Author: Eran Kinsbruner
Publisher: Perforce
Pages: 357
ISBN: 978-8671126044
Print: B08FKW8B9B
Kindle:B08FKWD2TR
Audience: Devops developers
Rating: 3
Reviewer: Kay Ewbank

With a subtitle of 'machine learning and artificial intelligence in the age of devops', this book certainly sounds as though it fits current trends - so do the contents match the promise?

The book starts with a section covering the fundamentals of AI and machine learning in testing, with chapters on AI and ML testing tools, how to classify them, and AI-based autonomous testing. The book then moves to look at testing AI-basd applications, and an overview of the new categories of software defects in the current era.

The second section of the book is mainly about continuous testing and how this can be done using AI and ML. The author starts with an overview, after which individual chapters have been contributed by people from a variety of companies specializing in AI-based testing tools. Chapters included an introduction to robotic process automation, API testing with AI and ML, testing conversational AI apps, and cognitive engineering.

The third and final part is titled 'maturing code quality and devops teams productivity using AI and ML', and like the previous section has chapters from contributors from a range of companies selling products that fall into this market sector. Chapters cover fuzzing and ML, using machine learning to improve static code analysis results, and expediting release cycles with test impact analysis using AI and ML.

Conclusion

There was some good material in this book, but I found it frustrating. Many of the chapters looked suspiciously like paper versions of presentations from conferences or sales talks, and while some chapters were meaty enough, I read others waiting for the technical material to start, only to get to the conclusion without feeling things had ever got going. This was exacerbated by the fact that the chapters are short - there are 26 chapters in a book of 350 pages, with lots of screendumps. I did find chapters covering tools that sounded interesting, and if you're interested in finding out more about what products are available in the devops sector that use AI and/or ML, this might be a good resource.

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