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Multi-χ: Identifying Multiple Authors from Source Code Files
2020
Proceedings on Privacy Enhancing Technologies
AbstractMost authorship identification schemes assume that code samples are written by a single author. However, real software projects are typically the result of a team effort, making it essential to consider a finegrained multi-author identification in a single code sample, which we address with Multi-χ. Multi-χ leverages a deep learning-based approach for multi-author identification in source code, is lightweight, uses a compact representation for efficiency, and does not require any code
doi:10.2478/popets-2020-0044
fatcat:baodtyoke5f4dcqsoaqubaydqq