defects and issues more accurately, reduces the risk of human error and deployment risks by ensuring that new code changes do not break existing functionality, and ensures testers have a comprehensive test coverage.
Key approaches to automated test case generation
Model-based testing- Model-based testing is also called complete testing. It automates the test case generation by creating models of the system under test using various modelling languages and techniques. These models represent the system’s behaviour, functionality, and interactions.
This approach analyses the behaviour of an application with well-defined states and transitions against the predefined values. If the application behaviour does not match the expected output, it will flag it as a violation and needs to be corrected before it is tested again.
Data-driven testing- Data-driven testing is an approach for automating a large number of test case generation with varied inputs. It uses external data sources to supply input data for test cases. It modifies the test data without affecting the test logic and test data. Data-driven testing helps developers and testers automate test case generation, improve testing efficiency, and increase test coverage.
Code-based test case generation- Code-based test generation is an approach that is generally used to determine the source code of the application. It automates and generates the test cases and identifies testable units, such as functions or methods. This is done by evaluating the code paths, logic, conditions, and requirements of applications, which ensures the code is thoroughly tested. This enables testers to improve test coverage and deliver high-quality applications.
AI-driven test generation – AI-driven test generation is another approach for automated test case generation. It leverages artificial intelligence and machine learning techniques to generate test cases, identify test scenarios, and predict potential defects.
Testers can leverage AI-driven solutions to scale up testing from local environments and remove environment-related schedule delays. One of the AI-driven testing platforms is LambdaTest, which provides a range of testing tools, including automated testing, manual testing, and accessibility testing. By integrating accessibility testing tools, testers and developers can improve the platform’s capabilities to test their web applications for accessibility issues.
LambdaTest is an AI-native test orchestration and execution platform. Its AI testing capabilities enable testers to automate test case generation and execution. This decreases the manual effort required for comprehensive coverage of test case generation. This AI-driven approach helps create robust test cases by learning from previous test executions.
It is a platform for testing cross-browser compatibility that enables testers to test at scale on an online cloud Selenium Grid of more than 3000 real devices, browsers, and operating system combinations. To help testers quickly start and run automated mobile testing, LambdaTest provides a wide range of extra functionalities. Its primary capabilities include geo-location testing across several regions, support for many browsers’ live browser testing, parallel testing, resolution display testing, automatic screenshot testing, and testing in multiple locations at once.
Furthermore, it may be successfully integrated with a wide range of desirable project management, CD/CI solutions, automation frameworks, and bug-logging tools. With such a flawless connection, the team can immediately determine and log issues that arise while testing, guaranteeing that the application functions as intended.
Challenges
- False positives and negatives- Due to the generation of false-positive and false-negative test cases in automating testing, the requirement of manual labour comes in the form of human intervention to analyse and validate the generated test cases.
- Limited context understanding- As automatic test case generation analyses the software itself, it faces difficulty in understanding the broader context, which can lead to the generation of irrelevant test cases.
- Maintenance- The automatic test case generation tool requires maintenance and updates to ensure that the software remains relevant and meets the testing needs.
Strategies to maximise test coverage through automated test case generation
Risk-based coverage
Risk-based coverage is a strategy to maximise test coverage by combining risk analysis with automated test case generation. It leverages automated testing tools to generate targeted test cases. It prioritises testing efforts on high-risk areas of the software application and focuses on critical components, functionalities, and features that pose the greatest risk to the organisation, users, or system.
This strategy allocates resources effectively, reduces waste, decreases the potential risk, and eliminates defects and failures based on the application’s logic to ensure comprehensive coverage and deliver high-quality software applications.
Integrating automated tests in CI/CD pipelines
Integration of automated tests into continuous integration /continuous deployment (CI/CD) pipelines results in increased test coverage. It gives quick feedback on code changes, identifies errors earlier, reduces risk, enhances collaboration between development and testing teams, and ensures high-quality application delivery.
This strategy ensures the consistency and reliability of automated tests throughout the software development lifecycle.
Use of machine learning
By consuming less time and resources for the creation, management, and execution of test cases, the use of machine learning in test automation for unit tests can help in maximizing the test coverage. It ensures thorough testing of code units and determines errors and defects earlier, and provides a more effective and efficient software application. Later in the life cycle of an application, writing and maintaining AI-based unit test scripts is also beneficial.
AI created test scripts
When there have been updates, alterations, or changes to the code, it might be challenging for testers to estimate how many tests are necessary. Artificial intelligence and machine learning-based automated testing systems can analyse whether an application needs multiple tests or not. By avoiding the execution of ineffective test cases, it enables teams to enhance the test coverage and save time and money. Also, it is practical to evaluate a system’s overall performance without repeating the test scripts.
Using AI-powered testing tools
It is the most common strategy to maximise the comprehensive coverage of automated tests. It leverages artificial Intelligence and machine learning algorithms to enhance and automate the testing process and provides intelligent testing solutions faster. With the increase in the efficiency, effectiveness, and accuracy of testing, it also reduces the manual effort and cost required to test applications. Its advanced features make the test creation, execution, and maintenance quite easy.
Regression testing with RPA
Regression testing can be automated using RPA (Robotic Process Automation) to maximise the test coverage. It looks over the screen, uses the application’s functions, locates, and gathers data. All tasks are automated, operated through web or mobile applications, and are only carried out by robots.
It can automate time-consuming, repetitive operations like test case execution and data entry, resulting in less time and resources for regression testing. Additionally, it enables codeless testing, boosts efficiency, and delivers accurate results. One can get correct findings faster by using AI in test automation. When AI handles the majority of the testing process, time is saved as a result.
Conclusion
In conclusion, automation testing has become a crucial component in the software development cycle. It offers several benefits that can significantly improve the ability to interact with an organisation’s logic, reduce the cost, increase the reliability, boost the quality of the software applications, and accelerate delivery.
Testers can utilise AI and ML to examine and enhance the automated testing process with little to no human involvement. It gives testers the ability to generate test cases automatically, improving accuracy and efficiency.
Also Read-Innovative Test Generation Techniques for Modern Applications