Test automation is an important task in software development required for the rapid execution of test cases to check reproducibility, consistency and early error detection. One of the commonly used and handy AI, ChatGPT, is a language model developed by OpenAI. It has numerous functionalities and helps efficiently perform tasks like test automation. ChatGPT can be integrated into the test automation toolkit for data validation, streamlining test case generation and simplifying complex testing scenarios.
Understanding ChatGPT
For a better output from ChatGPT for automation testing, it is important to begin by understanding its function mechanism. It is divided into three parts:
- Training: It is trained through numerous resources where it learns language rules, patterns and context.
- Language understanding: It analyses the prompt and understands the context behind the input.
- Text generation: The text response is generated based on understanding from the previous step.
Benefits of Chat GPT for Test Automation
The AI model serves multiple benefits in test automation, such as:
- Rapidly generates test cases
- Generates ideas and assumptions for queries
- Improves test coverage
- Efficient for generalized testing
- Provides standardized test case formats
- Saves time and cost
- Enhance productivity
- Integrability with automation frameworks
Examples of Using ChatGPT for Testing Automation
ChatGPT for automation testing can be done in the following multiple scenarios, among numerous others:
Writing Test Scripts with ChatGPT
ChatGPT can generate test scripts while independently assuming different scenarios for ChatGPT test automation. The complete operation of the same was evident when we entered the test query “create a test script with 2 assumed methods to open a school website and download the brochure”. ChatGPT assumed two methods, one to directly download the brochure through the available button while another to navigate through the site and reach the desired location to download it.
Generating Test Data
ChatGPT test automation involves generating test data. Here we generated test data with the prompt “generate test data of 10 patients in a tabulated manner for researching the gut microbiome of patients with comorbid conditions like diabetes, CVD. Ensure considering all the relevant parameters”. ChatGPT generated the test data considering multiple directly associated parameters. Further, it also informed us about the random and unreal generation of data and the importance of taking real data.
Using ChatGPT to Change and Fix Code
Here we assume a simulation to indicate cytoskeleton (biologically cellular structure) movement. This structure is responsible for the movement of cells, such as immune cells that fight off infection in our body. The prompt to fix the code was, “I have a Python function to simulate the cytoskeleton movement. But the simulation seems to be unable to portray actin and myosin filament accurately. How to fix the code for this?” ChatGPT test automation fixed the error by introducing random movement and time steps and improving the interaction between the two.
Design Sample Test Cases
The blood groups of humans exhibit multiple alleles, meaning the blood group can have different genotypes. We consider this case to predict the blood group of a child. The prompt for ChatGPT for test automation is “Test cases for testing the probability of the presence of a specific blood group in a child. Consider a real-world scenario where the blood group can have two genotypes”.
Format Data
DNA sequences are stored in various formats. Our query was based on FASTA and GenBank format. The former provides only the DNA sequence, while the latter is associated with multiple other information. In ChatGPT test automation for data formatting, the prompt was “Write a program in Python and R to convert the DNA and protein sequences from FASTA format to GenBank format simultaneously.” ChatGPT provides a separate program for each programming language for data/sequence manipulation using specific libraries.
Test Result Analysis
Mutations are changes in genetic sequences. Concerning their immense importance, we request ChatGPT for automation testing to compare the mutation frequency. Here is the prompt for the same “Analyze the results of two random DNA sequences with different mutation rates. State the frequency of different types of mutations in it”.
Challenges and Limitations of Using ChatGPT for Test Automation
Though ChatGPT for test automation is effortless, certain limitations are associated with it. Here are the significant ones enlisted:
- ChatGPT has encountered loads of data in training. However, it lacks expertise in any specific domain. It leads to a lack of accuracy.
- It might misinterpret prompts.
- The assumptions and ability to simulate scenarios or conditions are limited.
- An inability to infer from ambiguous queries.
- Responses are more generalized
- Providing confidential information concerning experiments and innovation is risky
- The inability to actually interact with real experiments or software limits its capability to adapt.
- Not suitable for complex test scenarios
- Coding and factual errors are seen without warning
- Human validation is required to add to the already available tasks
Best Practices for Utilizing ChatGPT in Test Automation
Here are some tips to overcome the multiple challenges that a user may face while performing test automation:
- Be specific with keywords
- Use ChatGPT itself to generate the ideas
- Ensure manual validation of its query with a focus on each line and step
- Avoid sharing sensitive information or idea
- Focus on the methodology of ChatGPT to develop answers and modify them accordingly
- Ensure to provide context about your queries
- Provide background information, specifically for the concerned domain
- Integrate ChatGPT with traditional automation toolkit
- Carefully consider possible biases by ChatGPT and check the results accordingly
Conclusion
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FAQs
1. Can ChatGPT replace manual testing entirely?
Despite numerous helpful responses in different scenarios, the accuracy and efficiency of ChatGPT are questionable due to significant challenges and limitations. Hence, chat gpt for automation testing will not replace manual testing entirely.
2. How accurate does ChatGPT generate the test scripts?
The lack of accuracy in its responses may not ensure complete correctness. The reliability of test scripts varies depending on the scenario and complexity of the query.
3. Can ChatGPT handle complex user interactions and edge cases?
ChatGPT has several limitations, and the inability to handle complex user interactions and edge cases is one among them.
4. Is prior knowledge of programming or AI required to use ChatGPT for test automation?
A good understanding of programming languages is crucial for a professional career in test automation. It aids in various aspects.
5. Can ChatGPT be integrated with existing test automation frameworks and tools?
It can be integrated with frameworks like Selenium, Puppeteer and multiple others.
