Once in history, there comes an invention that defines a particular moment or period of time. ChatGPT is one of those recent momentary Natural Language Processing advancements taking the internet by storm.
As soon as ChatGPT was launched, it garnered a million users[i] in just 5 days. This conversational AI tool is designed by OpenAI and can answer natural language-based questions, write descriptive content, and perform complex tasks.
As a language model, ChatGPT provides an algorithm that helps you process and analyze large datasets and generate responses.
Interestingly, ChatGPT can also write test cases in a broad range of frameworks and languages. Hence, allowing web and mobile app developers to make the most of it.
With this blog post, you will learn more about how you can leverage this platform to write code in multiple programming languages.
Let’s dive in!
ChatGPT and Its Features
ChatGPT is built on top of the Generative Pre Training (GPT-3.5) chatbot, creating well-articulated responses to queries across various topics.
ChatGPT uses AI and ML algorithms to analyze the data, understand natural language patterns, and incorporate words in context. You can ask ChatGPT whatever questions you want and do a back-and-forth chat-like interaction.
The platform creates automated creative responses and can also remember previous conversations to generate coherent answers to your queries.
AI & ML in Software Testing
The adoption of AI and Machine Learning technologies has provided multiple new capabilities to QA testers such as cross-browser and cross-platform testing.
These technologies have significantly augmented the skills and capacities of QA testers and helped them eliminate testing discrepancies.
Take a quick look at the benefits of integrating AI into software testing:
1. Improved Accuracy
Automated testing can identify mistakes made during manual testing. AI comes with effective testing methodologies that can help you write test scripts and deliver the best possible UX, leading to improved accuracy and speed.
2. Quicker Test Creation
AI also helps improve and enhance the performance of intelligent test recorders. You can leverage AI tools to record user sessions and identify missing test cases. They can also add tests to the library if a test case is found missing.
3. Faster Time to Market
Executing tests manually takes time since the development teams often have to change the program source code. AI can speed up the process of running and re-running automated tests, minimize total costs, and allow applications to quickly reach the market.
How Can ChatGPT Impact the Testing Industry?
The evolution of AI chatbots has made everyone question whether the language search models will replace Google search. A question-and-answer chatbot replacing Google search might become a frightening scenario for search marketing specialists.
75%[ii] of executives already fear going out of business within five years if they don’t scale AI.
And add to that, we are in an era where AI bots are shrinking delivery cycles, accelerating rates of source code changes, and decreasing the time taken for ‘Continuous Testing’.
While most businesses have also started using the basic forms of AI, the time demands continuing the testing evolution to achieve a level of efficiency required for the testing of robotics, IoT, and so on, so forth.
Also with new software and IoT devices being developed every other day, it’s going to be an unimaginable amount of data in real-time which will need quality assurance. To resolve such challenges, AI and ML are valuable technologies that help businesses scale and prioritize software quality.
Here’s how ChatGPT can enable test automation effectively.
Test Automation Using ChatGPT
As we talked about how ChatGPT can generate properly formatted and relevant code in many languages, the question is – can ChatGPT be used to generate code for automated testing?
ChatGPT can write Selenium in multiple languages but writing accurate codes would require someone to provide ChatGPT with a description of the test. Since it is a chat-like conversational AI, it seems essential to input major details in your query to generate a perfect, executable code that wouldn’t need any modifications.
Let’s take a look at one of the use cases of Cucumber code generation using ChatGPT.
ChatGPT and Cucumber Test Case
Cucumber is a testing framework that uses behavior-driven development. Some key terms such as ‘Given’, ‘When’, and ‘Then’ are compiled in a feature file.
In step definitions, these natural language phrases are associated with the automation code. This helps Cucumber combine the natural language intent of a test with the automation code.
This example shows how ChatGPT is able to generate code. It generated the scenario and step definitions using a single prompt that Cucumber used in one go. Without explicitly mentioning, ChatGPT already knows the different components Cucumber needs to run.
The prompt about what to test on the website sounds a bit vague. But ChatGPT still managed to generate a script to test the search functionality of the Google website.
AI tools and frameworks will have a significant role in the future of test automation to overcome the challenges QA testers face in testing applications. Using AI for software testing is a step towards making testing more efficient, intelligent, and less time-consuming.
ChatGPT is on its way to becoming a significant natural language model taking AI technology a step forward in the world of software testing. Also, it is more likely to create low-code testing solutions to resolve many testing challenges.
Want to Learn More About the Usage of ChatGPT in Quality Testing? Talk to Us!
Popular Blog Posts