Often I come across what I like to call “Misplaced Code”, that is, code which should be refactored to a specific, independent concern rather than mistakenly being defined in an incorrect context.

For instance, consider the following example to get a better idea of what I mean:

Taking the above example into a broader context, it is quite common to see code such as this scattered throughout a codebase; particularly in the context of view concerns. At best this could become hard to maintain and, at worst, it will result in unexpected bugs down the road. In most cases (as in the above example) the actual code itself is not necessarily bad, however it is the context in which it is placed which is what I would like to highlight as it will almost certainly cause technical debt to some extent.

Considering the above example, should code such as this become redundantly implemented throughout a codebase it is quite easy to see how it can become a maintenance issue as, something as simple as a change to a hostname would require multiple refactorings. A much more appropriate solution would be to encapsulate this logic within a specific class whose purpose is to provide a facility from which this information can be determined. In this manner unnecessary redundancy would be eliminated (as well as risk) and valuable development time would be regained as the code would need only be tested and written once – in one place.

So again, using the above example, this could be refactored to a specific API and client code would leverage the API as in the following:

This may appear quite straightforward, however, I have seen examples (this one in particular) in numerous projects over the years and it is worth pointing out. Always take the context to which code is placed into consideration and you will reap the maintenance benefits in the long run.

Throughout my career I have always been drawn to books which provide a practical way of thinking about software. Books of this nature have an emphasis on the fundamental principles which apply to all software engineering disciplines; and form much of the basis of the Agile methodologies many of us have come to appreciate.

From time to time I find myself going back to the seminal text The Pragmatic Programmer as it provides a great source of what should be kept in mind from day to day. I thought I would share some tips from the book with my readers that I find of particular value, and would be happy to explain them at length; in the context of real world software challenges.

Care About Your Craft
Why spend your life developing software unless you care about doing it well?

Provide Options, Don’t Make Lame Excuses
Instead of excuses, provide options. Don’t say it can’t be done; explain what can be done.

Critically Analyze What You Read and Hear
Don’t be swayed by vendors, media hype, or dogma. Analyze information in terms of you and your project.

Design with Contracts
Use contracts to document and verify that code does no more and no less than it claims to do.

Refactor Early, Refactor Often
Just as you might weed and rearrange a garden, rewrite, rework, and re-architect code when it needs it. Fix the root of the problem.

Costly Tools Don’t Produce Better Designs
Beware of vendor hype, industry dogma, and the aura of the price tag. Judge tools on their merits.

Start When You’re Ready
You’ve been building experience all your life. Don’t ignore niggling doubts.

Don’t Be a Slave to Formal Methods
Don’t blindly adopt any technique without putting it into the context of your development practices and capabilities.

It’s Both What You Say and the Way You Say It
There’s no point in having great ideas if you don’t communicate them effectively.

You Can’t Write Perfect Software
Software can’t be perfect. Protect your code and users from the inevitable errors.

Build Documentation In, Don’t Bolt It On
Documentation created separately from code is less likely to be correct and up to date.

Put Abstractions in Code, Details in Metadata
Program for the general case, and put the specifics outside the compiled code base.

Work with a User to Think Like a User
It’s the best way to gain insight into how the system will really be used.

Program Close to the Problem Domain
Design and code in your user’s language.

Use a Project Glossary
Create and maintain a single source of all the specific terms and vocabulary for a project.

Be a Catalyst for Change
You can’t force change on people. Instead, show them how the future might be and help them participate in creating it.

DRY – Don’t Repeat Yourself
Every piece of knowledge must have a single, unambiguous, authoritative representation within a system.

Eliminate Effects Between Unrelated Things
Design components that are self-contained, independent, and have a single, well-defined purpose.

Iterate the Schedule with the Code
Use experience you gain as you implement to refine the project time scales.

Use the Power of Command Shells
Use the shell when graphical user interfaces don’t cut it.

Don’t Panic When Debugging
Take a deep breath and THINK! about what could be causing the bug.

Don’t Assume It – Prove It
Prove your assumptions in the actual environment—with real data and boundary conditions.

Write Code That Writes Code
Code generators increase your productivity and help avoid duplication.

Test Your Software, or Your Users Will
Test ruthlessly. Don’t make your users find bugs for you.

Don’t Gather Requirements—Dig for Them
Requirements rarely lie on the surface. They’re buried deep beneath layers of assumptions, misconceptions, and politics.

Abstractions Live Longer than Details
Invest in the abstraction, not the implementation. Abstractions can survive the barrage of changes from different implementations and new technologies.

Don’t Think Outside the Box—Find the Box
When faced with an impossible problem, identify the real constraints. Ask yourself: “Does it have to be done this way? Does it have to be done at all?”;

Some Things Are Better Done than Described
Don’t fall into the specification spiral—at some point you need to start coding.

Don’t Use Manual Procedures
A shell script or batch file will execute the same instructions, in the same order, time after time.

Test State Coverage, Not Code Coverage
Identify and test significant program states. Just testing lines of code isn’t enough.

Gently Exceed Your Users’ Expectations
Come to understand your users’ expectations, then deliver just that little bit more.

Don’t Live with Broken Windows
Fix bad designs, wrong decisions, and poor code when you see them.

Remember the Big Picture
Don’t get so engrossed in the details that you forget to check what’s happening around you.

Make It Easy to Reuse
If it’s easy to reuse, people will. Create an environment that supports reuse.

There Are No Final Decisions
No decision is cast in stone. Instead, consider each as being written in the sand at the beach, and plan for change.

Estimate to Avoid Surprises
Estimate before you start. You’ll spot potential problems up front.

Use a Single Editor Well
The editor should be an extension of your hand; make sure your editor is configurable, extensible, and programmable.

Fix the Problem, Not the Blame
It doesn’t really matter whether the bug is your fault or someone else’s—it is still your problem, and it still needs to be fixed.

“select” Isn’t Broken
It is rare to find a bug in the OS or the compiler, or even a third-party product or library. The bug is most likely in the application.

Learn a Text Manipulation Language
You spend a large part of each day working with text. Why not have the computer do some of it for you?

Use Exceptions for Exceptional Problems
Exceptions can suffer from all the readability and maintainability problems of classic spaghetti code. Reserve exceptions for exceptional things.

Minimize Coupling Between Modules
Avoid coupling by writing “shy” code and applying the Law of Demeter.

Design Using Services
Design in terms of services: independent, concurrent objects behind well-defined, consistent interfaces.

Don’t Program by Coincidence
Rely only on reliable things. Beware of accidental complexity, and don’t confuse a happy coincidence with a purposeful plan.

Organize Teams Around Functionality
Don’t separate designers from coders, testers from data modelers. Build teams the way you build code.

Test Early. Test Often. Test Automatically.
Tests that run with every build are much more effective than test plans that sit on a shelf.

Find Bugs Once
Once a human tester finds a bug, it should be the last time a human tester finds that bug. Automatic tests should check for it from then on.

Sign Your Work
Craftsmen of an earlier age were proud to sign their work. You should be, too.

I suggest keeping these tips, or a subset of them, as well as some of your own, somewhere visible; even if some appear rather obvious, as it will serve as a good general reminder of the things you should always keep in mind.

One of the many welcome additions to the Flex 3.4 SDK is the inclusion of the Vector class. Vectors in particular are especially welcome as they provide compile time type safety over what would otherwise not be available when implementing custom solutions, such as a typed collection.

Essentially, Vectors are just typed Arrays. And while not as robust or powerful, Vectors are similar to Generics in C# and Java. When it is known at design time that a collection will only ever need to work with a single type, Vectors can be utilized to provide type safety and also to allow for significant performance gains over using other collection types in Flex.

I recently wanted to convert quite a few typed Array implementations to Vectors, however, the Arrays were being traversed with an Iterator. In order to reduce the amount of client code which needed to be refactored I simply implemented a Vector specific Iterator implementation.

If you are familiar with Iterator Pattern in general, and the Iterator interface in particular, then usage will prove to be very straight forward. You can use the Vector Iterator to perform standard iterations over a Vector. Below is an example of a typical client implementation:

Using an Iterator with a Vector ensures only a linear search can be performed, which proves useful with Vectors as they are dense Arrays. However, one consideration that must be made when using an Iterator with a Vector is that you loose type safety when accessing items in the Vector via iterator.next(). It is because of this I would suggest only using Iterator’s with Vectors to support backwards compatibility when refactoring existing Arrays which are being used with existing Iterators.

The VectorIterator and it’s associated test are available below:
VectorIterator
VectorIteratorTest

On occasion developers may find a need to quickly measure the time it takes for a request to a remote service to return a response back to the client without the need to employ an automated testing tool to perform the instrumentation. This information can prove quite valuable for performing application diagnostics on the client and, when measured in terms of code execution, monitoring at the execution level will always be a bit more precise than that which can be measured by using a Network proxy alone, such as Charles or Fiddler, etc.

Obviously there are numerous solutions which can be implemented to monitor the elapsed time of a service invocation, however it was my goal to provide a unified solution which could easily be implemented into existing client code without significant refactorings being required.

In order to achieve this I first needed to consider what the typical implementation of a service invocation is in order to isolate the
commonality. From there it is only a matter of determining a solution that meets the objective in the most non intrusive manner possible.

To begin let us consider what a “typical” service invocation might look like for the three most common services available in the Flex Framework; HTTPService, RemoteObject and WebService.

Based on the 3 above implementations we can deduce that the common API used when performing a service invocation is AsyncToken. So to provide a unified solution for all three common Services we could either extend AsyncToken or provider an API which wraps AsyncToken. For my needs I chose to implement an API which simply monitors an AsyncToken from which the duration of an invocation can be determined, thus I wrote an RPCDiagnostics API which can be “plugged” into an AsyncToken client implementation.

RPCDiagnostics provides basic performance analysis of a Remote Procedure Call by providing a message which displays information about the operation duration via a standard trace call. In addition, an event listener of type RPCDiagnosticsEvent can be added to facilitate custom diagnostics and Logging.

RPCDiagnostics can easily be implemented as an addition to an existing AsyncToken or in place of an AsyncToken. The following examples demonstrate both implementations.

Implementing RPCDiagnostics onto an existing AsyncToken:

Implementing RPCDiagnostics in place of an AsyncToken:

Implementing a listener to an RPCDiagnostics instance:

The RPCDiagnostics API and dependencies can be downloaded via the Open Source AS3 APIs page or from the below links:

RPCDiagnostics
RPCDiagnosticsEvent
Execution