English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Program authorship attribution has implications for the privacy ofprogrammers who wish to contribute code anonymously. While previous work hasshown that complete files that are individually authored can be attributed, weshow here for the first time that accounts belonging to open sourcecontributors containing short, incomplete, and typically uncompilable fragmentscan also be effectively attributed.  We propose a technique for authorship attribution of contributor accountscontaining small source code samples, such as those that can be obtained fromversion control systems or other direct comparison of sequential versions. Weshow that while application of previous methods to individual small source codesamples yields an accuracy of about 73% for 106 programmers as a baseline, byensembling and averaging the classification probabilities of a sufficientlylarge set of samples belonging to the same author we achieve 99% accuracy forassigning the set of samples to the correct author. Through these results, wedemonstrate that attribution is an important threat to privacy for programmerseven in real-world collaborative environments such as GitHub. Additionally, wepropose the use of calibration curves to identify samples by unknown andpreviously unencountered authors in the open world setting. We show that we canalso use these calibration curves in the case that we do not have linkinginformation and thus are forced to classify individual samples directly. Thisis because the calibration curves allow us to identify which samples are morelikely to have been correctly attributed. Using such a curve can help ananalyst choose a cut-off point which will prevent most misclassifications, atthe cost of causing the rejection of some of the more dubious correctattributions.

Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments