Title: FaCoY - A Code-to-Code Search Engine

Abstract:
Code search is an unavoidable activity in software development.
Various approaches and techniques have been explored in the literature
to support code search tasks. Most of these approaches focus on
serving user queries provided as natural language free-form input.
However, there exists a wide range of use-case scenarios where a
code-to-code approach would be most beneficial. For example, research
directions in code transplantation, code diversity, patch
recommendation can leverage a code-to-code search engine to find
essential ingredients for their techniques.

In this paper, we propose FaCoY, a novel approach for statically
finding code fragments which may be semantically similar to user input
code. FaCoY implements a query alternation strategy: instead of
directly matching code query tokens with code in the search space,
FaCoY first attempts to identify other tokens which may also be
relevant in implementing the functional behavior of the input code.
With various experiments, we show that (1) FaCoY is more effective
than online code-to-code search engines; (2) FaCoY can detect more
semantic code clones (i.e., Type-4) in BigCloneBench than the
state-of-the-art; (3) FaCoY, while static, can detect code fragments
which are indeed similar with respect to runtime execution behavior;
and (4) FaCoY can be useful in code/patch recommendation.