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MISIM: An End-to-End Neural Code Similarity System

by   Fangke Ye, et al.
Georgia Institute of Technology

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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