Revisiting Process versus Product Metrics: a Large Scale Analysis

Numerous automated SE methods can build predictive models from software project data. But what methods and conclusions should we endorse as we move from analytics in-the small (dealing with a handful of projects) to analytics in-the-large (dealing with hundreds of projects)? To answer this question, we recheck prior small-scale results (about process versus product metrics for defect prediction) using 722,471 commits from 770 Github projects. We find that some analytics in-the-small conclusions still hold when scaling up to analytics in-the large. For example, like prior work, we see that process metrics are better predictors for defects than product metrics (best process/product-based learners respectively achieve recalls of 98%/44% and AUCs of 95%/54%, median values). However, we warn that it is unwise to trust metric importance results from analytics in-the-small studies since those change, dramatically when moving to analytics in-the-large. Also, when reasoning in-the-large about hundreds of projects, it is better to use predictions from multiple models (since single model predictions can become very confused and exhibit very high variance). Apart from the above specific conclusions, our more general point is that the SE community now needs to revisit many of the conclusions previously obtained via analytics in-the-small.