In its (admittedly weak) defense, if the submitter edits the title back during the few-minute window after submitting where this is possible, the normalizer does not kick back in. But it is a bit opaque, in the sense that the poster needs to: realize that the title has been changed; know that they can edit the title (but not too slow!); and know that it won't be filtered through again.
I have no idea what the website would look like without it, but I have a feeling it does more good than harm.
Maybe it's invisible when it works as intended, but the only times I notice a title has been changed, is when it's changed back to the original title, stripping most of the valuable context in the process. De-editorialization, I suppose.
You get an hour or so to edit the title. There is ample time to fix it if the auto-edits mangle it. The submitter just has to look at the submission after hitting submit one time to see if there was a problem.
It's more of a gut feeling than anything else. I don't know what the normalization rules are, but I think we mostly notice the normalizer when it mangles something [1], and not when it's just quietly humming along. Someone with more spare time could probably figure out a dataset of original to normalized titles and measure that.
[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.
I'm sure this method has evolved and/or been supplanted over the last 15 years, but one thing that struck me reading this is how much the dynamics of unit test coverage have changed in recent history, with AI-generated commits containing 10x as many unit tests (many of them kind of silly and tautological) as in the olden days. Gonna need to update some of those coefficients in their CRAP1 formula... Or maybe test coverage has/will become too noisy a parameter to use at all.
Anecdotally, I’ve found that codebases that enforce code coverage metrics often have worse behavior coverage than ones that don’t.
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.
A measure is only good if I take action on it and in turn make things better. There are a lot of things that are easy to measure, but there is no useful action I should take on the measure.
Yes, but also all too often “useful” is interpreted to mean “moves the metric”. If that metric is merely a proxy for some more tangible outcome then that may not be good enough.
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.
I have a goal to make the codebase at work cargo-crap compliant and enforce it with CI. I let an agent run overnight with it once and the diff touched like 40% of our codebase which is untenable for a single merge. So for now I’m doing it piecemeal as the opportunity presents itself.
The pendulum has swung too far in the direction of class, function, cyclomatic complexity (and here, CRAP) and similar idiotic metrics.
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)_/¯
A while back Hillel Wayne did a talk (whose name I forget) on what empirical evidence on software quality actually says.
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.
>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
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.
The thing is, AI has no idea when an abstraction is good or not.
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.
Wow! Did not expect this blast from the past this morning. I worked with Alberto and Bob at the same startup long ago. Hello to any other Agitators who found this today.
One of the many reasons leadership needs to change, they are now more aggressive towards exploiting their customers, especially in cloud, Kurain is ruining that platform, but the investors like it
>Note: This post is rated PG-13 for use of a mild expletive. If you are likely to be offended by the repeated use a word commonly heard in elementary school playgrounds, please don’t read any further.
Mild as this ironic passive aggressiveness is, can't imagine something like this in modern sterile corporate messaging.
"repeated use" seems to be doing the mitigation work here, though it does seem unusual that someone offended by the repetition would be unoffended by a one-off.
> 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.
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.
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.
For Rust there's https://crates.io/crates/cargo-crap, and for Go there's https://padiazg.github.io/go-crap/
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/
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.
Every indirect call is a conditional branch, where the condition can be arbitrarily far away in time and space.
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.
every bit of humanity went with the motto
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.
Are we just writing tautologies now?