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Classification of Engineered Open Source Projects through Machine Learning

ФИО студента: Kozhevnikov Dmitriy

Руководитель: Dmitry Pantiukhin

Кампус/факультет: Faculty of Computer Science

Программа: System and Software Engineering (Master)

Оценка: 10

Год защиты: 2017

On-line software forges as GitHub, Bitbucket, GitLab, etc. represent an abundant resource of insightful information, which can be mined to foster understanding of code architecture, code longevity, life-cycle and evolution, programming language analysis, etc. However, a remarkable number of repositories harbors student assignments, small software experiments or simply serve the purpose of generic storage. Including such repositories into analysis may introduce considerable distortion to results of the study, hence inappropriate projects should be sifted away. In this work we propose an evaluation framework for software repositories to formally express various features that compose the notion of engineered software project. Our framework employs both meta-information acquired from the repository and data received from static analysis of source code contents. The automation of feature collection is implemented with a tool called Reporanger, which is able to perform extraction for a given repository without human intervention. We select and label a set of 300 repositories to train a number of classifiers based on machine learning algorithms. The best performing classifier employs Decision Forest model that demonstrates Area Under Curve = 0.958 and F-Score = 0.906, which is significantly better than the classifier based on conventional stargazer criteria (Area Under Curve = 0.821 and F-Score = 0.782). Our research does not only produce a software project classifier, but also identifies the most impactful features that are help best to highlight the differences between engineered and non-engineered projects.

Full text (added June 4, 2017)

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