Skip to main content

Shutter Sync, when failure provides enlightenment

Shutter sync is an interesting artefact generated when we video moving objects. Take a look at this video of a Helicopter taking off:

Notice how the boats are moving as normal, but the rotors appear to be barely moving at all. This isn’t a ‘Photoshop’. It’s an effect of video camera’s frame rate matching the speed/position of the rotors. Each time the camera takes a picture or ‘frame’ the rotors happen to be in approximately the same relative position.

The regular and deterministic behaviour of both machines enables the helicopter to appear to be both broken and flying. The rotors don’t appear to be working, while other evidence suggests its rotors are providing all the lift required.

What's so exciting is that this tells us something useful, as well as apparently being a flaw or fail. We could both assume the rotors move with a constant rotation, and estimate a series of possible values for the speed of the rotors, given this video.

Your automated checks/tests can be exhibit this too. Take for example a check that often/always ‘fails’. But when you examine the software with other tools the problem disappears.
This might be a probe effect - that is, the ‘bug’ may only happen because of the testing tool. This is actually quite common. It was a bugbear of mine in the days of pre-webdriver browser automation e.g. Selenium RC, as RC inserted a lot of JavaScript into the page - often resulting in erroneous behaviour.

The ‘failure’ could also be a race condition. The regular systematic behaviour of the the testing framework, interacts with near perfect timing with the software being tested. The checking code, sees the problem frequently & repeatedly - as it always checks in the narrow window of time, when there is a problem.

Automated test/check ‘failures’ like the above are often dismissed immediately as things to work-around or fix. While it might make sense to ‘clean’ this from our results, we could miss potentially valuable avenues for testing. The ‘failing’ test is presenting us with information. That information might be more valuable than a clean pass/fail result - especially if the apparent failures have an inconsistency of this kind.

Just as with shutter sync, where we determine the behaviour of the rotors from the video. We can glean useful information from the ‘failing’ test/check. Investigation of these test ‘failures’ might show that the GUI is not quite in sync with the database or other users screens etc.  Maybe when the UI suggests an action is done - the user/system could actually still write some data for a short while. Or two events that as far as an API shown happen sequentially - in reality happen at the same time.

Comments

Popular posts from this blog

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 -…