Source code changes may inadvertently introduce performance regressions.
Benchmarking each software version is traditionally employed to identify
performance regressions. Although effective, this exhaustive approach is hard
to carry out in practice. This paper contrasts source code changes against
performance variations. By analyzing 1,288 software versions from 17 open
source projects, we identified 10 source code changes leading to a performance
variation (improvement or regression). We have produced a cost model to infer
whether a software commit introduces a performance variation by analyzing the
source code and sampling the execution of a few versions. By profiling the
execution of only 17% of the versions, our model is able to identify 83% of the
performance regressions greater than 5% and 100% of the regressions greater
than 50%.