@inproceedings{472210c430cc48de8b742e0188bf1861,
title = "Cosine Similarity through Control Flow Graphs for Secure Software Engineering",
abstract = "As the recommender systems need data to generate a recommendation, the dataset is intended as a standardized input for research on recommendation systems for software engineering but is also useful in many other areas that analyze source code. In this paper, we present a collection of 557-cryptography C# solutions, which includes 19590 classes. From these repositories of classes are generated two datasets with 19, 590 lines of code, respectively 1, 191, 340 lines of code using control flow graph rules. For two datasets are calculated TF-IDF and Cosine Similarity.",
keywords = "Control Flow Graph, Cosine Similarity, Machine Learning, Recommender Systems, TF-IDF",
author = "Astrit Desku and Bujar Raufi and Artan Luma and Besnik Selimi",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th International Conference on Engineering and Emerging Technologies, ICEET 2021 ; Conference date: 27-10-2021 Through 28-10-2021",
year = "2021",
doi = "10.1109/ICEET53442.2021.9659648",
language = "English",
series = "7th International Conference on Engineering and Emerging Technologies, ICEET 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "7th International Conference on Engineering and Emerging Technologies, ICEET 2021",
address = "United States",
}