Valuable feedback, fast with AI

If you’d ask me what the goal of testing is, my answer would be simple:

“To provide valuable feedback, fast”

If you’d ask me what AI can do to help us achieve that goal, my answer would be a little less simple.

AI can help us understand systems, analyse risks, explore products, design tests, write and maintain test automation, investigate failures and report our findings.

It can also confidently give us incorrect information, create tests that provide little value, hide problems behind plausible explanations and make us faster at doing the wrong thing.

So, how do we make sure we end up on the right side of that equation?

In this three-day, immersive, hands-on course, you will investigate how AI can support testing and test automation, experiment with different ways of using AI in your work, and learn how to decide where AI can add value, and where human judgment remains essential.

We will follow the journey of a fictional online bank as it tries to improve the way it tests its software and make effective use of AI along the way. You will examine real-world testing problems, investigate them with and without AI, design and implement AI-assisted solutions, and use what you learn to create an AI-enabled testing strategy.

There will be no AI magic tricks. No endless collection of clever prompts. Instead, we will focus on using AI deliberately, critically and pragmatically, to help achieve the same goal we have always had: valuable feedback, fast.

Yes, I’d like to book this course for my team!

What will you learn?

Day one - AI as a testing partner

On the first day of the course, participants will be introduced to ValuBank, a development organization working on an online banking application. A significant new feature is about to be introduced, and the teams need to determine how to test it.

We will first approach the problem without AI. You will investigate the system, study the change, identify risks and create test ideas based on your own experience and judgement. Only then will we introduce AI and use it as a partner in our testing activities.

We will experiment with using AI to understand an unfamiliar system and a new change, identify risks, support exploratory testing and analyse and document our findings. Throughout the day, we will compare what we get from AI with what we get from our own testing expertise.

The goal? To find out where AI can genuinely make us better testers.

Understanding a system and a significant change
Using AI to investigate systems, requirements and changes
AI-assisted risk analysis
Exploratory testing with AI
Using AI to analyse and document testing results
Evaluating and challenging AI output
Understanding where AI adds value, and where it doesn’t

_Day two - AI and test automation

It’s no news that AI can generate test code very quickly. However, producing code is not the same as getting valuable feedback from your tests. If we’re careless, we can very quickly end up with a large test suite that is producing a lot of noise, without much of a signal.

On day 2, we will therefore look at AI-assisted test automation from the perspective of our overall testing strategy. We will investigate where AI can help us design, implement, review, debug and maintain automated tests, while paying close attention to the quality and value of the resulting feedback.

We will also experiment with more autonomous approaches, including AI agents that can plan, execute and analyse testing activities.

Identifying opportunities for AI-assisted test automation
Generating and implementing automated tests
Reviewing and improving AI-generated test code
Using AI to investigate and explain test failures
AI-assisted test maintenance
Designing guardrails for AI-assisted automation
Designing and using AI agents for testing
Evaluating the risks and limitations of autonomous test automation

Day three - Building an AI-enabled testing and automation strategy

After wrapping up our work on AI-assisted testing and test automation, it’s time to turn those experiments into a actionable strategy.

We will return to the working agreements we created at the start of the course and use what we have learned to refine them. We will identify where AI can contribute to the testing process, where it should be constrained, and where we can and cannot do without human judgment.

We will then turn these decisions into an AI-enabled testing strategy for ValuBank, using the tools and techniques we have explored during the course.

Identifying valuable AI use cases across the testing process
Deciding what AI should do, what humans should do and where they should work together
Designing AI-assisted testing and automation workflows
Measuring the value and risks of AI-assisted testing
Creating and presenting an AI-enabled testing strategy

Who should take this course?

This course is for software development and testing practitioners, as well as tech and team leads, who want to learn how to make effective and responsible use of AI in testing and test automation.

It is particularly useful for teams who have already experimented with AI and are now asking questions such as:

Where can AI actually help us in our testing work?
How do we make sure AI-assisted and AI-generated tests provide useful feedback?
Where should we keep humans firmly in the loop?
How much autonomy should we give AI agents?
How do we track the impact of our AI-assisted efforts on our product and our process?
How do we turn individual AI experiments into a coherent strategy?

Participants are expected to have a basic understanding of modern AI concepts such as LLMs, prompting, and AI agents.

The technical, hands-on exercises can be presented using Java, C# or TypeScript, depending on the context and requirements of the group. Some familiarity with one of these languages will be advantageous for the test automation exercises. Even without extensive programming experience, though, there is plenty to get out of the course, and pair and ensemble work will be encouraged throughout.

Course duration and delivery

The ‘Valuable feedback, fast with AI’ course takes 3 days. The course is deliberately designed as an immersive, experiential workshop combining investigation, discussion, technical experimentation and strategy work.

The wide range of exercises in this course is designed to address both the practical and strategic aspects of using AI in testing and test automation. As a participant, you will:

perform testing activities both with and without AI
contribute to facilitated discussions about the benefits, risks and limitations of AI
complete hands-on exercises using AI to support testing and test automation
challenge and review AI-generated work rather than accepting it at face value
build and present an AI-enabled testing strategy
define concrete actions for bringing what you learned back to your own context

Due to the experiential nature of the course, I strongly prefer running it on site, in person. The maximum group size is 25 participants. There is no minimum group size, but having at least 6 participants contributes to better discussions through a wider range of backgrounds, ideas and prior experience.

I’m interested, what’s next?

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Yes, I’d like to book this course for my team!

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