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How to Use NDepend’s Trend Charts

Editorial note: I originally wrote this post for the NDepend blog.  You can check out the original here, at their site.  While you’re there, download NDepend and give the trend chart functionality a try.

Imagine a scene for a moment.  A year earlier, a corporate VP spun up a major software project for his organization.  He brought a slew of his organization’s software developers into the project.  But he also needed to add more staff in the form of contractors.

This strained the budget, so he cut a few corners in terms of team member experience.  The VP reasoned that he could make up for this with strategic use of experienced architects up front.  Those architects would prototype good patterns and make it so the less seasoned contractors could just kind of paint by numbers.  The architects spent a few months doing just that and handed the work off to the contractors.

Fast forward to the present.  Now a consultant sits in a nice office, explaining to a beleaguered VP how they got so far behind schedule.  I can picture this scene quite easily because organizations hire me to be this consultant.  I live this scene over and over again.

NDepend Trend Charts

Concepts like technical debt help quite a bit.  I also enlist various other metaphors to help them understand the issues that they face.  But nothing hits home like a visual.  I’ve described this before.  Generate an actual dependency map of their codebase and show it next to the ones the architects created in Visio, and you invariably see a disconnect.

Today, I’d like to take a look at another visual feature of NDepend: trend charts.  These allow you to see a graph-style representation of your codebase’s properties as a function of time.  And you can customize them a great deal.

NDepend trend charts help you visualize your code

In the scene I painted for you a moment ago, the VP—and the people in his program—feel pain for a specific reason.  They go far too long without reconciling the plan with reality.  I come along a year in and generate a diagram that they should have looked at all along.

Trend charts, by design, help combat that problem.  They allow you to get a feel for strategic properties of a codebase.  But they allow you to see how that property varies with time.  You can take advantage of that in some powerful ways.

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A Look at Some Unit Test Framework Options for .NET

Editorial note: I originally wrote this post for the Stackify blog.  You can check out the original here, at their site.  While you’re there have a look at their offerings, Prefix and Retrace.

If you enjoy the subject of human cognitive biases, you should check out the curse of knowledge.  When dealing with others, we tend to assume they know what we know.  And we do this when no justification for the assumption exists.

Do you fancy a more concrete example?  Take a new job and count how many people bombard you with company jargon and acronyms, knowing full well you just started a few hours ago.  This happens because these folks cannot imagine not knowing these things without expending considerable mental effort.

Why do I lead with this in a post about unit test frameworks?  Well, it seems entirely appropriate to me.  I earn my living as an IT management and strategy consultant, causing me to spend time at many companies helping them improve software development practice.  Because of this, I have occasion to see an awful lot of introductions to unit testing.  And these introductions usually subconsciously assume knowledge of unit testing.

“It’s easy!  Just pick a unit test runner and a coverage tool, and get those setup.  Oh, you’ll also probably want to pick a mocking framework, and here are some helpful Nuget packages.  Anyway, now just write a test.  We’ll start with a calculator class…”

Today, I will do my best to spare you that.  I have some practice with this, since I write a lot, publish courses, and train developers.  So let’s take a look at test frameworks.

What Are Unit Tests?

Thought you’d caught me there, didn’t you?  Don’t worry.  I won’t just assume you know these things.

Let’s start with unit testing in its most basic form, leaving all other subjects aside.  You want to focus on a piece of functionality in your code and test it in isolation.  For example, let’s say that we had the aforementioned Calculator class and that it contained an Add(int, int) method.  Let’s say that you want to write some code to test that method.

No magic there.  I just create a test called “CalculatorTester” and then write a method that instantiates and exercises Calculator.Add().  You could write this knowing nothing about unit testing practice at all.  And, if someone had told you to automate the testing of Calculator.Add(), you may have done this exact thing.

Congratulations.  You have written a unit test.   I say this because it focuses on a method and tests it in isolation.

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CodeIt.Right Rules Explained, Part 6

Editorial note: I originally wrote this post for the SubMain blog.  You can check out the original here, at their site.  While you’re there, follow the tag to have a look at the rest of the series.

Let’s do another installment of the CodeIt.Right Rules Explained series.  Today we have post number six in that series, with three more rules.  As always, I’ll start off with my two personal rules about static analysis guidance, along with an explanation for them.

  • Never implement a suggested fix without knowing what makes it a fix.
  • Never ignore a suggested fix without understanding what makes it a fix.

It may seem as though I play rhetorical games here.  After all, I could just say, “learn the reasoning behind all suggested fixes.”  But I want to underscore the decision you face when confronted with static analysis feedback.  In all cases, you must actively choose to ignore the feedback or address it.  And for both options, you need to understand the logic behind the suggestions.

In that spirit, I’ll offer up explanations for our three rules without further ado.

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Fixing Your Snarled Dependency Graph

Editorial note: I originally wrote this post for the NDepend blog.  You can check out the original here, at their site.  While you’re there, have a look at NDepend’s features for helping you visualize your codebase.

I’ve written before about making use of NDepend’s dependency graph.  Well, indirectly, anyway.  In that post, I talked about the phenomenon of actual software architecture not matching the pretty diagrams people draw in Visio.  It reminds me of Helmuth von Moltke’s wisdom that no battle plan survives contact with the enemy.

Typically, architects conceive of wondrous, clean, and decoupled systems.  Then they immortalize this pristine architecture in Visio.  Naturally, print outs go up on the wall, and everyone knows what the system should look like.  But somehow, it never actually winds up looking like that.

Architectures of Despair

I think we all know what it winds up looking like.  Or, at least, what it can look like.  Sometimes the actual architecture only misses the mark by a little, around the edges.  But other times, it goes sailing off in the wrong direction, like a disastrous misfire at the archery range.

When this happens, we have metaphors for the result.  Work in the industry long enough, and you’ll hold your nose and describe a codebase as a big ball of mud.  You might also hear descriptors involving tangled Christmas tree lights or spaghetti code.  Maybe you’ll hear about a bramble bush or something.

The specific image varies, but the properties do not.  All of them describe something snarled, difficult to separate, and unpleasant to work with.  They indicate complexity without intent or purpose.  And when that happens, deadlines slip and defects proliferate.  Oh, and the people working in the codebase become miserable, now regarding those Visio diagrams as cruel jokes.

All of this stems from a core problem: a tangled dependency graph.

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Why NDepend Uses Google’s Page Rank

Editorial note: I originally wrote this post for the NDepend blog.  You can check out the original here, at their site.  While you’re there, have a look at type rank and all of the other metrics that NDepend will show you about your code.

I remember my early days of blogging as sort of a comedy of errors.  Oh, don’t get me wrong.  I don’t think those early posts were terrible, since I’d always written a lot.  Rather, I knew very little about everything besides the writing.  For example, I initially thought link spammers were just somewhat daft blog commenters.  I stumbled through various mistakes and learned the art of blogging in fits and starts.  This included my discovery of something called page rank.

Page rank had a relatively involved calculation, but that didn’t interest me at the time.  Instead, I found myself dazzled by some gamification.  Sites like this one would take your domain and a captcha as input and spit out a score from 0 to 10 as output.  That simply, they turned my blogging world upside down.  I now had a score to chase and a means of comparing myself against others.  And I vaguely understood that getting more inbound links would increase my page rank score.

Of course, as an introvert, I struggle with outgoing self-promotion.  Cold outreach to people to see if they’d link to me never seriously occurred to me.  Instead, I reasoned that I would play the long game.  Write enough posts, and the shares start to come.  And then when the shares come, so too will the links.  So I watched my page rank inch slowly upward over time.

The Decline of Page Rank

My page rank ticked upward until one day it didn’t anymore.  Turns out, Google slowly killed it over the course of a number of years.  Ten months passed between its penultimate update and its final one.  So there I stood (metaphorically), waiting for a boost to my rank that would never come.

But why did Google kill page rank?  Wouldn’t such an easily digestible construct continue to help people?  Well, sort of.  Unfortunately, it disproportionately helped the wrong sort of people.

The Google founders developed the concept during their time at Stanford.  Conceptually, the page rank algorithm regards a link from site A to site B as a “vote” for site B, by site A.  But not all pages get to “vote” equally.  The higher a rank the page has, the more worthwhile its vote, creating a conceptual feedback loop.

On the surface, this sounds great, and, in many ways, it was.  As you can imagine, a site with a ton of inbound links, like a government study or a news outlet, would accumulate a great deal of rank.  Since employees would carefully curate such sites, you could put a lot of stock in a site to which they linked (and search engines did).  So in theory, you have a democratized system in which the sites best regarded by the public had the best rank.

But in this theory, no link spammers existed.  If you wanted good page rank, you could produce high quality, popular content.  Or you could pay some shady outfit to carpet bomb blog comment sections with links to your site.  Because of this fatal flaw, page rank eventually dwindled to obscurity.

A Useful Reappropriation of Page Rank

For clarity, understand that Google (probably) still uses some incarnation of this scheme.  But they no longer update the easily consumed public version of it.  They now use it as only one of many factors in what they display in response to searches.  The heyday of comparing page rank scores for sites has come and gone.  But that doesn’t mean we can’t use it elsewhere, and to great efficacy.

For instance, consider applying this to codebases.  Instead of a situation where website A links to website B, imagine a situation where type A refers directly to type B.  Now, imagine your codebase as a (hopefully acyclic) directed graph with edges and nodes.  You start to have an interesting vehicle for reasoning about your codebase.

What would a high rank mean in this context?  Well, relatively high rank for a type would mean that other types tended to refer to it at a high rate.  Types with relatively low (or zero) rank would take no dependencies, existing at the edge of your code.  And the types with the highest rank?  These would be types used by other types with high rank.

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