Most professionals who have tried ChatGPT get a positive impression. ChatGPT is a very simple and smart tool that can do many more tasks than traditional AI assistants. At the same time, in some cases, it can work faster, better, and with more efficiency than humans. “I’ve done with ChatGPT the work that usually took 2 work days for 2 hours!” My friend shared with me such a wonderful experience. Another friend speculated that ChatGPT could replace most knowledge workers. Is this possible? In this article, the Zebrunner team explores whether this AI tool can replace QA engineers and finds out how to use ChatGPT in the QA sphere with maximum efficiency.
Will ChatGPT potentially disrupt jobs in the knowledge worker industry?
A new technological revolution is happening now, this is breathtaking and a bit frightening. It seems that ChatGPT is disrupting our current labor market and our approaches to work. However, this is not the first technological revolution from the beginning of the XXth century. Moreover, this is not the first such type of revolution from the 90th.
We all (or almost all) remember when computers became a must-have gadget not just for companies but for the average person too. We remember how the Internet transformed our world into a global market. We remember how smartphones appeared and put all possibilities of the global world into our pockets. Back in the early 2000s, cybernetics and the development of artificial intelligence and robotics technologies were mostly theoretical concepts, with little practical application. However, today we have many examples of these technologies in action, and ChatGPT is one of them. Anyone can try it out and see for themselves that it works.
As we see, ChatGPT is just the next step of technological development. It seems more likely that “knowledge” workers will adjust to new conditions and tools and change their approach to work.
In particular, when a new technological field begins to rapidly grow, it has a significant impact on the job market by creating a multitude of new professions. For instance, in recent years, the rise of technology has led to the emergence of numerous job titles such as online marketing managers, social media managers, cloud engineers, DevOps engineers, data scientists, cybersecurity analysts, mobile app developers, and AI/machine learning engineers. This is not an exhaustive list, there are many other new professions in the informational technology field.
ChatGPT impacts the job market in a similar way, we already get a range of announcements about new positions for interaction with ChatGPT. This technology simplifies our routine work by allowing us to delegate repetitive tasks to AI, freeing up our time to focus on generating and realizing creative ideas.
Can ChatGPT substitute QA engineers?
ChatGPT is a large language model developed by OpenAI. The model uses deep learning techniques to generate human-like responses to natural language prompts. It allows ChatGPT to understand and respond to a wide range of questions and statements. It is capable of a wide range of language tasks, including language translation, question answering, programming code writing, text completion, report preparation, conversation generation, and more.
ChatGPT provides a huge amount of benefits for QA engineers. They can use ChatGPT to generate test cases and identify non-obvious scenarios where the software may perform poorly. By collaborating with the AI model, testers enhance their efficiency and contribute to the overall quality of the product.
ChatGPT guarantees the accuracy and dependability of the testing process, as it reduces the possibility of human error to zero in some cases. This technology automates repetitive tasks, where humans are prone to missing critical details. Moreover, this AI model is advantageous for manual testing, as it helps QA engineers to oversee critical edge cases that may have been overlooked. But can ChatGPT replace QA engineers?
Manual QA requires a creative and non-traditional approach to testing. The key goal of a manual QA engineer is to find non-obvious bugs and explore real-world scenarios that can produce mistakes. The position of manual QA demands a problem-solving ability that is currently beyond the scope of AI technology. ChatGPT can assist manual QA engineers by triaging and classifying bugs, creating documentation and knowledge sharing.
What about automation QA engineers? ChatGPT can generate test cases, analyze automated test results, and identify patterns in data. Nevertheless, it is more likely to complement automation engineers rather than replace them. While ChatGPT can provide valuable insights and automate certain parts of the testing process, human QA professionals have in-depth knowledge of testing frameworks and methodologies, as well as the ability to develop and implement complex automated test suites that ensure the quality and reliability of software applications.
I would compare using ChatGPT in QA with automation implementation. At the beginning of test automation, there were a lot of predictions that automated testing will substitute for a manual one. Has it happened? No, it hasn’t. Both manual and automated testing are important components of the QA process. It’s logical to assume that ChatGPT will enforce QA tools and pipelines. Let’s see the key ways we can use ChatGPT in the QA.
QA engineers can use ChatGPT for generating questions related to the features or functions you want to test. Having satisfactory responses you can create test cases. ChatGPT also can be applied to analyze the software requirements and specifications and suggest possible test cases based on its understanding. Additionally, ChatGPT assists in creating test cases for different scenarios and edge cases. It can explore the software's behavior under different conditions and suggest test cases that cover all possible scenarios. This ensures that the software is thoroughly tested and all possible issues are identified and addressed before the product is released.
This is a routine task that takes a lot of time. Now, it’s possible to delegate it to AI. ChatGPT is good for test plan development. Ask questions, gather information, and use it to identify the testing scope, approach, the resources required, and the timelines. It’s also convenient to apply ChatGPT for clarification of requirements and specifications for testing. Moreover, ChatGPT can be used to create clear and concise reporting with a testing overview, results, defects, or issues identified.
Let’s say you need to reformat your test plan for Jira tickets. ChatGPT will help you to prepare your data.
ChatGPT allows developers to isolate individual units or components of the model for unit testing. By testing each unit separately, developers can identify issues with the specific components of the model and fix them quickly. Also, ChatGPT helps with data generation. For unit testing, developers can use ChatGPT to generate input text and expected outputs for each unit and use them as test cases for automated testing.
Train ChatGPT on existing test cases. It will use this knowledge to generate new test cases automatically. ChatGPT can execute automated tests using a variety of testing frameworks and tools. This AI model interacts with the testing environment, simulates user inputs, and verifies that the application behaves as expected. In addition, ChatGPT can generate test automation reports that provide insights into the testing process.
When a QA engineer encounters an error, they can input a description of the error into ChatGPT, and it can provide potential causes and solutions based on its training data and understanding of programming concepts. Additionally, ChatGPT can provide examples of how other engineers have addressed similar errors in the past, and offer insights into best practices for error decoding and debugging.
ChatGPT assists with documentation providing clear and concise explanations of complex technical concepts related to error decoding. This can be especially helpful for junior QA engineers who may be less familiar with certain programming concepts or terminology.
ChatGPT can generate a large number of diverse inputs that cover a wide range of scenarios and use cases. This way you answer one of the essential testing chatbot challenges about generating realistic and varied inputs to test the bot’s responses. What’s more, you can apply ChatGPT for evaluating the quality of a chatbot’s responses by comparing them to the responses generated by ChatGPT. This way you are identifying areas where the chatbot might fail or where it might perform particularly well. Furthermore, ChatGPT provides a large amount of input data and evaluates the quality of the bot’s responses to that data. This way you can apply ChatGPT for chatbot training.
ChatGPT can be fine-tuned on a specific Natural Language Processing (NLP) task or dataset to improve the performance of NLP models. Fine-tuning involves training ChatGPT on a smaller, task-specific dataset to adapt it to that specific task.
Also, you can use ChatGPT possibilities to generate synthetic data to augment existing datasets for NLP tasks. This way, ChatGPT improves the robustness and generalization of NLP models. Moreover, ChatGPT can be applied to analyze errors made by NLP models and provide insights into areas where the models may be falling short. Furthermore, ChatGPT can be used as a pre-training step for NLP models, providing a strong foundation in language understanding that can be transferred to other NLP tasks. This approach can help improve the performance of NLP models on a range of tasks.
Sentiment analysis testing refers to the process of evaluating the accuracy and effectiveness of a sentiment analysis model or algorithm. This technique is used in natural language processing to automatically identify the sentiment expressed in a piece of text, whether it is positive, negative, or neutral. ChatGPT assists with test data generation.
One of the challenges in sentiment analysis testing is generating a large and diverse dataset of text samples with known sentiment labels. ChatGPT generates synthetic test data that covers a wide range of scenarios and use cases. This improves the robustness of the sentiment analysis model. Also, ChatGPT evaluates the performance of a sentiment analysis model by comparing its predictions with the known sentiment labels of the test data.
ChatGPT is an efficient tool for testing the dialogue flow of a voice assistant application. It helps to simulate user interaction and evaluate the accuracy and relevance of the voice assistant’s responses. You also can apply ChatGPT to test errors and edge cases in voice assistant applications.
This is a type of software testing approach that aims to predict the likelihood of defects or errors in software applications before they occur. This type of testing uses various techniques and tools to analyze the software code and identify potential issues that could lead to defects or failures. ChatGPT generates test cases for identifying potential defects or errors in software. It also can analyze past data, such as previous test results and defect reports, to predict the likelihood of defects or errors in future software releases. With this information, you can prioritize testing efforts and focus on areas of the code that are more likely to contain defects.
With its ability to answer questions and provide suggestions based on vast amounts of data, ChatGPT can streamline and automate many QA tasks that were previously performed manually.
This could potentially lead to a reduction in the number of jobs available in certain areas of QA, particularly those that involve routine and repetitive tasks that can be automated. However, it could also create new job opportunities in areas such as data analysis, AI programming, and machine learning, as companies may require professionals with expertise in these areas to develop and maintain their AI systems.
It's worth noting that while ChatGPT can provide quick and accurate answers to many questions, it's not infallible, and there may still be a need for human oversight and intervention in some cases. Overall, the impact of ChatGPT on the job market in the QA domain will depend on how quickly and extensively it is adopted by companies and how well it performs in real-world applications.
