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A Survey of Automatic Source Code Summarization

Symmetry, 2022
Source code summarization refers to the natural language description of the source code’s function. It can help developers easily understand the semantics of the source code.
Zhang Chunyan, Qinglei Zhou, Fudong Liu
exaly   +2 more sources

Source code analysis dataset [PDF]

Data in Brief, 2019
The data in this article pair source code with three artifacts from 108,568 projects downloaded from Github that have a redistributable license and at least 10 stars.
Ben Gelman, Banjo Obayomi, Jessica Moore, David Slater   +3 more
doaj   +2 more sources

Academic Source Code Plagiarism Detection by Measuring Program Behavioral Similarity [PDF]

IEEE Access, 2021
Source code plagiarism is a long-standing issue in tertiary computer science education. Many source code plagiarism detection tools have been proposed to aid in the detection of source code plagiarism.
Hayden Cheers, Yuqing Lin, Shamus P. Smith   +2 more
doaj   +2 more sources

Vulnerability Prediction From Source Code Using Machine Learning

IEEE Access, 2020
As the role of information and communication technologies gradually increases in our lives, software security becomes a major issue to provide protection against malicious attempts and to avoid ending up with noncompensable damages to the system.
Zeki Bilgin, Mehmet Akif Ersoy, Elif Ustundag Soykan, Emrah Tomur, Pinar Comak, Leyli Karacay   +5 more
doaj   +2 more sources

Source Code

Medienimpulse, 2011
„They say ev’rything can be replaced Yet ev’ry distance is not near “ Bob ...
Thomas Ballhausen, Lisa Leitenmüller
doaj   +3 more sources

DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection [PDF]

International Symposium on Recent Advances in Intrusion Detection, 2023
We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects.
Yizheng Chen, Zhoujie Ding, Lamya Alowain, Xinyun Chen, David A. Wagner   +4 more
semanticscholar   +1 more source

The Stack: 3 TB of permissively licensed source code [PDF]

Trans. Mach. Learn. Res., 2022
Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language processing but also for code understanding and generation.
Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, Dzmitry Bahdanau, L. V. Werra, H. D. Vries   +12 more
semanticscholar   +1 more source

An Empirical Comparison of Pre-Trained Models of Source Code [PDF]

International Conference on Software Engineering, 2023
While a large number of pre-trained models of source code have been successfully developed and applied to a variety of software engineering (SE) tasks in recent years, our understanding of these pre-trained models is arguably fairly limited.
Changan Niu, Chuanyi Li, Vincent Ng, Dongxiao Chen, Jidong Ge, B. Luo   +5 more
semanticscholar   +1 more source

TRACED: Execution-Aware Pre-Training for Source Code [PDF]

International Conference on Software Engineering, 2023
Most existing pretrained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency graphs, etc.).
Yangruibo Ding, Benjamin Steenhoek, Kexin Pei, Gail E Kaiser, Wei Le, Baishakhi Ray   +5 more
semanticscholar   +1 more source

VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection [PDF]

IEEE International Joint Conference on Neural Network, 2022
This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code. Our approach pre-trains a RoBERTa model with a custom tokenisation pipeline on real-world code from open-source C/C++ projects.
Hazim Hanif, S. Maffeis
semanticscholar   +1 more source