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How to Analyze a Static Analyzer

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, take a look around at some of the other posts, and sign up for the RSS feed, if you’re so inclined.

First things first.  I really wanted to call this post, “who will analyze the analyzer,” because I fancy myself clever.  This title would have mirrored the relatively famous Latin question from Satires, “who will guard the guards themselves?”  But I suspect that the confusion I’d cause with that title would outweigh any appreciation of my cleverness.

So, without any literary references whatsoever, I’ll talk about static analyzers.  More specifically, I’ll talk about how you should analyze them to determine fitness for your purpose.

Before I dive into that, however, let’s do a quick refresher on the definition of static analyzer.  This stack overflow question nails it pretty well, right at the beginning of the accepted answer.

Analyzing code without executing it. Generally used to find bugs or ensure conformance to coding guidelines.

Succinctly put, Aaron, and just so.  Most of what we do with code tends to be dynamic analysis.  Whether through automated tests or manual running of the program, we fire it up and see what happens.  Static analyzers, on the other hand, look at the code and use it to make deductions.  These include both deductions about runtime behavior and about the codebase itself.

What’s Your Goal?

Why rehash the definition?  Well, because I want to underscore the point that you can do many different things with static analyzers.  Even if you just think of them as “that thing that complains at me about the Microsoft guidelines,” they cover a whole lot more ground.

As such, your first step in sizing up the field involves setting your own goals.  What do you want out of the tool?  Some of them focus exclusively on code quality.  Others target specific concerns, such as behavioral correctness or security.  Still others simply offer so-called “linting.”  Some do a mix of many things.

Lay out your goals and expectations.  Once you’ve done that, you will find that you’ve narrowed the field considerably.  From there, you can proceed with a more apples to apples comparison.

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Computing Technical Debt with NDepend

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, take a look at the newest version of NDepend and its options for helping you quantify tech debt.

For years, I have struggled to articulate technical debt to non-technical stakeholders.  This struggle says something, given that technical debt makes an excellent metaphor in and of itself.

The concept explains that you incur a price for taking quality shortcuts in the code to get done quickly.  But you don’t just pay for those shortcuts with more work later — you accrue interest.  Save yourself an hour today with some copy pasta, and you’ll eventually pay for that decisions with many hours down the road.

So I say to interested, non-technical parties, “think of these shortcuts today as decisions upon which you pay interest down the line.”  They typically squint at me a little and say, “yeah, I get it.”  But I generally don’t think they get it.  At least, not fully.

Lack of Concreteness

I think the reason for this tends to come from a lack of actual units.  As a counterexample, think of explaining an auto loan to someone.  “I’m going to loan you $30,000 to buy a car.  With sales tax and interest factored in, you’ll pay me back over a 5 year period, and you’ll pay me about $36,000 in total.”  Explained this way to a consumer, they get it.  “Oh, I see.  It’ll cost me about $6,000 if I want you to come up with that much cash on my behalf.”  They can make an informed value decision.

But that falls flat for a project manager in a codebase.  “Oh man, you don’t want us to squeeze this in by Friday.  We’ll have to do terrible, unspeakable things in the code!  We’ll create so much tech debt.”

“Uh, okay.  That sounds ominous.  What’s the cost?”

“What do you mean?  There’s tech debt!  It’ll be worse later when we fix it than if we do it correctly the first time.”

“Right, but how much worse?  How much more time?”

“Well, you can’t exactly put a number to it, but much worse!”

And so and and so forth.  I imagine that anyone reading can recall similar conversations from one end or the other (or maybe even both).  Technical debt provides a phenomenal metaphor in the abstract.  But when it comes to specifics, it tends to fizzle a bit.

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Exploring the Tech Debt In Your Codebase

Editorial note: I originally wrote this for the NDepend blog.  You can check out the original here, at their site.  While you’re there, check out the tech debt features in the newest version of NDepend.

Recently, I posted about how the new version of NDepend lets you compute tech debt.  In that post, I learned that I had earned a “B” out of the box.  With 40 minutes of time investment, I could make that an “A.”  Not too shabby!

In that same post, I also talked about the various settings in and around “debt settings.”  With debt settings, you can change units of debt (time, money), thresholds, and assumptions of working capacity.  For folks at the intersection of tech and business, this provides an invaluable way to communicate with the business.

But I really just scratched the surface with that mention.  You’re probably wondering what this looks like in more detail.  How does this interact with the NDepend features you already know and love?  Well, today, I’d like to take a look at just that.

To start, let’s look at the queries and rules explorer in some detail.

Introducing Quality Gates

Take a look at this screenshot, and you’ll notice some renamed entries, some new entries, and some familiar ones.

In the past, “Code Smells” and “Code Regressions” had the names “Code Quality” and “Code Quality Regression,” respectively.  With that resolved, the true newcomers sit on top: Quality Gates and Hot Spots.  Let’s talk about quality gates.

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Learning with Hands on Projects

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, take a look around and download NDepend to try it out.

If you want a surefire way to make money, look for enormous disparity between demand and supply.  As software developers, we understand this implicitly.  When we open our inboxes in the morning, we see vacuous missives from recruiters.  “Hey, dudebro, we need a JavaScript ninja-rockstar like you!”

You don’t tend to see vaguely patronizing, unflinchingly desperate requests like that unless you sit on some kind of goldmine.  They approach us the way one might approach a mischevious toddler holding a winning lottery ticket.  And, of course, anyone would expect that with wildly disproportionate supply and demand.

But, for us, this transcends just writing the code and oozes into learning about it.  Like baseball teams playing the long game, companies would rather grow their own talent than shell out for high-priced free agents.  And so learning about software might just prove more of a growth industry than writing it.

Look at wildly successful industry player Pluralsight.  It has built a benevolent commercial empire on the simple promise of democratized learning about technical pursuits.  Then you have a host of fast followers, an army of boot camp providers, and endless how-to blogs.  Sometimes it seem as though a gigantic wave of pressure pushes us all toward writing a bit of code.

The Learning Tools in Your Tool Belt

Let’s say that you’re convinced.  You see the money to be made, or you simply feel drawn to the profession.  You want to get involved, but don’t quite know where to start.  What sorts of learning can developers avail themselves of?

While I won’t call this an exhaustive list, I can offer some general categories of learning.  First, consider theoretical, passive learning.  You crack open some book called, “Principles of Modern Web Development” and get to reading.  Second, you have classroom-style learning.  An instructor leads your lessons, curates information, and engages you in Q&A.  And, third, you have hands on learning.  With this kind of learning, you put actual concepts into practice.

Understand that I do not consider these mutually exclusive by any means.  Any serious leaning plan worth its salt is going to incorporate elements of all of these (and probably other form categories that escape me at the moment).  But understanding the flavors will inform the rest of this post.

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The One Thing Every Company Can Do to Reduce Technical Debt

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, take a look at the technical debt functionality in the latest version of NDepend.

The idea of technical debt has become ubiquitous in our industry.  It started as a metaphor to help business stakeholders understand the compounding cost of shortcuts in the code.  Then, from there, it grew to define perhaps the foundational of tradeoffs in the tech world.

You’d find yourself hard pressed, these days, to find a software shop that has never heard of tech debt.  It seems that just about everyone can talk in the abstract about dragons looming in their code, portending an eventual reckoning.  “We need to do something about our tech debt,” has become the rallying cry for “we’re running before we walk.”

As with its fiscal counterpart, when all other factors equal, having less tech debt is better than having more.  Technical debt creates drag on the pace of new feature deliver until someone ‘repays’ it.  And so shops constantly grapple with the question, “how can we reduce our tech debt?”

I could easily write a post where I listed the 3 or 5 or 13 or whatever ways to reduce tech debt.  First, I’d tell you to reduce problematic coupling.  Then, I’d tell you to stop it with the global variables.  You get the idea.

But today, I want to do something a bit different.  I want to talk about the one thing that every company can do to reduce tech debt.  I consider it to be sort of a step zero.

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