Abstract Source code authorship attribution can be used for many types of intelligence on binaries and executables, including forensics, but introduces a threat to the privacy of anonymous programmers. Previous work has shown how to attribute individually authored code files and code segments. In this work, we examine authorship segmentation, in which we determine authorship of arbitrary parts of a program. While previous work has performed segmentation at the textual level, we attempt to attribute subtrees of the abstract syntax tree (AST). We focus on two primary problems: identifying the primary author of an arbitrary AST subtree and identifying on which edges of the AST primary authorship changes. We demonstrate that the former is a difficult problem but the later is much easier. We also demonstrate methods by which we can leverage the easier problem to improve accuracy for the harder problem. We show that while identifying the author of subtrees is difficult overall, this is primarily due to the abundance of small subtrees: in the validation set we can attribute subtrees of at least 25 nodes with accuracy over 80% and at least 33 nodes with accuracy over 90%, while in the test set we can attribute subtrees of at least 33 nodes with accuracy of 70%. While our baseline accuracy for single AST nodes is 20.21% for the validation set and 35.66% for the test set, we present techniques by which we can increase this accuracy to 42.01% and 49.21% respectively. We further present observations about collaborative code found on GitHub that may drive further research.

Abstract Program authorship attribution has implications for the privacy of programmers who wish to contribute code anonymously. While previous work has shown that individually authored complete files can be attributed, these efforts have focused on such ideal data sets as contest submissions and student assignments. We explore the problem of authorship attribution “in the wild,” examining source code obtained from open-source version control systems, and investigate how contributions can be attributed to their authors, either on an individual or a per-account basis. In this work, we present a study of attribution of code collected from collaborative environments and identify factors which make attribution of code fragments more or less successful. For individual contributions, we show that previous methods (adapted to be applied to short code fragments) yield an accuracy of approximately 50% or 60%, depending on whether we average by sample or by author, at identifying the correct author out of a set of 104 programmers. By ensembling the classification probabilities of a sufficiently large set of samples belonging to the same author we achieve much higher accuracy for assigning the set of samples to the correct author from a known suspect set. Additionally, we propose the use of calibration curves to identify which samples are by unknown and previously unencountered authors.