Successful cross-language clone detection could enable researchers and developers to create robust language migration tools, facilitate learning additional programming languages once one is mastered, and promote reuse of code snippets over a broader code base. However, identifying cross-language clones presents special challenges to the clone detection problem. A lack of common underlying representation between arbitrary languages means detecting clones requires one of the following solutions: 1) a static analysis framework replicated across each targeted language with annotations matching language features across all languages, or 2) a dynamic analysis framework that detects clones based on runtime behavior.

In this work, we demonstrate the feasibility of the latter solution, a dynamic analysis approach for cross-language clone detection. As an added challenge, we target a static typed language, Java, and a dynamic typed language, Python. As is done in prior clone detection work, we use input/output behavior to match clones, though we overcome limitations of prior work by amplifying the number of inputs and covering more data types; and as a result, achieve better clusters than prior attempts. Compared to HitoshiIO, a recent clone detection tool, SLACC retrieves 6x as many clusters and has higher precision (86.7% vs. 30.7%).

This is the first work to perform clone detection for dynamic typed languages (precision = 87.3%) and the first to perform clone detection across languages that lack a common underlying representation (precision = 94.1%). It provides a first step towards the larger goal of extensible and scalable language migration tools.