Stories about Software


Static Analysis and The Other Kind of False Positives

Editorial Note: I originally wrote this post for the NDepend blog.  You can check out the original here, at the NDepend site.  If you’re a fan of static analysis tools, do yourself a favor and take a look at the NDepend offering while you’re there.

A common complaint and source of resistance to the adoption of static analysis is the idea of false positives.  And I can understand this.  It requires only one case of running a tool on your codebase and seeing 27,834 warnings to color your view on such things forever.

There are any number of ways to react to such a state of affairs, though there are two that I commonly see.  These are as follows.

  1. Sheepish, rueful acknowledgement: “yeah, we’re pretty hopeless…”
  2. Defensive, indignant resistance: “look, we’re not perfect, but any tool that says our code is this bad is nitpicking to an insane degree.”

In either case, the idea of false positives carries the day.  With the first reaction, the team assumes that the tool’s results are, by and large, too advanced to be of interest.  In the second case, the team assumes that the results are too fussy.  In both of these, and in the case of other varying reactions as well, the tool is providing more information than the team wants or can use at the moment.  “False positive” becomes less a matter of “the tool detected what it calls a violation but the tool is wrong” and more a matter of “the tool accurately detected a violation, but I don’t care right now.”  (Of course, the former can happen as well, but the latter seems more frequently to serve as a barrier to adoption and what I’m interested in discussing today).

Is this a reason to skip the tool altogether?  Of course not.  And, when put that way, I doubt many people would argue that it is.  But that doesn’t stop people form hesitating or procrastinating when it comes to adoption.  After all, no one is thinking, “I want to add 27,834 things to the team’s to do list.”  Nor should they — that’s clearly not a good outcome.


With that in mind, let’s take a look at some common sources of false positives and the ways to address them.  How can you ratchet up the signal to noise ratio of a static analysis tool so that is valuable, rather than daunting?

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Logging for Continuous Integration

Editorial Note: I originally wrote this post for the LogEntries blog.  Check out the original here, at their site.  While you’re there, take a look at the product offering, which includes storage, aggregation, and sophisticated search of your log information.

If you look at the title of this post, you’re probably thinking to yourself, “huh, that’s never really come up.”  Of course, it’s possible that you’re not.  But, in my travels as a consultant helping dev teams with practice and gap analysis, I’ve never had anyone ask me, “what do you recommend in terms of a logging solution for continuous integration?”

But hey, this is an easily solved problem, right?  After all, continuous integration means Jenkins, and Jenkins has an application log.  Perhaps that’s why no one is asking about it!  Now, all that’s left is to sit back and bask in the glow of every compiler warning your application has ever generated since the dawn of time.


What Actually Is Continuous Integration?

Now, I know what you’re thinking.  TeamCity is another continuous integration tool, and it also has logs.  Or what about TFS or Bamboo?  Jenkins doesn’t have sole possession of the continuous integration mind share.  There are any number of products designed for this purpose.

And thus we arrive at a popular misconception.

Continuous integration is not Jenkins.  It’s not Team City.  It’s not TFS or Bamboo.  And it’s also not the non-empty set that results from choosing one of the tools.  Continuous integration is a practice, not a tool.  And it’s actually a simple practice at that.

If you go back to basics via Wikipedia, you’ll find this definition.

Continuous integration (CI) is the practice, in software engineering, of merging all developer working copies to a shared mainline several times a day.

Notice it does not say, “CI is where you hook your Github account up to Jenkins.”  There is no mention of any particular tool; it just describes the idea of developers’ source code never getting very far out of sync.  Cringe (appropriately), but you could just as easily achieve this by having developers collaborate using Notepad to edit source files housed on a shared Dropbox account.

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Managing Risk via Static Analysis

Editorial Note: I originally wrote this post for the NDepend blog.  Check out the original here, at their site.  While you’re there, take a look at the features of NDepend and download a trial, if you’re so inclined.

When software developer talk about static analysis, it’s often in the context of craft improvement.  Ask most developers in a group about static analysis tools and you’ll get a range of responses, many of which will be fueled by some degree of passion, resulting from past experience.  From here, the conversation will tend to dive into the weeds for any non-technical stakeholder that might be listening; if you’re not a programmer, you probably don’t have much of an opinion as to whether or not cyclomatic complexity of 5 is acceptable for a method.

As a result, static analysis tends to get pegged heavily as a purely a matter of shop.  The topic tends to be pretty opaque to management because developers present it to them in terms of “this will make us better and the code better.”  Management that trusts the developers will tend to agree to the purchase with a sentiment of, “okay, I’ll take your word for it.”  Management that is more skeptical says, “maybe next year if our numbers are good.”

I find this to be a shame because it’s a lost opportunity, even when management agrees.

Static analysis most certainly is a way for developers to improve their craft and their codebases.  But, in the hands of an architect or team lead that truly understands the business and works well with management, static analysis can be an excellent tool for managers, even if the use has to be a management-architect team effort.

How so?  Well, there are a lot of ways, but the one I’d like to mention today is risk management.  As the title would imply, managing risk tends to be the purview of people whose title is manager.  Sure, the developers have responsibility for this, but their primary charter is to build stuff — management exists specifically to engage in planning activities, including the crucial concern of risk management.

How does this work?  Well, I’ll show you, and I’ll do it by explaining the sort of highly technical things that static analysis could catch in highly non-technical and readable ways.  These are all going to be operational risks — static analysis can’t help you if you’re building the wrong product or badly under-staffing your projects.  But it can help you avoid landmines in your software.  If you’re a manager, allow me, for the moment, to serve as your “business-savvy architect.”


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Is Your Source Control Usage Conducive to Code Review?

Editorial Note: I originally wrote this post for the SmartBear blog.  Head over to their site and check out the original.  While you’re there, have a look around at posts by some other authors as well.

I can think back to times in my career that the source control that I was using (or not using) made me a cranky, unhappy human being.  Years and years ago, there was the time that a coworker accidentally left all of the files in the codebase checked out through Visual SourceSafe and went on vacation.  I distinctly remember enlisting a sysadmin and the two of us going into the source control server with admin credentials and hacking at settings until we could undo that and I could work.  You see, Visual SourceSafe employed a pessimistic locking strategy by which his checkout meant I couldn’t do anything with the code.

There was also the time, a few years later, when I was suffering through a project that used Rational Clear Case.  On a normal day, delivering code to the official branch or stream or whatever took half an hour.  If I had to work from home, it took all morning.

Angry guy smshing computer

And then there was the time that I was switched onto a project with no source control at all.  The C source code was stored on a production server — a production server that controlled physical machinery in the real world.  To “check things in,” you would modify the C code, turn off the physical machine, load the modified kernel modules, turn the machine on, and then revert real quick if things started blowing up.  I’m not kidding.  This was the commit/rollback strategy when I arrived (I did actually migrate this).

Tools Affect Behavior

These things make for fun war stories, but they also serve to illustrate how source control dictates behavior.  With Visual SourceSafe, we implemented some kind of out of band email protocol to remind people to check in.  With Rational Clear Case, I implemented a homegrown SVN for day to day version control and delivered/integrated only a few times per month.  With the machine server, there was extensive historical commenting in every single source file.  These tools spur you toward behaviors, and, in these cases, toward wasteful or bad behaviors.

For the examples I listed, I was steered toward useless process, steered away from continuous integration, and steered toward neurotic documentation.  But the steering can apply to almost anything, and that includes having a healthy code review process.

There have been studies conducted that demonstrate the importance of code review.  It is uniquely effective when it comes to catching defects earlier than later, and it promotes collective code ownership, thus reducing “bus factor.”  I could go on, but let’s take it as axiomatic in this post that you want to do it.

Does your source control situation make it easy for you to conduct code reviews?  Or does it discourage you, making life tough if you do them, and thus making you less likely to do them.  If it’s the latter, that’s not a good situation.

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Easy to Miss Code Smells

Editorial Note: I originally wrote this post for the NDepend blog.  Please head over to the site and check out the original.  There’s a lot of great content over there, and, if you’re not familiar with NDepend, download a trial and try it out.

The concept of a code smell is, perhaps, one of the most evocative in our profession.  The name itself has a levity factor to it, conjuring a mental image of one’s coworkers writing code so bad that it actually emits a foul odor.  But the metaphor has a certain utility as well in the “where there’s smoke, there may be fire” sense.

In case you’re not familiar, a code smell is an observable feature of the code (the smoke) that often belies a deeper existing problem (the fire).  When you say that a code smell exists, what you’re communicating is “you may be justified here, but I’m skeptical – in my experience this is probably a design flaw.”

Smelly computer

Of course, accusing code of having a smell is only slightly less incendiary to the author than accusing code of being flat out bad.  Them’s fightin’ words, as they say.  But, for all the arguments and all of the righteous indignation that code smell accusations have generated over the years, their usefulness is undeniable.

No doubt you’ve heard of some of the most common and easiest to visualize code smells.  The God Class, Primitive Obsession, and Inappropriate Intimacy all come to mind.  These indicate, respectively a class in your code base doing way too much, a tendency to use primitive types when you should take advantage of classes, and a module or class that breaks encapsulation by knowing too many details about another.  The combination of their visual memorability and their wisdom has prodded us over the years to break things down, to create cohesive objects, and to preserve encapsulation.

I would argue, however, that there are many more code smells out there than the big, iconic ones that get a lot of attention.  I’d like today to discuss a few that I don’t think are as commonly known.  I’ll make the case for why, once you’ve mastered avoiding the well-known ones, you should watch for these as well.

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