Satabdi Mishra

Introduction: The start of a new decade has brought with it the wonder and awe of Artificial Intelligence (AI). The biggest breakthrough has been through the efforts of the not-for-profit research company, OpenAI. Originally created in 2015, as an antithesis to Google Deepmind to freely collaborate with the research community and spearhead the ethical development of AI, they have launched several revolutionary products like Dall-E, MuseNet, Whisper, Dactyl, Codex, and the most popular GPT language models. GPT-3 GPT-3 (Generative Pre-trained Transformer 3) is one of the most advanced natural language processing (NLP) models and has the potential to generate responses to an unlimited range of human language queries with little to no human input. GPT-3 works by looking for patterns in text. The model is trained on a massive text dataset of over 45TB of curated text sourced from across the web with a whooping 175 billion parameters. It can be used for a variety of natural language processing tasks, including question-answering, summarization, conversation modelling and text generation. With advanced capabilities in language understanding, generated text, and conversational AI, OpenAI GPT-3 is regarded as the most powerful language model to date. How does it work? The model is based on multi-layer transformer architecture, which is a neural network that learns context and thus meaning by tracking relationships in sequential data like the words in this sentence. Transformer models apply an evolving set of mathematical techniques, called attention or self-attention, to detect subtle ways even distant data elements in a series influence and depend on each other. GPT-3 uses a technique called masked language modelling, which implies that it is trained to predict the next word in a sentence given the previous words. This allows the model to generate text that is more natural and human-like. It utilizes zero-shot learning, wherein a pre-trained deep learning model is made to generalize on a novel category of samples, i.e., the training and testing set classes are disjoint. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. This allows the model to generate highly accurate results with just a few input words and produce very natural-sounding text. Lastly, it employs transfer learning to exploit the knowledge gained from a previous task to improve generalization about another. This allows the model to quickly adapt to new tasks and generate more accurate results. This is significant in deep learning since most real-world problems typically do not have millions of labelled data points to train such complex models. The model makes use of reinforcement learning(RL) algorithms and re-trains itself from user feedback for contiguous learning so as to improvise itself with time. Testing with GPT-3 OpenAI GPT3 might be a great choice for automating the software development life cycle (SDLC). With GPT-3, the Test engineers can leverage the following use cases: The most common use case for a text generation model within testing would be definitely Test Code Generation i.e. automatically generating test scripts based on data. Minimal to zero manual interventions and time spent looking up IDs, selectors, or xpaths. With GPT-3, Test Script & Test Case Generation can be made smooth, as GPT-3 uses a Prompt, Example, and Output model. To generate test scripts, the test engineer simply needs to provide a Prompt that includes the context of what they are trying to do. For example, simple text: “Open www.xyz.com, and login”, test cases, or analytics data. Then the test engineer needs to provide an example of what they expect back from GPT-3, which in this case would be an example of the code in the language you wish to convert the data to. Supplying approximately 4-6 examples will yield the best results. Once those two things are supplied to GPT-3, the output will return the code for the prompt given, which can then be saved to a file either permanently or temporarily and then executed. We can also apply similar principles to generating entire test frameworks based on input loaded into GPT-3 and converting it into customized test frameworks for the application under test (Web, Mobile, API). The engineer can simply specify the application under test, what language, and the type of automation framework they would like to begin with, and then the framework can be automatically generated within a very short period. How can we leverage GPT-3 in test automation development? OpenAI GPT-3 is powerful and a perfect indication of where AI is headed in terms of integrating AI systems into test automation via its quick setup and easy-to-use integration. There are some learning curve areas that should be taken into account when evaluating if this tool would be right for your team. But in comparison to generating your own custom model, the learning curve is completely manageable. The test automation development process can be broadly classified into the following three major phases. Let us see how we can use GPT-3 to accelerate each one of these phases and make the entire process faster and more efficient. 1. Identifying the application under test. The first phase of the test automation development process is identifying applications under test (AUT). This involves identifying the business logic, functional requirements and non-functional requirements that need to be tested. GPT-3 can identify the application under test by using natural language processing (NLP) to analyse the code base, text in the user interface and the associated documentation to determine the application type. For example, if the application contains English words like “Shipping” or “Add to cart,” etc. then GPT-3 can infer that the application is an e-commerce platform. By following this step, we can create reusable test cases/scenarios based on these identified objects/functions/dependencies which can be used later during any testing activities such as manual regression or exploratory testing. AI can also help accelerate your entire process. 2. Creating test cases/scenarios. Once the data structures have been identified, GPT-3 can then generate test scenarios for each case. Also, you can leverage GPT-3 to generate