SLACC: Simion-based Language Agnostic Code Clones

• Published in: 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE) pp. 210 - 221
• Main Authors: Mathew, George, Parnin, Chris, Stolee, Kathryn T
• Format: Conference Proceeding
• Language: English
• Published: ACM 01.10.2020
• Subjects: Cloning; cross-language analysis; Java; Python; Runtime; semantic code clone detection; Semantics; Static analysis
• ISSN: 1558-1225
• DOI: 10.1145/3377811.3380407

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 codebase. 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 called SLACC for cross-language clone detection. Like prior clone detection techniques, 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. Since clusters are generated based on input/output behavior, SLACC supports cross-language clone detection. As an added challenge, we target a static typed language, Java, and a dynamic typed language, Python. Compared to HitoshiIO, a recent clone detection tool for Java, SLACC retrieves 6 times 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 scalable language migration tools.