Gojko Adzic

Thoughtworks Studios were very kind to organise a Twist 2.0 demo for me this morning. I was very impressed with the new productivity features in the tool. Twist is the functional testing component of their lifecycle management tools, started with the idea to empower teams to manage complex test sets and promote collaboration in agile testing with non technical users.
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There is a point during the implementation of acceptance testing where it is enough to give you some value, but there is still a lot of friction with the tools. At that point, many teams start challenging whether the thing is paying off or not. Here is a simple way to prove that to yourself.

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Here’s a video from a joint workshop that David Evans, Mike Scott and I organised yesterday at Skills Matter. We talked about strategies to get the most out of acceptance tests (especially with FitNesse) and organised a group workshop to review some good and bad examples of acceptance tests – taken from my Hands On Acceptance Testing workshop.

While doing research for my new book, I was very surprised to find out that Jim Shore gave up on acceptance testing. I use his “describe-demonstrate-develop” process description all the time in my workshops, so I guess I better stop doing that. Jim Shore wrote:

My experience with Fit and other agile acceptance testing tools is that they cost more than they’re worth. There’s a lot of value in getting concrete examples from real customers and business experts; not so much value in using “natural language” tools like Fit and similar.

The two failure patterns that Shore describes in his post are falling back on testers to write everything and merging acceptance and integration tests. I’ve experienced both of these myself, and it seems that they are common in general. We discussed both during the top 10 ways to fail with acceptance testing openspace session at CITCON Europe last year. However, there are good ways to solve both problems. Continue Reading »

I got this question from a reader today:

How would you effectively define a sufficient set of If-When-Then scenarios to test for correctness what is potentially an extremely large set of transformations?

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