A collection of code profiling techniques follows.

The biggest problems with dynamic memory management are:

1. leaks and
2. corruption.

While the later is rather tricky to analyse, for memleaks there is valgrind. Invocation as follows:

$ gcc -g test.c
$ valgrind --leak-check=full ./a.out

When programming, the code complexity (O-notation) is the main factor identifying CPU-intense algorithms. Reducing the code's complexity often doesn't suffice, though. E.g. IO-intense operations often lead to delays at run-time which isn't covered by the O-notation, at all. This means that aside of complexity analysis, there always should be run-time code execution time measurement. And this is where gprof comes into action:

$ gcc -pg -g test.c
$ ./a.out
$ gprof a.out gmon.out

Profiling Python is pretty simple, using cPython module:

$ python -m cPython myscript.py

Code coverage means to have a look at how often each line of code is being executed. Interestingly, this can be used to “measure” the code's complexity. This way a broader view over the problem can be provided, e.g. answering the question if a seldomly executed O(n) algorithm is heavier than an O(log(n)) one being executed all the time. One of the best tools to analyse the code coverage is gcov:

$ gcc -fprofile-arcs -ftest-coverage test.c
$ ./a.out
$ gcov tmp.c

this will create the file test.c.gcov containing the annotated source code of test.c.