MISIM: An End-to-End Neural Code Similarity System
Fangke Ye, Shengtian Zhou, Anand Venkat, Ryan Marcus, Nesime Tatbul, Jesmin Jahan Tithi, Paul Petersen, Timothy Mattson, Tim Kraska, Pradeep Dubey, Vivek Sarkar, Justin Gottschlich. 2020
Code similarity systems are integral to a range of applications from code recommendation to automated construction of software tests and defect mitigation. In this paper, we present Machine Inferred Code Similarity (MISIM), a novel end-to-end code similarity system that consists of two core components. First, MISIM uses a novel context-aware similarity structure, which is designed to aid in lifting semantic meaning from code syntax. Second, MISIM provides a neural-based code similarity scoring system, which can be implemented with various neural network algorithms and topologies with learned parameters. We compare MISIM to three other state-of-the-art code similarity systems: (i) code2vec, (ii) Neural Code Comprehension, and (iii) Aroma. In our experimental evaluation across 45,780 programs, MISIM consistently outperformed all three systems, often by a large factor (upwards of 40.6x).
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