ChatGPT in Quality Test PDF Download
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Author: Andreas Treutmann
Publisher: BoD – Books on Demand
ISBN: 3757805658
Category : Computers
Languages : en
Pages : 57
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Book Description
ChatGPT is on everyone's lips. There is talk of astonishing features, but also of new risks and dangers. To demonstrate what is really true about all the reports, as a systems analyst and system architect with years of experience, I conducted my own comprehensive quality test. All the important features of ChatGPT were examined closely. Examples are used to explain what works well and what doesn't. In a concluding summary, the opportunities offered by ChatGPT, as well as the risks and dangers, are discussed.
Author: Andreas Treutmann
Publisher: BoD – Books on Demand
ISBN: 3757805658
Category : Computers
Languages : en
Pages : 57
Get Book Here
Book Description
ChatGPT is on everyone's lips. There is talk of astonishing features, but also of new risks and dangers. To demonstrate what is really true about all the reports, as a systems analyst and system architect with years of experience, I conducted my own comprehensive quality test. All the important features of ChatGPT were examined closely. Examples are used to explain what works well and what doesn't. In a concluding summary, the opportunities offered by ChatGPT, as well as the risks and dangers, are discussed.
Author: Mark Winteringham
Publisher: Simon and Schuster
ISBN: 1633437361
Category : Computers
Languages : en
Pages : 302
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Book Description
Speed up your testing and deliver exceptional product quality with the power of AI tools. The more you test, the more you learn about your software. Software Testing with Generative AI shows you how you can expand, automate, and enhance your testing with Large Language Model (LLM)-based AI. Your team will soon be delivering higher quality tests, all in less time. In Software Testing with Generative AI you’ll learn how to: • Spot opportunities to improve test quality with AI • Construct test automation with the support of AI tools • Formulate new ideas during exploratory testing using AI tools • Use AI tools to aid the design process of new features • Improve the testability of a context with the help of AI tools • Maximize your output with prompt engineering • Create custom LLMs for your business’s specific needs Software Testing with Generative AI is full of hype-free advice for supporting your software testing with AI. In it, you’ll find strategies from bestselling author Mark Winteringham to generate synthetic testing data, implement automation, and even augment and improve your test design with AI. Foreword by Nicola Martin. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology There’s a simple rule in software testing: the more you test, the more you learn. And as any testing pro will tell you, good testing takes time. By integrating large language models (LLMs) and generative AI into your process, you can dramatically automate and enhance testing, improve quality and coverage, and deliver more meaningful results. About the book Software Testing with Generative AI shows you how AI can elevate every aspect of testing—automation, test data management, test scripting, exploratory testing, and more! Learn how to use AI coding tools like Copilot to guide test-driven development, get relevant feedback about your applications from ChatGPT, and use the OpenAI API to integrate AI into your data generation. You’ll soon have higher-quality testing that takes up less of your time. What's inside • Improve test quality and coverage • AI-powered test automation • Build agents that act as testing assistants About the reader For developers, testers, and quality engineers. About the author Mark Winteringham is an experienced software tester who teaches many aspects of software testing. He is the author of Testing Web APIs. The technical editor on this book was Robert Walsh. Table of Contents Part 1 1 Enhancing testing with large language models 2 Large language models and prompt engineering 3 Artificial intelligence, automation, and testing Part 2 4 AI-assisted testing for developers 5 Test planning with AI support 6 Rapid data creation using AI 7 Accelerating and improving UI automation using AI 8 Assisting exploratory testing with artificial intelligence 9 AI agents as testing assistants Part 3 10 Introducing customized LLMs 11 Contextualizing prompts with retrieval-augmented generation 12 Fine-tuning LLMs with business domain knowledge Appendix A Setting up and using ChatGPT Appendix B Setting up and using GitHub Copilot Appendix C Exploratory testing notes
Author: Virendra Pal Singh
Publisher: Xoffencerpublication
ISBN: 8119534859
Category : Science
Languages : en
Pages : 175
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Book Description
ChatGPT is an artificial intelligence (AI)-based chatbot that makes use of natural language processing in order to provide conversational discourse that sounds and feels like it was done with a real person. This is accomplished by producing conversational discourse in many languages, including English, Japanese, and Korean. ChatGPT is a cloud-based application that may be accessed using the Azure cloud platform. It was created by Microsoft Research. The language model is able to provide responses to inquiries as well as generate a variety of written material, such as emails, posts on social networking sites, and posts on platforms that enable users to write in the format of an essay. On November 30, 2022, OpenAI developed ChatGPT, a big language model-based chatbot, and made it available to the public. Only the beta version of ChatGPT was first made accessible to users. Through the utilization of this feature, users are provided with the capacity to enhance and lead a discussion in the direction of a desired duration, structure, style, level of information, and language. This ability is made available to users. What we refer to as the Chat Generative Pre-trained Transformer is referred to as ChatGPT in its shorter version. In order to determine the context of the dialogue at each level of the discussion, a method known as "prompt engineering" is utilized. This method involves looking at the prompts and responses that came before it in the conversation. ChatGPT is built on either the GPT-3.5 or the GPT-4 model, both of which are a part of OpenAI's proprietary line of generative pre-trained transformer (GPT) models. Both models were used in the construction of ChatGPT. Conversational interactions are one of the primary focuses of ChatGPT's design. The Google-developed transformer architecture serves as the foundation for these models, which have been modified for use in conversational applications by employing a mix of supervised learning and reinforcement learning strategies. These methods were utilized in order to get a higher level of precision with the models. The research preview version of ChatGPT was made accessible to the general public for the very first time as a free service.
Author: Kohei Arai
Publisher: Springer Nature
ISBN: 3031539605
Category :
Languages : en
Pages : 735
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Book Description
Author: Midhun Moorthi C
Publisher: Academic Guru Publishing House
ISBN: 8119338790
Category : Study Aids
Languages : en
Pages : 209
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Book Description
This book provides a thorough introduction to two cutting-edge technologies known as Generative AI and ChatGPT. Both of these technologies have received much attention in recent years. Generative AI and ChatGPT can completely reshape sectors and society as a whole by increasing productivity and innovation and making it possible to have more tailored experiences. The natural language processing tool, ChatGPT, powered by artificial intelligence technology, enables you to engage in human-like conversation with the chatbot and provides several other benefits. The language model can answer inquiries and assist with activities such as the composition of emails, essays, and code. This book aims to give a comprehensive overview of the technologies, architectures, and training techniques mentioned above, including their history, the process by which they were developed, and their present status. This book assists in discovering novel applications of these technologies that have been put into practice to generate quantifiable advantages, such as increased efficiency, customer happiness, security, and revenue growth. The book also discusses the book's potential applicability across a variety of sectors and use cases.
Author: Alexander Tarlinder
Publisher: Addison-Wesley Professional
ISBN: 0134291085
Category : Computers
Languages : en
Pages : 629
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Book Description
How do successful agile teams deliver bug-free, maintainable software—iteration after iteration? The answer is: By seamlessly combining development and testing. On such teams, the developers write testable code that enables them to verify it using various types of automated tests. This approach keeps regressions at bay and prevents “testing crunches”—which otherwise may occur near the end of an iteration—from ever happening. Writing testable code, however, is often difficult, because it requires knowledge and skills that cut across multiple disciplines. In Developer Testing, leading test expert and mentor Alexander Tarlinder presents concise, focused guidance for making new and legacy code far more testable. Tarlinder helps you answer questions like: When have I tested this enough? How many tests do I need to write? What should my tests verify? You’ll learn how to design for testability and utilize techniques like refactoring, dependency breaking, unit testing, data-driven testing, and test-driven development to achieve the highest possible confidence in your software. Through practical examples in Java, C#, Groovy, and Ruby, you’ll discover what works—and what doesn’t. You can quickly begin using Tarlinder’s technology-agnostic insights with most languages and toolsets while not getting buried in specialist details. The author helps you adapt your current programming style for testability, make a testing mindset “second nature,” improve your code, and enrich your day-to-day experience as a software professional. With this guide, you will Understand the discipline and vocabulary of testing from the developer’s standpoint Base developer tests on well-established testing techniques and best practices Recognize code constructs that impact testability Effectively name, organize, and execute unit tests Master the essentials of classic and “mockist-style” TDD Leverage test doubles with or without mocking frameworks Capture the benefits of programming by contract, even without runtime support for contracts Take control of dependencies between classes, components, layers, and tiers Handle combinatorial explosions of test cases, or scenarios requiring many similar tests Manage code duplication when it can’t be eliminated Actively maintain and improve your test suites Perform more advanced tests at the integration, system, and end-to-end levels Develop an understanding for how the organizational context influences quality assurance Establish well-balanced and effective testing strategies suitable for agile teams
Author: Achilleas Achilleos
Publisher: Springer Nature
ISBN: 3031664590
Category :
Languages : en
Pages : 197
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Book Description
Author: Antonia Bertolino
Publisher: Springer Nature
ISBN: 303170245X
Category :
Languages : en
Pages : 476
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Book Description
Author: Radek Silhavy
Publisher: Springer Nature
ISBN: 3031703006
Category :
Languages : en
Pages : 843
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Book Description
Author: Bernhard Steffen
Publisher: Springer Nature
ISBN: 3031460022
Category : Computers
Languages : en
Pages : 454
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Book Description
This book constitutes the proceedings of the First International Conference on Bridging the Gap between AI and Reality, AISoLA 2023, which took place in Crete, Greece, in October 2023. The papers included in this book focus on the following topics: The nature of AI-based systems; ethical, economic and legal implications of AI-systems in practice; ways to make controlled use of AI via the various kinds of formal methods-based validation techniques; dedicated applications scenarios which may allow certain levels of assistance; and education in times of deep learning.
