# MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure

arXiv: https://arxiv.org/abs/2006.05265 · HTML: https://mlsanity.com/paper/2006.05265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code](https://mlsanity.com/paper/2205.11739/) (2022)
- [Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting](https://mlsanity.com/paper/2405.16133/) (2024)
- [End to End Software Engineering Research](https://mlsanity.com/paper/2112.11858/) (2021)
- [Neuro-symbolic Zero-Shot Code Cloning with Cross-Language Intermediate Representation](https://mlsanity.com/paper/2304.13350/) (2023)
- [AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection](https://mlsanity.com/paper/2311.07277/) (2023)
- [Bridging Code Graphs and Large Language Models for Better Code Understanding](https://mlsanity.com/paper/2512.07666/) (2025)

## Abstract (arXiv)

Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection. Yet, the accuracy of such systems has not yet reached a level of general purpose reliability. To help address this, we present Machine Inferred Code Similarity (MISIM), a neural code semantics similarity system consisting of two core components: (i)MISIM uses a novel context-aware semantics structure, which was purpose-built to lift semantics from code syntax; (ii)MISIM uses an extensible neural code similarity scoring algorithm, which can be used for various neural network architectures with learned parameters. We compare MISIM to four state-of-the-art systems, including two additional hand-customized models, over 328K programs consisting of over 18 million lines of code. Our experiments show that MISIM has 8.08% better accuracy (using MAP@R) compared to the next best performing system.