Automation testing is critical in software development to provide high-quality applications. It can be an effective approach to accelerate the release of applications. However, test automation will also prove to be complicated due to technological advancements and the complexity of modern applications. Testers still have difficulties in developing test cases, writing test scripts, and successfully implementing test automation.
The advancement of modern AI technologies, such as machine learning and natural language processing, has transformed the field of automation testing. It allows for more advanced capabilities, such as the creation of test cases and data in natural language, advanced analysis of test results, and more. As the leader of this change, ChatGPT recently made its way into the automated testing industry. It has pro-human abilities, including the ability to speak and teach how to build test cases and scripts.
With ChatGPT for test automation, testers can understand technical ideas, obtain answers to any questions, and complete complex activities like creating test cases. They can even comprehend and interpret natural language, converse with any user in natural terms, and generate unique answers on every topic.
In this article, we will cover the basic idea about ChatGPT and its transformation in test automation, along with the advantages. We will also cover effective strategies to consider while using prompts in automation testing. Top ChatGPT prompts will also be covered below for a smooth and effective test automation.
Understanding ChatGPT: A transformative approach in test automation
OpenAI created ChatGPT, which stands for Generative Pre-trained Transformer. It is a cutting-edge artificial intelligence model that assesses and understands natural language using deep learning methods. Based on transformer architecture, it responds to input text like that of an AI chatbot.
It is a useful tool in test automation for creating test cases and other difficult tasks due to its capacity to analyse and understand natural language. Chat GPT is used extensively for automated software testing, which has improved its accuracy, cost-effectiveness, and efficiency. A crucial component of the software development life cycle, automation testing enables errors and problems to be checked with less human involvement.
The strongest point about it is its capability to code in many programming languages. ChatGPT, however, can do more than just write code. It can define positive and negative test cases regardless of the complexity of the application, develop complicated test automation testing pipelines utilising CI/CD, and create automation test cases for any test environment.
Chat GPT has improved the reliability and intelligence of automation testing. With ChatGPT, the automated test takes less time and effort to complete, and the possibility of error has decreased. With improved test result analysis, testers may also detect potential errors and flaws in applications.
Advantages of Using ChatGPT Prompts in Automation Testing
Test Case Generation
Based on the user stories, ChatGPT assists the testers in developing test cases and test scripts. Testers can create a test case that incorporates a variety of test scenarios and edge scenarios with ease. It provides human-like responses by analysing the natural language input. As a result, even stakeholders who are not highly skilled can take part in the testing process because it does not require programming knowledge.
Analysis of Test Results
One of the main responsibilities of automated testing is analysing test results. It entails test results analysis post-test case execution to identify errors and improve application quality. ChatGPT makes it easy to complete such a task. It can automatically evaluate test results and detect specific patterns in the data.
Report software errors
One of the most important tasks in the automation testing process is reporting problems or bugs discovered. This entails locating, documenting, allocating, and monitoring the error. Testers can report error-related information using ChatGPT by using its natural language. Additionally, it enables testers to look for trends in the test data and get ideas for quality applications.
Machine learning
One of the difficulties that testers encounter during automation testing is identifying the aspects of applications that are prone to errors and affect their functionality. Testers can effectively handle this problem by using ChatGPT. To determine which part of a software is more likely to have errors, bugs, or flaws, ChatGPT uses its machine learning skills.
Make SQL Request
ChatGPT can assist testers in creating SQL queries for getting relevant information from the databases in the context of automation testing. This is accomplished by exploiting ChatGPT’s natural language processing capabilities. It allows testers to ensure the accuracy and consistency of automation tests by generating SQL that matches the software requirement specification.
Low-Code Testing
Low-code development has made it easier to create applications. Low-code testing allows testers with limited or no programming abilities to develop software. ChatGPT can be used as a low-code testing tool because of its natural language processing structure, usage of the Page Object Model to change code, and other characteristics. It is now simple to alter and fix code using ChatGPT.
Performs exploratory testing.
ChatGPT enables testers to effectively perform exploratory testing by transforming automation testing. Due to its ability to understand and establish human-like responses, ChatGPT simulates a real-world user and interacts with the application being tested in a human-like approach. This enables testers to quickly recognise errors or bugs that could have been challenging to detect in a more traditional way of testing.
Top ChatGPT Prompts for Test Automation
- Create Test Cases for a Login Page
Prompt: “Create relevant test cases for a login form that includes email, password, and forgot password options.” Instead of developing test cases manually, this prompt allows testers to draft them rapidly.
- Detect odd scenarios for Entry Fields
Prompt: “List odd scenarios for a form input field that accepts mobile numbers.” Unusual scenarios are frequently overlooked while planning tests manually. This prompt guarantees that unexpected or unusual conditions are included in tests.
- Write Simple Automation Code
Prompt: Write a Python Selenium WebDriver script that can test a sign-up form. Automation testers can employ this prompt to create code suggestions in a variety of programming languages. It reduces the time when creating scripts that are repeated.
- Provide Performance Test Ideas
Prompt: Provide performance test ideas for a website’s checkout system. Performance testing is critical for understanding how users view a website. This prompt includes scenarios like load testing, stress testing, and testing with several users at the same time, allowing for more effective planning.
- Create Test Data Examples.
Prompt: “Create realistic test data on a user profile page, such as name, email, birthdate, and address. It may be time-consuming to collect sample data manually. This prompt allows testers to gather data quickly for testing input rules and confirming how the contents display on the screen.
- Simply explain the bug log
Prompt: “Explain this problem report simply: ‘Screen freezes while hitting checkout after entering discount code.'” QA teams are sometimes required to explain bugs to non-technical individuals. This prompt makes bug explanations more understandable and shareable.
- Suggest Regression Test Situations
Prompt: “Suggest regression test scenarios after changing a website’s search feature.” After each big update, regression testing is critical. This prompt suggests areas that need testing to avoid unexpected bugs in other components.
- Check for insufficient test coverage
Prompts: “Review the test cases and provide if anything is unavailable. Ensuring thorough test coverage can be difficult. This prompt instructs ChatGPT to analyse the test set and propose any missing paths or conditions.
- Make a QA Test Plan Layout
Prompt: “Create a sample QA test strategy architecture for a mobile application”. For new projects or teams, this prompt provides a short sketch of a test strategy. It specifies test objectives, scope, tools, responsibilities, and testing times.
- Identify Potential Bugs in Feature Explanations:
Prompt: “List bugs that may appear in a feature that allows testers to execute updates on social media sites.” Teams can design more dependable applications by detecting bugs early, before writing code. This prompt helps with early testing.
- Generate Test Cases
Prompt: Build test cases for the feature/functionality of a web application, considering positive, negative, and edge circumstances.
AI-generated test cases provide a good starting point, but real-world testing is necessary for accuracy. Real-world testing ensures accuracy, validates test coverage, handles unexpected UI behaviour, and ensures cross-browser and cross-device compatibility. Running such testing on a cloud-based platform is preferable for improved scalability, security, and dependability. Cloud-based testing platforms, such as LambdaTest, allow automated testing for web and mobile applications across multiple devices, browsers and operating system combinations.
LambdaTest is an AI testing tool to run manual and automated tests at scale. The platform enables real-time and automated testing on more than 3000 environments and real mobile devices. LambdaTest primarily leverages ChatGPT prompts via its AI-powered agent, KaneAI, and a set of AI testing tools. These tools enable quality assurance (QA) teams to communicate with generative AI with natural language prompts, automating and improving their testing activities. These prompts can help ChatGPT generate precise and structured bug reports that include techniques that replicate intended behaviour and actual behaviour, hence improving collaboration within development teams.
Moreover, LambdaTest uses ChatGPT as a generative AI assistant, allowing testers to automate routine tasks and build test artifacts. This integration focuses on using ChatGPT to create intelligent tests, with LambdaTest including cloud-based grids and Test Manager, managing test orchestration, execution, and analysis. One of the primary advantages of using LambdaTest in generative AI testing is that testers may use the features provided by ChatGPT to develop effective test cases that can be run at scale in the cloud.
Strategies for Using Prompt with ChatGPT in Test Automation
Express the problem clearly and concisely
Before accessing OpenAI, testers should ensure that they fully understand the problem they are trying to solve. By breaking the issue into more manageable parts and specifying the desired results or functionality, they must ensure to express it as simply as possible.
Refrain from entering confidential data
Since OpenAI uses the inputs and outputs to keep training and improving ChatGPT, any data testers entering into the application are no longer in control of it. Never enter confidential information into ChatGPT. Testers might want to monitor what information they are willing to share, even if they do not work with confidential data.
Iterative Refinement and Feedback Loops
Prompts are rarely “write-once” tasks. Testers should go over AI-generated output, determine where prompts need more context or description, and iteratively improve their language. Over time, this feedback loop creates more accurate, domain-specific prompt libraries, increasing productivity and test quality.
Data-Driven and Structured Output
Providing specific details or asking for certain output formats (e.g., JSON or tables) helps match outcomes with automation tool requirements. Testers must define if they require outputs in Gherkin, markdown tables, or step-by-step instructions to optimize integration and ensure usability.
Versioning and Collaboration
Save and version the most effective prompts. Share them in a common repository so that team members can reuse and adjust established patterns. Integrated tools and prompt management platforms assist QA teams in optimising their procedures as new test coverage requirements emerge.
Conclusion
The evolving nature of automation testing offers new insight into the concept of application testing. ChatGPT will be a revolution in automation testing, enhancing effectiveness and efficiency. Its machine learning and natural processing language make it simple to create test cases and scripts that revolutionise the automated testing process. ChatGPT is a useful tool for QA professionals when given the right prompts. They contribute to time-saving, early error identification, and high-quality software. With the growth of testers working with AI, such tools as ChatGPT will become even more widespread in test automation.
