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 [7]. Using the proposed code embeddings, we present an implementation of the XGBoost algorithm that achieves an average \(90\%\) 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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Notes
- 1.
We define dynamic analysis as the process of developing a computer software evaluation based on data acquired from experiments conducted out on a real computing system by executing programs against a range of different inputs.
- 2.
The model is generic and other values can be used as well, yet these are the most relevant values that we have used in our research.
- 3.
In our research, we have searched only a small discrete set of values for n, described earlier in this section.
- 4.
Frontend refers here to the part of the hardware responsible for fetching and decoding instructions.
- 5.
TheInputsCodeforces is a public dataset:
- 6.
- 7.
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Folea, R., Iacob, R., Slusanschi, E., Rebedea, T. (2023). Complexity-Based Code Embeddings. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_20
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