Skip to main content

A Good Run!


“We got a good run from the tests” the tester stated.
“So what’s the story?” the scrum master asked.
“85% Pass” comes the reply, meekly.
“OK, just need to fix that 5% then.” The scrum master announces before striding off to announce that the team is only a couple of % away from success.

Our tester takes a moment to try and process the exchange…


Firstly, their own words:
“We got a good run”
Why had they said that? Well - in a sense - it was true. They had executed the tests before, and they had returned a much higher failure rate. But the code being checked was the same...

OK, so there were at least 3 obvious ways to interpret the data.
  1. The app code meets the criteria checked by the tests. ( Based on test run 2 )
  2. The app code does not meet the criteria checked by the tests. ( Based on test run 1 )
  3. The tests are as reliable a the toss of the coin. ( Based on both test runs )

Its surprising how unlikely people are to choose (3).


Secondly, the scrum master’s words:
“just need to fix that 5%”
Our tester assumes this relates to the de-facto “threshold” that is usually considered as good enough to release. As if the results were a linear scale, such as height or weight. If your code gets over 90% then it gets to pass the gate and get on the release roller-coaster.

The threshold tends to be arbitrary, I worked with a client that thought 86% was good but 83% was just not fit for purpose! Their use tends to indicate a problem. Why are we caring about a number rather than a possibly broken feature? What features or risks do the failing 10% represent? Why do we have so many routine failures?

Do you hear these sort of conversations in your team? If so, then your team might need some coaching.
Labels:

Comments

Post a comment

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
9 comments
Read more

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
Post a comment
Read more

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
1 comment
Read more