A changed nothing, and I worry they’re managing to the metric.
B stopped opening and closing a large number of trivial bugs.
C did a cull of old bugs and changed their intake behavior but is growing a backlog.
A changed nothing, and I worry they’re managing to the metric.
B stopped opening and closing a large number of trivial bugs.
C did a cull of old bugs and changed their intake behavior but is growing a backlog.
A has been consistently improving it since before the change, so it's only possible that they are managing to the metric if they had earlier access to it.
B may be doing that, but the graph doesn't actually measure how many bugs you closed. Those ones seem to have decided to manage by the metric, removing the variance but targeting a high, comfortable level.
Agreed on C, they did a large "hey, we will be measured by that now" one time effort and then forgot about the metric.
The change didn't improve anybody's performance.
Also, if this is only a change in managing bugs, nothing may have changed except for more bug tracking for trivial bugs, or the opposite, ignoring more severe bugs
Meh. You can make numbers say anything you want. And any metric ceases being useful once it's known. All in all these lines tell such a partial story as to be useless and I'm immensely suspicious of any manager that enjoys reading in tea leaves.
@LabPlot @programming I'd say its difficult to tell without at formal statistical assessment, but if I had to pick, i'd say B. The difference between pre and post in A is just a continuation of a pre=existing trend. The difference in C looks like it might be reverting to mean over time, or even getting worse than it was prior to the change if the study went on longer.
Assuming a steady flow of bugs of the same kind, we share the same line of reasoning.
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