SeSaMe: A Data Set of Semantically Similar Java Methods
Marius Kamp, Patrick Kreutzer, Michæl Philippsen · 2019
In the past, techniques for detecting similarly behaving code fragments were often only evaluated with small, artificial oracles or with code originating from programming competitions. Such code fragments differ largely from production codes. To enable more realistic evaluations, this paper presents SeSaMe, a data set of method pairs that are classified according to their semantic similarity. We applied text similarity measures on JavaDoc comments mined from 11 open source repositories and manually classified a selection of 857 pairs.