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Top Developers are scarce and often “hidden” amongst a bunch of CVs. Too many hoops can scare your best candidates away. Simple coding challenges may weed out your worst candidates, but without revealing who’s best. The result can be hours of fruitless interviews, phone calls and other time-wasting drudgery.
By trying to keep candidates happy, you risk reducing your process to the point where you cannot be sure who the best developer is. When you are paying top wages, you want to know.
What if there was a magic wand you could wave and have all the data you needed to make an informed decision about a developer candidate? That would be nice, right?
You would most likely ask for the following:
Deep data on the above would enable you to objectively compare candidates and hire the best developers.
Unfortunately, the methods dev hiring managers are using don’t truly provide the information they need:
|Development Technique||Delivery Pace||Code Quality||Testing Strategy|
|Online programming puzzles||NONE||LOW||HIGH||LOW|
Looking at this table, is it any wonder that Hiring Managers are left unsure as to who to hire?
The above approaches are what we call “developer assessment theatre”.
Where we are actors pretending that we have special intuition into candidates’ skills and capabilities.
It feels like a comfortable place because any decision can be easily justified.
But it relies on luck and is not a consistent and repeatable process.
“Everything that can be counted does not necessarily count; everything that counts cannot necessarily be counted.”
Einstein could have been talking about the state of dev assessments.
But just imagine if the objective data that counts for assessing developers could be counted and you could obtain it with much less effort than your current process.