  Semantic clones are program components with similar behavior, but different
textual representation. Semantic similarity is hard to detect, and semantic
clone detection is still an open issue. We present semantic clone detection via
Probabilistic Software Modeling (PSM) as a robust method for detecting
semantically equivalent methods. PSM inspects the structure and runtime
behavior of a program and synthesizes a network of Probabilistic Models (PMs).
Each PM in the network represents a method in the program and is capable of
generating and evaluating runtime events. We leverage these capabilities to
accurately find semantic clones. Results show that the approach can detect
semantic clones in the complete absence of syntactic similarity with high
precision and low error rates.
