Complexity-based code embeddings
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Abstract (arXiv)
This paper presents a generic method for transforming the source code of various algorithms to numerical embeddings, by dynamically analysing the behaviour of computer programs against different inputs and by tailoring multiple generic complexity functions for the analysed metrics. The used algorithms embeddings are based on r-Complexity . Using the proposed code embeddings, we present an implementation of the XGBoost algorithm that achieves an average F1-score on a multi-label dataset with 11 classes, built using real-world code snippets submitted for programming competitions on the Codeforces platform.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 1 Jan 2026.
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References · 10
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2018code2vec: Learning Distributed Representations of Codecited by 250
- 2017Advances in Pre-Training Distributed Word Representationscited by 207
- 2018Neural Code Comprehension: A Learnable Representation of Code Semanticscited by 59
- 2020Fast and Memory-Efficient Neural Code Completioncited by 38
- 2020Exploring Software Naturalness through Neural Language Modelscited by 32
- 2017Dynamic Neural Program Embedding for Program Repaircited by 23
- 2019Learning Scalable and Precise Representation of Program Semanticscited by 6
- 2018A Cross-Architecture Instruction Embedding Model for Natural Language Processing-Inspired Binary Code Analysiscited by 5
- 2018Semantic embeddings for program behavior patternscited by 1
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