New SpamAssassin Rule Development Tools

Recently, I’ve been working on new systems to develop SpamAssassin rules faster, and with a lower ‘barrier to entry’ to the core ruleset. Some highlights seem bloggable, seeing as it’s all web-based and I can link to it!

The ‘preflight’ BuildBot:

This uses the fantastic BuildBot continuous-integration system to monitor changes to our Subversion repository.

Every time something is checked into SVN, this wakes up and immediately runs mass-checks using that latest code and rules, allowing near-real-time viewing of changes in rule behaviour. (A ‘mass-check’ is a massive run of SpamAssassin across a corpus of hundreds of thousands of emails, en masse, to measure rule hit-rates.)

The corpus it mass-checks is split in a certain way so that results will be available very quickly — typically in under 10 minutes — with increasing quantities of results becoming available as time elapses.

Progress of the mass-checks are visible at the BuildBot here; as they complete, their results become visible on the Rule-QA app (below). (More info, if you’re curious.)

The Rule-QA App:

To date, we’ve used the basic “freqs” table — output from the hit-frequencies command-line script — as the UI for rule QA and evaluation. This is fine for a small number of developers, but it scales badly and (like mass-checks) requires a pretty complex setup on the developer’s machine.

This new component is a web application, which takes the “freqs” table, and “webifies” it — demo.

Some major improvements are also made possible; the most important, that it can now display ‘freqs’ for multiple revisions during the day, and keeps historical data for comparison. It adds several new reports from ‘hit-frequencies’; a score-map, overlaps, a performance measurement, and a boolean ‘promoteability’ measurement.

Finally, a really useful new report is the graph of rule hit-rate, as it changes over time. Here’s a cached demo, or see the same data produced ‘live’. This gives a totally new insight into how the rule hits for various people’s corpora, how that changed over time, and allows a whole new type of rule analysis. (In fact, it also allows pretty good corpus analysis, too; can you tell which submitters bounce high-scoring spam at receipt time?)

(More info on these.)

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