From f917b40044dcde6a9dee0613b33bd735022985d7 Mon Sep 17 00:00:00 2001 From: Craig Maloney Date: Wed, 20 Jun 2018 08:24:07 -0400 Subject: [PATCH] Fleshing out more of ch. 2 --- chapter02.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/chapter02.md b/chapter02.md index ea5f838..7241077 100644 --- a/chapter02.md +++ b/chapter02.md @@ -25,3 +25,17 @@ There's nothing wrong with reading a post-mortem about a project - we can learn There are many metrics for which folks rank various programmers. You've likely seen these metrics manifest themselves in competition sites, commits to projects, productivity, turn-around of code, and good ol' gut feelings. We do it to ourselves and others. We compare our work against our peers and folks that we admire. But that can lead us to make comparisons that aren't objective or based on all of the data. I can compare myself against folks who do low-level programming and find that I don't measure up in that realm; never mind that I haven't done a whole lot of low-level programming. Or I can compare myself against folks who were mentored by programmers whose names are legendary in the field. Unfortunately I will likely find gaps between my knowledge and their knowledge. Comparisons like this tend to be unhelpful and lead us into punishing ourselves for not being the other person. We look at our projects and our history and find that we're not the other person, nor could we ever be the other person. The major problem with ranking programmers (or really anything for that matter) is that ranking systems are based on one set of criteria. There is no real standard for ranking programmers. Sites that rank programmers based on the number of problems they've solved have only determined that there are a set of programmers who really enjoy solving these sorts of problems and will spend the time to do so. It tells us little about the programmer's abilities. Or we can look at how many lines of code a programmer used in order to solve a problem, or how "clean" the solution is (clean being another nebulous term). We can look at the Big O notation of the code that a programmer generates or how well the code adapts to various circumstances. Very little of this tells us anything about the programmer themselves. What it does tell us is that the programmer has had experiences that lead them to that particular solution. It tells us that the programmer has seen these sorts of problems before and has thought deeply about how to make them better. We learn that the programmer has devoted time and energy to practice these sorts of problems. What it doesn't tell us is an overall measurement of the programmer's skills or abilities. It's similar to the apocryphal tale of a brilliant professor. This professor was an absolute genius in his field and was one of the go-to people for answers about his subject. But despite his brilliance he was unable to understand how to change a tire on a car. Does that mean the professor was not as brilliant as folks claimed him to be? Hardly. It just meant that the professor had spent more time in his profession than he had changing tires. The same is true for programmers. If a programmer spends most of their time solving a particular set of problems they will eventually become skilled at those sorts of problems. But if they struggle with a different sort of problem it doesn't discount their overall skills; it just points out an area that they might want to work on. + +## Measuring programmer output + +There's also a tendency to measure programmer productivity via how many commits the programmer can make to a project. In an era of social coding sites like Github / Gitlab we can easily look at what other coders are committing and feel that we're somehow not measuring up. And unlike certain measurements of old (lines of code, which measures how many lines of code a programmer adds to a program) we can see the quality of their commits to a project. It can be daunting seeing a lot of quality work done by our peers. + +This too can be a source of frustration and feelings of inadequacy. "Why can't I be as productive or useful as this other person" we might say to ourselves? Or worse, these metrics may be used by others to judge productivity and we may find ourselves being criticized for our output (or lack thereof). + +Unfortunately commits and lines of code are the most visible measurement of coding productivity, but they don't show much about the actual practice of programming. We can't measure to any degree of accuracy the amount of time thinking about the problem. We don't see the mounds of reference material the programmer used in order to figure out a solution. And we certainly don't know just from looking at commits if this is the result of one person's afternoon of work or many days of work (unless they commit more often). We might find that this person is committing the work of multiple folks in their organization with the committer as the focal point for the other contributions. + +Measuring ourselves on the quantity of others output is easy and seductive but it isn't useful for figuring out how to improve ourselves in relation to the other programmer (other than "generate more commits". That line of thinking can lea us to thinking that we're not spending enough time doing "actual programming" and lead to overwork, stress, and burn-out. + +## Ways to improve (needs better title) + +(How to be more mindful about comparing ourselves to others) -- 2.31.1