Learning from the Boeing 787's broken software.

Earlier this year Boeing 787 engineers were given some new instructions by the FAA (The US government's: Federal Aviation Authority). They were informed that if the aeroplane's electrical generators were left running for 248 days, they would enter fail-safe mode.

In plain English: they will stop producing electrical power. This short video looks into why that might be and how this information can hep us test our software.

The FAA directive is available on their website.
A Guardian article: Boeing 787 bug could cause 'loss of control' eamliner-bug-could-cause-loss-of-control

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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…

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 -…

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…

investigating software

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