Also, (2011)
I have no idea what the website would look like without it, but I have a feeling it does more good than harm.
(Or when the auto-renamer does a funny!)
[1]: Example from the frontpage right now: "How Big Are Factorials?" probably got normalized to "Big Are Factorials?" originally, based off other previous manglings I have seen previously.
It’s a classic example of Goodhart’s Law in action. Code coverage metrics only measure what percentage of code the test suite causes to run. But it’s very, very easy to write tests that run code without actually confirming that it produces correct output for all possible inputs. And it’s very, very easy to assume that a module with 90+% code coverage also has 90+% behavior coverage, and then become complacent about reviewing the suite for proper behavior coverage.
It already is, ive banned unit tests via ci checks from our codebases, they were not particularly useful before LLMs and now they are a net negative.
We require int and some e2es and that does all that units do and more.
For Rust there's https://crates.io/crates/cargo-crap, and for Go there's https://padiazg.github.io/go-crap/
The one that tech tends to stumble on most often is velocity-type metrics. The problem there is that you can’t pay the bills with velocity. And velocity metrics tend to favor cheap shovelware features that cohere poorly over anything that involves having the team slow down on churning out code long enough to work out elegant solutions to subtle problems.
This reminds me of a talk Sandi Metz did called "All the Little Things" where she covers the Gilded Rose kata. In the talk, she reworks her solution until there's almost nothing left showing the essence of the problem being solved.
The cyclomatic complexity metric is touted at each step as a proxy for goodness of design and removal of complexity. However, a weakness of the measure itself is that it doesn't account for the control flow indirection that happens through OO method dispatch itself.
At the same time, Kevlin Henney's talk called "Gilding the Rose" takes the same kata and arrives at a far more sane solution he works up to and reveals at the end.
Short functions used to be hot. Uncle Bob used to proselytize "The first rule of functions is that they should be short. The second rule of functions is that they should be shorter than that." Now emphasizing the benefits of longer functions is pretty trendy. https://github.com/johnousterhout/aposd-vs-clean-code
This industry is pretty idiotic sometimes ¯\_(ツ)_/¯
As I recall, he concluded that there’s really no support for then-popular ideas like short functions, reducing cyclomatic complexity, avoiding explicit branch statements and loops, or TDD. (Tests yes, just not TDD.)
He made a pretty strong case that only two principles are particularly robust. One was that limiting code volume is good. The other is that working people too hard is bad.
What We Know We Don’t Know • Hillel Wayne. (2019, April 28). Hillel Wayne. https://www.hillelwayne.com/talks/what-we-know-we-dont-know/
Every indirect call is a conditional branch, where the condition can be arbitrarily far away in time and space.
Amen, it’s hard to push back against an opaque term (cyclomatic!) when it isn’t really a measure of goodness, it’s a measure of branching, kind of a normal thing in code.
Early on I found that code with low cyclomatic complexity was just usually extremely verbose, lots of passing this to that while avoiding the branching necessary to get something done.
And yes, you can game the metric by hiding the complexity among the confusion of objects and components.
Something like abstractions traversed during interpretation, lines of abstraction v.s. functional implementation, or logic statement dispersion.
It was hard to pin down what was abstraction vs. implementation, but it's much easier now.
The reductio ad absurdum here is that, if abstraction can just be assumed to be bad for quality and maintainability, then perhaps we should go back to hand writing machine code for non-microcoded sequential execution CPU architectures. Conversely, if that idea sounds as preposterous to you as it does to me, then you’re stuck conceding that at least some abstractions are mostly good. So then, before you can automate deciding which ones should and should not count against a code quality metric that’s computed automatically, you need to find an operational definition that can be applied deterministically.
Mild as this ironic passive aggressiveness is, can't imagine something like this in modern sterile corporate messaging.
and
> Here’s why we think that CRAP1 is a good anti-pattern to detect. Writing automated tests (e.g., using JUnit) for complex and convoluted code is particularly challenging, so crappy code usually comes with few, if any, automated tests.
This is so wrong.
The formula uses code coverage as a fundamental metric, when in reality, a lot of people write code "correct from construction", so coverage is not even applicable. Many times too, people only care the use cases they care about work perfectly.
There are also many other reasons code is not tested, not because it's complex, but because it's simple.