Provenance & Profiling

Is your car German or Japanese? Are your chocolates from Belgium? And your wine, which country might that be from? There's a good chance you know the answer to some of those questions. Our culture places value on provenance. That is, we care where our possessions originate. It's something we tend to notice.

Warning label?

Furthermore, we ascribe, often without our notice, characteristics to things because of their provenance. For example, that's a Japanese radio - its reliable but not cheap, etc. I often do this un-empirically, without measurement or examination. (that's a flaw)

For software testing, our automatic identification of provenance can be both a useful tool and a distraction.

Noticing where or from whom a feature originated can be enlightening. You may learn over time that a particular team or person tends to implement certain things well, and others things not so well.

This has a tendency to help me to find some bugs relatively easily with individual teams. The first time it may have been time-consuming to see an issue. On subsequent releases, annoyingly easy.

That emotion is useful. Its an indication that you have, maybe subconsciously, profiled the team. You can direct your response to that feeling, in constructive ways. For example, searching for a route cause or suggesting the addition of some unit tests.

It can be useful to think and deliberate over how you may have profiled the teams or people. Again, this is valuable 'intelligence,' the profile can help with planning your time and focusing test automation efforts. Though, the pattern also embodies a form of bias. You should be aware that you are subject to this bias, and routinely double check your assumptions.

It would be easy to concentrate too long on easy to find - known unknowns that were easy to spot thanks to your biased view. To increase our opportunities for discovering new types of issues, set aside time for investigating the app in other ways.

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The gamification of Software Testing

A while back, I sat in on a planning meeting. Many planning meetings slide awkwardly into a sort of ad-hoc technical analysis discussion, and this was no exception. With a little prompting, the team started to draw up what they wanted to build on a whiteboard.

The picture spoke its thousand words, and I could feel that the team now understood what needed to be done. The right questions were being asked, and initial development guesstimates were approaching common sense levels.

The discussion came around to testing, skipping over how they might test the feature, the team focused immediately on how long testing would take.

When probed as to how the testing would be performed? How we might find out what the team did wrong? Confused faces stared back at me. During our ensuing chat, I realised that they had been using BDD scenarios [only] as a metric of what testing needs to be done and when they are ready to ship. (Now I knew why I was hired to help)

There is nothing wrong with checking t…

Betting in Testing

“I’ve completed my testing of this feature, and I think it's ready to ship”
“Are you willing to bet on that?”
No, Don't worry, I’m not going to list various ways you could test the feature better or things you might have forgotten.
Instead, I recommend you to ask yourself that question next time you believe you are finished.
Why? It might cause you to analyse your belief more critically. We arrive at a decision usually by means of a mixture of emotion, convention and reason. Considering the question of whether the feature and the app are good enough as a bet is likely to make you use a more evidence-based approach.

Why do I think I am done here? Would I bet money/reputation on it? I have a checklist stuck to one of my screens, that I read and contemplate when I get to this point. When you have considered the options, you may decide to check some more things or ship the app. Either could be the right decision.
Then the app fails…
The next day you log on and find that the feature is b…

A h̶i̶t̶c̶h̶h̶i̶k̶e̶r̶'s̶ software tester's guide to randomised testing - Part 1

Mostly Harmless, I've talked and written about randomisation as a technique in software testing several times over the last few years. It's great to see people's eyes light up when they grok the concept and its potential.
The idea that they can create random test data on the fly and pour this into the app step back and see what happens is exciting to people looking to find new blockers on their apps path to reliability.
But it's not long before a cloud appears in their sunny demeanour and they start to conceive of the possible pitfalls. Here are a few tips on how to avert the common apparent blockers. (Part 1) Problem: I've created loads of random numbers as input data, but how will I know the answer the software returns, is correct? - Do I have to re-implement the whole app logic in my test code?
Do you remember going to the fun-fair as a kid? Or maybe you recall taking your kids now as an adult? If so then you no doubt are familiar with the height restriction -…

investigating software

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