Coding Guidelines

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The coding guidelines relate to source code, implementation, and to the artifacts and infrastructure which should be part of development. Of course there is much overlap between process and coding.

Use automated builds.

Every project should be easy to build from checkout with an automated, batch build process. For C++ applications, SCons is recommended for its existing support of EOL tools, libraries, and common third-party components. Java projects might use ant or Eclipse. Whatever the tool, building and testing should be turnkey. [See the Joel Test, step 2.]

Use automated testing.

Use a testing framework like boost.test, cppunit, JUnit, cxxunit, or whatever, but integrate the testing into the automated build framework so it is easy to run the tests and detemine either pass or failure, without manually inspecting or comparing the output. A script which runs the program and tests for basic output is still helpful, sometimes called a smoke test. The more automated the testing, the more the computer can help by running the tests continuously on every change, often while further development continues simultaneously. One of the ideas behind the test-driven development process is that writing tests also helps the developer think clearly about the scope and the requirements, before writing the code.

Use continuous integration testing.

Take advantage of buildbot or other tools to run builds and tests whenever code is committed, potentially on multiple platforms, without doing it manually.

Use compilers effectively.

Most compilers can warn about questionable code constructs, such as missing return statements, unreached code, missing cases, unsafe type conversions. These warnings should always be enabled, and the code should compile cleanly without any warnings. This also works well with automated testing and continuous integration, since the compile step will report warnings whenever suspicious code has been added. When there is a warning, change the code. That way another developer later does not need to wonder whether the warning or the code is correct. GCC has the recommended -Wall and -Wextra options, but it also has the -Weffc+ option, which warns about violations of the style rules in Effective C++ [Meyers].

Very often code quality improves when it must be compiled on different platforms and with different compilers. On Linux, there are compilers available besides GCC, such as Clang from the LLVM project and the Intel compiler, and these may find and report different problems in the source code.

Use revision control.

There are subversion and git servers already available to use. If any of the guidelines in this document should be an absolute requirement, this is one of them. Beyond just using revision control, there are also good guidelines for commits and commit messages, such as this article On commit messages. [See the Joel Test, step 1.] Here are a couple highlights:

  • Do not mix cosmetic changes with functional changes. It is hard to see from a source code diff what behavior changed if many more source lines differ just because of reformatting or reindenting or renaming.
  • Commit unrelated fixes separately when feasible. That allows individual fixes to be understood separately and also backed out separately.
  • Do not "break the build". The trunk revision should always build without errors so no one has to fix compile problems just to keep working on their own changes. Commit intermediate, build-breaking changes onto a branch.
  • Write a descriptive log message. On many projects the log message will be emailed automatically to other interested persons, so the log message is an easy way to send out a simple notice and explanation of a change.

Use a logging framework.

For the original developer, this may not seem useful at first, but it's value comes for other developers who later have to learn how the software works. Log messages can be valuable clues into which parts of the code are doing what when, and where a problem may be happening.

Also, when software runs remotely, perhaps even autonomously, logs are invaluable because they can be retrieved by logging into the system or by email from the field operator. A good log can give important diagnostics more completely and more accurately than can be relayed over a phone call.

See Logging Frameworks.

Use a consistent style.

There are many coding styles out there, and we will never settle upon just one, but there are some good conventions to follow. The important thing is to pick a style and be consistent. See [RAL], the Google style guide, and [KDE] for other ideas about style. For the record, here is a basic list of good practices in EOL:

  • Differentiate class members from local variables with a naming convention.
  • Use a naming convention for class methods and functions, such as camel case (doThat) or underscores (do_that), and then use the convention consistently.
  • Use descriptive names. Do not abbreviate too much or leave out arbitrary letters just to have a shorter name.
  • Avoid long function definitions.
  • Separate interface and implementation. In C++, the header file often can use forward declarations rather than including other header files, which simplifies dependencies and speeds compilation. When implementations are defined in source modules rather than header files, then implementations can change without forcing clients of the interface to recompile. Consider using the pimpl idiom. For languages like Python and Java which force implementation to be defined with interface, use the language to make public interface explicit. The Python convention is to use leading underscores for private methods.
  • Favor spaces over tabs. Indenting by 8 spaces is excessive, 2 or 4 is adequate.
  • Avoid overcrowding source code. Use spaces around operators.
  • Keep source code lines within 80 columns.
  • Avoid complicating the flow of an algorithm just to optimize it, unless the performance has been measured. The compiler optimizes better than programmers. Premature optimization is the root of all evil, and often it is also the root of all obfuscation.

Facilitate code reuse.

There are many existing software implementations that we can use in EOL software projects. We should take advantage of them to avoid duplicating effort. We should also write our own code to facilitate its reuse in other EOL projects. Sometimes tools can help in searching for and identifying existing code. OpenGrok (internal only) can be used to search almost all of the EOL subversion repositories for specific code symbols or arbitrary text. And of course there is no substitute for just asking around, either in person or on the software engineering mailing lists.

There are many scripts and programs which at first look like one-off tasks, such as data processing specific to a single field project.  We know from experience that usually a very similar task comes along, so software gets copied and slightly modified.  Code reuse implies avoiding these copies.  Instead, make the scripts modular and configurable so code does not need to be copied in whole, but instead code can be shared and maintained for multiple projects and similar tasks.  Consolidate similar code into functions, consolidate functions into libraries and packages, share a single code base instead of duplicating it.

This is similar to the coding maxim Don't Repeat Yourself (DRY).

Deploy tests and logging as part of production software.

It should be possible to test software in its production environment, using the same automated tests used in the development environment. Likewise, the built-in logging capabilities should be available in production and not disabled or compiled out.


Short of mandating formal documentation requirements, it would be prudent to at least have some documentation goals. There are two types of documentation to address: programmer guides (API) and user guides. For API, at the minimum, there are almost effort-less tools now for generating API documentation from source code comments. These include doxygen, pydoc, sphinx, and javadoc. Public APIs should be commented, and they may as well be commented in a form from which online documentation can be generated. For user guides, there is no obvious answer. Some projects have used wikis effectively, especially when the wiki content can be downloaded for offline access in the field. The important point is that users should have documentation for basic operations. For operating instruments or processing data in the field, it is crucial that users have convenient and documented methods to verify that the software and instrument are operating normally. This means the software must support diagnosis (see logging) and troubleshooting, and the user guide must document the use and meaning of the diagnostics.

Do not optimize prematurely.

See all the references on the web about Knuth's quote, the 80/20 rule, and other challenges to performance metrics. Basically, there is little point to optimizing code unless it's performance will be measured accurately both before and after the optimization. Never do for the compiler what the compiler can do for you.  In other words, the compiler may already be optimizing code that you think appears slow, so optimizing manually is like fighting the compiler.  Don't bother unless you can verify where the compiler actually needs help.

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