More projects == better defect predictions?

More efficient defect prediction when using data from multiple projects?
http://dx.doi.org/10.1016/j.infsof.2012.10.003

By looking at the newest research in the defect prediction field I've discovered this piece of work which intrigued me a bit. Usually we build statistical models in forms of equations describing defect inflows or use analogy based estimates - we use historical data to create models for new projects. This usually works fine, but this paper discusses things one step further, namely (and I quote):

RQ2: How much within project data should be enriched with data from other projects to achieve comparable performance with full within project data predictions?

The results show that using only 10% of the data can yield results of the same quality, which can significantly improve the cost-efficiency of defect predictions in industrial contexts.

@Miroslaw Staron