Developers are data scientists. Or at least, they should be.
Consider this: 50% of the development time is typically spent on figuring out the system in order to figure out what to do next. In other words, software engineering is primarily a decision making business. Add to that the fact that often systems contain millions of lines of code and even more data, and you get an environment in which decisions have to be made quickly about lots of ever moving data.
Yet, too often, developers drill into the see of data manually with only rudimentary tool support. This approach does not scale and it should not perpetuate.
In this talk, we show live examples of how software engineering decisions can be made quickly and accurately by building custom analysis tools that enable browsing, visualizing or measuring code and data. All shown examples make use of the Moose analysis platform (http://moosetechnology.org) and Pharo (http://pharo.org).