  Many researchers assume that, for software analytics, "more data is better."
We write to show that, at least for learning defect predictors, this may not be
true. To demonstrate this, we analyzed hundreds of popular GitHub projects.
These projects ran for 84 months and contained 3,728 commits (median values).
Across these projects, most of the defects occur very early in their life
cycle. Hence, defect predictors learned from the first 150 commits and four
months perform just as well as anything else. This means that, at least for the
projects studied here, after the first few months, we need not continually
update our defect prediction models. We hope these results inspire other
researchers to adopt a "simplicity-first" approach to their work. Some domains
require a complex and data-hungry analysis. But before assuming complexity, it
is prudent to check the raw data looking for "short cuts" that can simplify the
analysis.
