What makes good code and why is some code better than others? One quantitative approach to answer this is the use of software metrics. These metrics try to capture the size and complexity of code in numbers (e.g. ‘lines of code‘, ‘cyclomatic complexity‘) and can be useful indicators for maintainability and simplicity (or more often the lack thereof).
I found three ways to get complexity measures for PHP code.
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This is a nice idea and proof of concept: spreading malware in BibTeX files.
Probably every TeX-User knows the advocacy about how powerful the language is… but do (or can) you proofread everything before you compile it?
Recently I replaced my office’s filtering network bridge which still ran on an old FreeBSD 4.11 box for the last eight years or so. The new system is based on a Soekris 5501, and because it took some time to choose the right software I decided to publish my notes on the tested BSD firewall products.
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Recently I had to work with SQL dumps to recover a database server and to update a MySQL installation. Because complete SQL dumps are too big to handle them with diff and vi (with enough memory vim actually works on large files, but it is really slow) I needed more traditional tools to compare them and to extract only the parts I needed.
Names and numbers are important but invaluable resources in IT (e.g. URIs, IPs, e-mail adresses). Their consideration nicely distinguishes techies (for lack of a better word; everyone with certain technological experience and proficiency) from non-techies (e.g. management :->).
Non-techies see these resources as abundant and dirt-cheap because they do not cost any money. – Whereas techies regard them are scarce and expensive because no amount of money can completely revert a bad allocation.
A quick reminder: the application deadline for GSoC2010 is this friday.
I highly recommend to participate because it is a great opportunity to get the experience of working with a mentor in a large open source project.
