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Artificial Intelligence And Data Science PDF Available DOI: 10.1007/978-3-031-41456-5_20 Non-peer-reviewed Preprint

Complexity-based code embeddings

Rares Folea, Radu Iacob, Emil Slusanschi, Traian Rebedea  ยท  Published 2026-01-01

Abstract

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.
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