Custom Java Metrics in Eclipse

Recently, a relative inquired if I could provide a tool to calculate custom metrics for java code, so I began search and found an old, but open source plugin for eclipse that does just exactly that- calculate metrics without doing any fluffy stuff.
I took its source code and provided it on github.


I had to do minor changes to make it working quickly, which was to use only the metrics table prospective and hide the views not in use.

Here, the metrics need to be provided with 2 essential things:

1. The name of metric in the plugin.xml file (which is read by the plugin in the list of metrics to calculate)
2. Name of the calculator file, which contains the logic used in calculating a specific metric value

The required custom metrics were outlined in a paper that discussed about quality of object oriented design and was academic in nature, so the nature of metrics created is quite simple.

For instance, the metric 'Inheritance Ratio' took the form of following tag in plugin.xml

    name="Inheritance Ratio"
    id="INHR"
    level="type">

and also mapped its logic via the calculator tag:
    name="Inheritance Ratio"
    calculatorClass="net.sourceforge.metrics.calculators.InheritanceRatio"
    level="type">


Next, there is need of simply creating this calculator file and overriding the calculate method. Another thing is to create constants like INHR (in this example) by which the metric and its calculator may communicate during runtime.

So, by piggybacking on this application, I was easily able to provide the metrics tool without worrying anything about the UI and source code management. I plan to finish this quickly and then improve it as per suggestions, which are always welcome!
https://github.com/SumitBisht/Metrics

Book Review: High Performance Python

High Performance Python by Micha Gorelick and Ian Ozsvald

To a causal user of python, this book might not seem interesting or handful, but if one is working on an application that desires the maximum effeciency out of computer, no matter how the code is structured, there is a need to go deeper and find out how the code actually works and this starts from knowing how to best write the code that ensures minimum of cpu cycles and ram.
The book starts with an overview of the various data structures and libraries that python offers as well as detailing the internal structure of various computer architecture. It then moves on to profiling and detecting bottlenecks both via memory and cpu cycles. In sucessive chapters, this is further explained as different forms of data structures are discussed. Similarly the performance gains provided by third party libraries like NumPy are discussed in detail, which is a learning experience. The chapter on compilers is also informative - the benefits of compilation depending upon which compiler is chosen. Important concepts like concurrency and multiprocessing are discussed in detail for the I/O and CPU bound problems.
Overall this is a book that you would like to keep in handy as a reference while designing a performant architecture or improving a python based application's response time. For me, this was an eye opener and a reckoner for improving the designg and effeciency of a python application and many concepts were beyond me at times, but I hope to learn more about python internals and this is a book that I will visit in future - to confirm my findings and keep on learning deeper concepts.
Disclaimer: I have been provided a free copy of this book by OReilly under their Blogger review program.