Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting Process versus Product Metrics: a Large Scale Analysis   Numerous methods can build predictive models from software data. However,
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 and the granularity of metrics)
using 722,471 commits from 700 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).
  That said, 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 confused and exhibit a high variance).

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin