April 22, 2017
20 Dev Metrics - 5. Impact of FailureThe fifth in my Twitter series 20 Dev Metrics builds on a previous metric, Feature Usage, to estimate the potential impact on end users when a piece of code fails.
Impact of Failure can be calculated by determining the critical path through the code (the call stack, basically) in a usage scenario and then assigning the value of Feature Usage to each method (or block of code, if you want to get down to that level of detail).
We do this for each usage scenario or use case, and add Feature Usage to methods every time they're in the critical path for the current scenario.
So, if a method is invoked in many high-traffic scenarios, our score for Impact of Failure will be very high.
What you'll get at the end is a kind of "heat map" of your code, showing which parts of your code could do the most damage if they broke. This can help you to target testing and other quality assurance activities at the highest-value code more effectively.
This is, of course, a non-trivial metric to collect. You'll need a way to record what methods get invoked when, say, you run your customer tests. And each customer test will need an associated value for Feature Usage. Ideally, this would be a feature of customer testing tools. But for now, you'll have some DIY tooling to do, using whatever instrumentation and/or meta-programming facilities your language has available. You could also use a code coverage reporting tool, generating reports one customer test at a time to see which code was executed in that scenario.
In the next metric, we'll look at another factor in code risk that we can use to help us really pinpoint QA efforts.
Posted 1 year, 1 month ago on April 22, 2017