Programming Tools: Code Complexity Metrics
A number of commercial tools are out there, as well as some open-source ones. None seem to give you the ability to define your own metrics, however, or to use extended versions of existing metrics. Also, of the open-source tools I found, most were outdated or no longer maintained. The one exception is the Eclipse Metrics Plugin.
For the rest of us, I decided to write an open-source program to produce metrics that end users can compute and modify. The program is written in Python and currently is limited to analyzing Python--thus the name PyMetrics--but the principles can be extended to any language. By writing the code in Python, you should be able to understand the program better than if I had written it in almost any other language. The major advantage of this approach is the output format. It lets you work with the raw numbers to produce your own reports and metrics.
The PyMetrics project is hosted on SourceForge.net, and the files can be downloaded from the project page. Please note that due to the short time I had to produce the program, it is not as polished as I would have liked. The things I would have liked to do include but are not limited to:
Showing what are considered industry standard metric values as a point of comparison for your metrics.
Simplifying some of the modules, including tokenize and the main module, PyMetrics.
Providing better modularization to allow others to extend this program more easily.
Defining a set of test suites.
As always, offering better documentation.
Metrics are numbers representing the complexity of a given program. Although some accepted standard measures exist, you need to decide what works for you and the acceptable thresholds.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
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