Jake Scruggs

I released metric_fu 0.9.0 last night and just look at all the super cool changes since 0.8.0: Flay task finds duplication and structural similarities in your project Reek identifies common code smells Roodi finds mistakes and troublesome code A configuration class so you can configure like the big boys Standardization of the reports look and feel Increased RSpec coverage Source control type is auto-detected for Churn task Under the hood refactoring to make adding new metrics reports easy Of course it still does all the old stuff like 2 different types of complexity reports, code churn analysis, code coverage, and good old Rails stats. Set up a Cruise Control task to generate metrics reports every day and then you pinpoint the exact moment your project went to hell (or possibly avoid that fate -- your call). Check out the project home page at: http://metric-fu.rubyforge.org/ The best part about this whole thing is that almost everything added since release 0.8 has been added by the...

I just released version 0.7.6 of metric_fu and there's all sorts of new stuff: First, MetricFu is now a Ruby gem on GitHub at: http://github.com/jscruggs/metric_fu Also, the Flog task can now flog any set of directories you like. Just put this into your Rakefile: MetricFu::DIRECTORIES_TO_FLOG = ['cms/app', 'cms/lib'] And, the Flog reports are much nicer: The Flog report generator now looks at a MD5 hash of the files its examining to determine if they files need to be re-Flogged -- which saves time. The Saikuro report generator can also look at custom directories like so: MetricFu::SAIKURO_OPTIONS = {"--input_directory" => '"cms/app | cms/lib"'} MetricFu started its life as a Rails Plugin, but I'm trying to turn it into a flexible gem that can be used in any Ruby application (but still have defaults that enable easy use in a Rails project). Eventually I'd like to create something that could tell you about methods that have hig...

First, I'd to extend a big thanks to Joe and Jim for giving me a ride to the conference today. The two tutorials I decided to attend today were: "The Advanced ActiveRecord Workshop" with Gregg Pollack & Jason Seifer (the Rails Envy Guys) and "The Ins and Outs of Ruby I/O" with James Edward Gray II and Gregory Brown. Both of which were excellent. The Advanced ActiveRecord Workshop covered: Loading large data sets (the 'ar-extensions' gem helps lots). A gentle reminder to properly index your freakin' database. How Rails optimizes multiple :includes in finds and how that goes to hell if you combine lots of :includes with :conditions The super awesome Named Scopes (which you need to look up right now, if you haven't already because it's not just about defining custom finders -- it's about defining custom finders that you can chain together and get optimized queries) Polymorphic Associations, Single Table Inheritance, Dirty Fields, Associ...

If you're going to be in the Chicago area on or about Saturday, September 20th, you can see me give my "Using Metrics to take a Hard Look at Your Code" talk at the Windy City Rails Conference . David Heinemeier Hansson, David Chelimsky, and Noel Rappin have been announced as speakers and I'll be joining them for a interesting day of Rails related goodness. Here's the abstract: It's an interesting fact of human nature that you can't do something every day and not secretly suspect that you're good at it. Which goes a long way toward explaining why everyone thinks they write fine code. To combat this self-delusion you can use metrics to take a hard look at your application. This talk will discuss the ways in which you can measure how good your Rails project really is. And how to fix the bad parts. Using a daily metrics build (run every day by CruiseControl.rb) you can compile a 'hit-list' of the worst methods in your application. Then your team ...

I've been using Flog to measure the complexity of my Ruby code and I was wondering how others interpret Flog numbers. I'll go first with my opinions: Score of Means 0-10 Awesome 11-20 Good enough 21-40 Might need refactoring 41-60 Possible to justify 61-100 Danger 100-200 Whoop, whoop, whoop 200 + Someone please think of the children (note: these are scores for an individual method) The 20 - 60 range is interesting to me. Sometimes the complexity of what you're doing will justify the score and other times the method is in desperate need of refactoring. The highest Flog score I've seen in the wild was in the 300's. What's you're personal high and what do you think of my rankings? In other news, I've redesigned the look of my blog so if you only see my post through a reader you might want to stop on by, take a look, and tell me what you think.

Every time I create a new rails project I usually put off writing tasks to analyze the code's quality 'cause it takes time and time is, you know, finite. So I've decided to extract some code into a rails plugin which I call metric_fu. It's a bunch of rake tasks that produce reports on code coverage (using Rcov ), cyclomatic complexity (using Saikuro ), flog scores (using Flog ), and rails stats (using 'rake stats'). It knows if it's being run inside a CruiseControl.rb build and puts the output in the Custom Build Artifacts folder so when you view a build you see this: The coverage report is your standard rcov report: Flog output is thrown into an html file: At the end metric_fu calculates the average flog score per method: You might want to check out my previous posts on what to do with a Flog report: The Method Hit List and When You Should Ignore Metrics Saikuro's output is the same as always: (I changed the warning and error levels for this pic -...

Our team has been doing a bang up job of reducing our complexity through our hit list (All of our methods are ranked by Flog score and we spend some part of every iteration picking the worst methods and trying to refactor them.). But sometimes we run into a situation where we prefer a high Flog score to a low one. For instance, this bit 'o code: def is_something?(x, y) x.foo == y.foo && x.bar == y.bar && x.blah == y.blah && # and 10 more lines of the same end which had a Flog score in the 60s. We could have changed it to this: def is_something?(x, y) ["foo", "bar", "blah", # and so on ].inject(true) do | a, b | a && x.send(b) == y.send(b) end end Which gave a us a much lower score. However that first method is crazy simple. Merely glance at it and you know what it's doing. The 'less complex' method? Even an experienced ruby dev would need a moment or two. As for a new dev... I can remem...

In a previous post I talked about how my team has a daily metrics build that reports on our code quality. One of the things we measure is the Flog score of our methods (Flog is a ruby program that evaluates the complexity of your methods). We have a standing developer task to look at the top five worst methods in our application and reduce their complexity. Every iteration we try to spend some time looking at a few developer tasks, which are things that don't provide any direct business value but do make our code better, automate some manual process, or fix some technical problem not tied to a bug. Yesterday Chirdeep and I refactored the worst method in our app and while it wasn't easy it produced a number of benefits: First, about halfway though the refactoring we came upon some twisted logic that seemed as if it could never evaluate to false. After pulling in Ali for some consultation, we realized that indeed it would always be true and therefore was a bug. We fixed i...

Everyone thinks they write good code -- it's just part of human nature. You can't do something every day and not secretly suspect that you're good at it. Self-delusion is a powerful thing so you need to use metrics to take a hard look at your code. On my current project, we've just added a daily metrics build (run every day at midnight by CruiseControl.rb ) that takes a look at our code in three ways: Code coverage with Rcov Cyclomatic complexity with Saikuro And um..., Flogging with Flog Rcov is a code coverage tool that can be used with Rails Rcov to add a bunch of rake tasks to your build so you can figure out which lines of code are run by your tests... and which are not. Saikuro computes cyclomatic complexity which "measures the number of linearly independent paths through a program's source code." Methods with more paths are harder to understand/debug/modify. And Flog is cyclomatic complexity with an attitude. It scores ruby methods with an ...