Results 1 to 10 of about 12,700,907 (198)
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 +3 more
doaj +4 more sources
Medienimpulse, 2011 „They say ev’rything can be replaced Yet ev’ry distance is not near “ Bob ...
Thomas Ballhausen, Lisa Leitenmüller
doaj +4 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 +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 +5 more
doaj +2 more sources
, 2013 This article appears in the Oxford Handbook of Sound and Image in Digital Media edited by Carol Vernallis, Amy Herzog, and John Richardson. This chapter attempts to think through the implications of eco-criticism in new media, especially for the theory of subjectivity.
Stephenson, William
openaire +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 +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 +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 +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 +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