Who This Book Is For
Welcome to The Performance Engineer's Guide to JMeter and AI.
This is for performance engineers, QA engineers, developers and DevOps who already know their way around JMeter and want to understand where AI can save their time and efforts and where it cannot (yet).
Whether you spend too much time writing and maintaining test scripts, struggle to correlate multiple metrics and KPIs after a test run, or just want to stop doing the repetitive parts of performance testing manually, this book is for you.
Note: This book assumes you know the basics of JMeter. If you are not yet familiar with JMeter, check out JMeter Playbook first. It covers all the basics you need to know before reading this one.
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What You Will Learn
| Chapter | Topic |
|---|---|
| 1 | How AI fits into the performance testing lifecycle and what problems it can solve |
| 2 | Generating realistic, diverse test data using AI and/or BlazeMeter Test Data Pro |
| 3 | Creating and maintaining test scripts: workload modelling, automated correlation, auto-healing |
| 4 | Analyzing test results: anomaly detection, failure prediction, correlating metrics and KPIs |
| 5 | Identifying and fixing performance bottlenecks with the help of AI |
| 6 | Advanced usage: MCP servers, sub-agents, and building automated testing workflows |
The appendix provides useful prompts, tool references, and recommended reading so you have everything at one place.
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How to Read This Book
Chapters follow the natural lifecycle of performance testing project: test data preparation, building test scripts, running tests, analyzing results, identifying root causes of failures or bottlenecsk. You can read it upside down or jump directly to the chapter that corresponds to the current stage of your project.
Back to topTip: AI tools evolve fast. The concepts and patterns in this book are stable and unlikely to change in the forseeable future, but specific UI details of the tools mentioned might change. Always check the official documentation if something does not match what you see.
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Prerequisites
- Working knowledge of Apache JMeter (Thread Groups, Samplers, Listeners, Extractors, Assertions)
- Basic understanding of HTTP protocol and REST APIs concept
- Familiarity with the operating system command line
No prior experience with AI tools or LLMs is required.