Hiccupps

This week I was working with a developer to make a change to a legacy codebase in an area neither of us are very familiar with. The need is easy to describe in general and some occurrences of the behaviour we want to alter are common, straightforward to identify in use, and clear in the code. Unfortunately, the logic in the application is complex, the data used is domain-specialised and the behaviour we are interested in can occur in extremely specific combinations that are hard for a layperson to predict. I had no confidence that cases we knew were all of the cases. My colleague did a round of work and asked me to take a look. I exercised the application in a few ways while inspecting its logs in real time so that I could see the effect of the changes (or not) immediately. This gave up some rarer examples which had not been covered in the code. I added a couple of tests to characterise them and he identified another code change. Next, rather than continue exercising the product lookin

  The Association for Software Testing is crowd-sourcing a book,  Navigating the World as a Context-Driven Tester , which aims to provide  responses to common questions and statements about testing from a  context-driven perspective . It's being edited by  Lee Hawkins  who is  posing questions on  Twitter ,   LinkedIn , Mastodon , Slack , and the AST  mailing list  and then collating the replies, focusing on practice over theory. I've decided to  contribute  by answering briefly, and without a lot of editing or crafting, by imagining that I'm speaking to someone in software development who's acting in good faith, cares about their work and mine, but doesn't have much visibility of what testing can be. Perhaps you'd like to join me?   --00-- "When the build is green, the product is of sufficient quality to release" An interesting take, and one I wouldn't agree with in general. That surprises you? Well, ho

  I was listening to the Radiolab podcast Stochasticity yesterday as I walked to the shop. The presenters were talking to two women, both named Laura Buxton, who became friends years ago because one of them released a balloon with their name and address attached from their back garden and the other found it 150 miles away in their back garden.  After Laura Two got in touch with Laura One they discovered other incredible similarities: they were both tall for their age, had brown hair and blue eyes, both had a labrador, black, a rabbit, grey, and a guinea pig with orange markings. They ended up in the local newspaper, and on national and even international television talking about how glad they were that fate had brought them together. If that sounds astonishing, how about this? As I strolled along the river and the podcast turned to the question of the probability of Laura-like events, David Spiegelhalter , a renowned statistician, jogged past me. What are the chances? The podcast use