## Code Quality vs. Lines of Code

### Redundancy?

• Notes for me:
• Landman suggests not redundant enough to chuck the CC metric
• The correlation between aggregated CC for all subroutines and the total SLOC of a file is higher than the correlation between CC and SLOC of individual subroutines

FIXME

## “Big Data”! Lines of Code vs. Function Declarations

We saw in our last exercise that we could use ASTs to explore the components of a program. That was a helpful exercise for learning about how we employ tools to collect data from programs on GitHub. However, we only collected a few hundred programs; and if you recall, they were all implementations of data structures and programming concepts in isolation. We know that from our sample, our results may not generalize to production code. This means we are lacking something called external validity: results of a study can realiably generalize to other contexts/samples (within reason).

Now, you’ve probably heard this before when discussing concepts like Big O: it only really starts to matter for performance when n is big enough. I know I’ve certainly questioned why O(log(n)) vs. O(n) even matters when my little sorting snippet runs just fine whatever the algorithm. But here is a big concept: Complexity matters for big data, and the important findings may be undetectable on toy problems.

Landman et al. also investigated this problem, using millions of Java and C programs.

FIXME: They’re actually different problems. I have to figure out how to tie them together: our problem looks at functions and lines, theirs looks at number of lines per function, and also looks at CC.

# lang,cc,sloc

source("../data/ESEUR_config.r")

library("plyr")
cc_loc$logloc=log(cc_loc$sloc)
Java_loc=subset(cc_loc, lang == "Java")