1 research outputs found
Analyzing Code Comments to Boost Program Comprehension
We are trying to find source code comments that help programmers understand a
nontrivial part of source code. One of such examples would be explaining to
assign a zero as a way to "clear" a buffer. Such comments are invaluable to
programmers and identifying them correctly would be of great help. Toward this
goal, we developed a method to discover explanatory code comments in a source
code. We first propose eleven distinct categories of code comments. We then
developed a decision-tree based classifier that can identify explanatory
comments with 60% precision and 80% recall. We analyzed 2,000 GitHub projects
that are written in two languages: Java and Python. This task is novel in that
it focuses on a microscopic comment ("local comment") within a method or
function, in contrast to the prior efforts that focused on API- or method-level
comments. We also investigated how different category of comments is used in
different projects. Our key finding is that there are two dominant types of
comments: preconditional and postconditional. Our findings also suggest that
many English code comments have a certain grammatical structure that are
consistent across different projects