Lately, I've found myself in a number of discussions about Technical Debt and how it applies to project development. Overall, I think it's a very powerful tool that -- when used wisely -- can be a great asset to any team. It seems to me that most of the people that I've been talking to really don't agree, and see Technical Debt as a plague that should be eliminated at first sight. So, I figured I'd share my opinions, and see what you think...

He talks about a few different kinds of technical debt described by the names of their financial counterparts:

• the Payday Loan (a current concession for the sake of time)
• a Mortgage (making small parts, payments, of a whole without consideration of the overall picture)
• a Credit Card (not knowing the need causes a sub-optimal solution)
• Hidden Debit (an unclear understanding of the full scope of the debt)

He also touches on two other topics - how to find and get rid of the Hidden Debt your project might have and a common misconception that technical debt doesn't exist in an aglie world.

When you're approaching a framework with a learning curve as steep as ZF, it's easy to be overwhelmed by new terms and declare them buzzwords. Instead, they have often a very precise meaning. I've creates this glossary to collect all the defined terms I could find, so that the PHP developer new to Zend Framework would have a place to come and lookup in the time of confusion.

He breaks it up into a few different sections - generic/reused terms, common component names, what MVC and the bootstrap are as well as the different parts of Zend_Forms.

The term weighting and ranking function is at the core of any information retrieval system. The vector space model with the cosine similarity is maybe the best known and most widely used, but there are plenty of alternatives. We're looking at two here, the BM25 function based around a probabilistic model, and a function based around language modeling.

He's put together a few examples on some basic weighting practices - simple string evaluation based on word occurrence, using the Okapi/BM25 method and language modeling with a little bit of probability and scoring involved.

Lucene supports boosting or weighting terms. For example, if I search for members of a web site, and I type in Dash, I want people with the name Dash to take precedence over somebody who has a hobby of running the 50-yard Dash.

He shows how to, with the help of a few simple lines of PHP code added to your application, you can easily boost terms based on field by whatever multiplier you choose.

The problem with finding a reliable pool of such pages is that basic search often contaisn many blog, forum, bugs.php.net and alike entries which area copy & paste outputs from users. This maybe fine in some instances, but what if you just want the real phpinfo() pages. The answer is surprisingly simple.

His solution? To search for an element always in the page, but unique to it - usually the term you're looking for (like his suggestion of "Zend Scripting Language Engine"). He links to two result sets, one from Google and the other from Yahoo.

Besides the phpinfo information, Ilia also mentions the other handy data you can find with similar searches to major search engines like Apache header information.