Skip to main content

Avoiding Wild Goose Chases While Debugging.

When I’m debugging a complex system, I’m constantly looking for patterns. I just ran this test code... What did I see in the log? I just processed a metric $^&*-load of data, did our memory footprint blip?

I’m probably using every freedom-unit of screen space to tail logs, run a memory usage tool, run an IDE & debugger, watch a trace of API calls, run test code… And I’m doing this over and over. Then I see it. Bingo, that spike in API calls hits only when that process over there jumps to 20% processor usage when the app also throws that error.

Unfortunately, I may have been mistaken. On a sufficiently complex system, the emergent behaviour can approach the appearance of randomness. Combinatorial explosions are for real, and they are happening constantly in your shiny new MacBook.

Multiple comparisons problem - Wikipedia

My bug isn’t what I think it is. I’m examining so many variables in a system with dozens of subsystems at play, it's inevitable I will see a correlation. We know this more formally as the multiple comparisons problem.




We’ve evolved to spot patterns and find paths in a forest of information. Sadly, we can be prone to see problems where only coincidences have occurred.

In this situation, it's easy to follow this fresh lead and spend your next half an hour chasing the new truth. We’ve all done it. Experiment taking a moment in review, try a clean dataset or different tool to falsify your conclusion. If your idea stands up to scrutiny, then the next 30 minutes might not be such a waste of my time. 

And if you completely disprove your-self? then relax, you’ve exercised superb technical analysis skills. Get a cup of tea, because you’ve earned it.
It Ain’t What You Don’t Know That Gets You Into Trouble. It’s What You Know for Sure That Just Ain’t So
  - (Maybe...) Mark Twain

Comments

Popular posts from this blog

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. Testing is gambling with your time to find information about the app. 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

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) A good motto for software testing, as well as pan-galactic hitchhiking. 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 tak

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 c