English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 8h 16m | 1.34 GB
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.
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.
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
Table of Contents
1 Part 1. Mindset Establishing a positive relationship with LLMs
2 Chapter 1. Enhancing testing with large language models
3 Chapter 1. Delivering value with LLMs
4 Chapter 1. Summary
5 Chapter 2. Large language models and prompt engineering
6 Chapter 2. Avoiding the risks of using LLMs
7 Chapter 2. Improving results with prompt engineering
8 Chapter 2. Examining the principles of prompt engineering
9 Chapter 2. Working with various LLMs
10 Chapter 2. Creating a library of prompts
11 Chapter 2. Solving problems by using prompts
12 Chapter 2. Summary
13 Chapter 3. Artificial intelligence, automation, and testing
14 Chapter 3. How tools help with testing
15 Chapter 3. Knowing when to use LLMs in testing
16 Chapter 3. Summary
17 Part 2. Technique Task identification and prompt engineering in testing
18 Chapter 4. AI-assisted testing for developers
19 Chapter 4. Pairing with LLMs
20 Chapter 4. Building in quality with AI assistance
21 Chapter 4. Creating our first TDD loop with LLMs
22 Chapter 4. Improving documentation and communication with LLMs
23 Chapter 4. Maintaining a balance with code assistants
24 Chapter 4. Summary
25 Chapter 5. Test planning with AI support
26 Chapter 5. Focused prompts with the use of models
27 Chapter 5. Combining models and LLMs to assist test planning
28 Chapter 5. LLMs and test cases
29 Chapter 5. Summary
30 Chapter 6. Rapid data creation using AI
31 Chapter 6. Processing complex test data with LLMs
32 Chapter 6. Setting up LLMs as test data managers
33 Chapter 6. Benefiting from generated test data
34 Chapter 6. Summary
35 Chapter 7. Accelerating and improving UI automation using AI
36 Chapter 7. Improving existing UI automation
37 Chapter 7. Summary
38 Chapter 8. Assisting exploratory testing with artificial intelligence
39 Chapter 8. Using LLMs during exploratory testing
40 Chapter 8. Summarizing testing notes with LLMs
41 Chapter 8. Summary
42 Chapter 9. AI agents as testing assistants
43 Chapter 9. Creating an AI Test Assistant
44 Chapter 9. Moving forward with AI test assistants
45 Chapter 9. Summary
46 Part 3. Context Customizing LLMs for testing contexts
47 Chapter 10. Introducing customized LLMs
48 Chapter 10. Embedding context further into prompts and LLMs
49 Chapter 10. Summary
50 Chapter 11. Contextualizing prompts with retrieval-augmented generation
51 Chapter 11. Building a RAG setup
52 Chapter 11. Enhancing data storage for RAG
53 Chapter 11. Summary
54 Chapter 12. Fine-tuning LLMs with business domain knowledge
55 Chapter 12. Executing a fine-tuning session
56 Chapter 12. Summary
57 Appendix A. Setting up and using ChatGPT
58 Appendix B. Setting up and using GitHub Copilot
59 Appendix B. Working with Copilot
60 Appendix C. Exploratory testing notes
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