  Reusing code can produce duplicate or near-duplicate code clones in code
repositories. Current code clone detection techniques, like Program Dependence
Graphs, rely on code structure and their dependencies to detect clones. These
techniques are expensive, using large amounts of processing power, time, and
memory. In practice, programmers often utilize code comments to comprehend and
reuse code, as comments carry important domain knowledge. But current code
detection techniques ignore code comments, mainly due to the ambiguity of the
English language. Recent advances in information retrieval techniques may have
the potential to utilize code comments for clone detection. We investigated
this by empirically comparing the accuracy of detecting clones with solely
comments versus solely source code (without comments) on the JHotDraw package,
which contains 315 classes and 27K lines of code. To detect clones at the file
level, we used a topic modeling technique, Latent Dirichlet Allocation, to
analyze code comments and GRAPLE -- utilizing Program Dependency Graph -- to
analyze code. Our results show 94.86 recall and 84.21 precision with Latent
Dirichlet Allocation and 28.7 recall and 55.39 precision using GRAPLE. We found
Latent Dirichlet Allocation generated false positives in cases where programs
lacked quality comments. But this limitation can be addressed by using a hybrid
approach: utilizing code comments at the file level to reduce the clone set and
then using Program Dependency Graph-based techniques at the method level to
detect precise clones. Our further analysis across Java and Python packages,
Java Swing and PyGUI, found a recall of 74.86\% and a precision of 84.21\%. Our
findings call for reexamining the assumptions regarding the use of code
comments in current clone detection techniques.
