Enhancing the Performance of Software Authorship Attribution Using an Ensemble of Deep Autoencoders

Gabriela Czibula, Mihaiela Lupea and Anamaria Briciu
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Gabriela Czibula: Department of Computer Science, Babeş-Bolyai University, 400347 Cluj-Napoca, Romania
Mihaiela Lupea: Department of Computer Science, Babeş-Bolyai University, 400347 Cluj-Napoca, Romania
Anamaria Briciu: Department of Computer Science, Babeş-Bolyai University, 400347 Cluj-Napoca, Romania

Mathematics, 2022, vol. 10, issue 15, 1-27

Abstract: Software authorship attribution, defined as the problem of software authentication and resolution of source code ownership, is of major relevance in the software engineering field. Authorship analysis of source code is more difficult than the classic task on literature, but it would be of great use in various software development activities such as software maintenance, software quality analysis or project management. This paper addresses the problem of code authorship attribution and introduces, as a proof of concept, a new supervised classification model AutoSoft for identifying the developer of a certain piece of code. The proposed model is composed of an ensemble of autoencoders that are trained to encode and recognize the programming style of software developers. An extension of the AutoSoft classifier, able to recognize an unknown developer (a developer that was not seen during the training), is also discussed and evaluated. Experiments conducted on software programs collected from the Google Code Jam data set highlight the performance of the proposed model in various test settings. A comparison to existing similar solutions for code authorship attribution indicates that AutoSoft outperforms most of them. Moreover, AutoSoft provides the advantage of adaptability, illustrated through a series of extensions such as the definition of class membership probabilities and the re-framing of the AutoSoft system to address one-class classification.

Keywords: software authorship attribution; natural language processing; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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