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Software Quality Journal

Erschienen in:

15.02.2020

An automated approach to assess the similarity of GitHub repositories

verfasst von: Phuong T. Nguyen, Juri Di Rocco, Riccardo Rubei, Davide Di Ruscio

Erschienen in: Software Quality Journal | Ausgabe 2/2020

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Abstract

Open source software (OSS) allows developers to study, change, and improve the code free of charge. There are several high-quality software projects which deliver stable and well-documented products. Most OSS forges typically sustain active user and expert communities which in turn provide decent levels of support both with respect to answering user questions as well as to repairing reported software bugs. Code reuse is an intrinsic feature of OSS, and developing a new system by leveraging existing open source components can reduce development effort, and thus it can be beneficial to at least two phases of the software life cycle, i.e., implementation and maintenance. However, to improve software quality, it is essential to develop a system by learning from well-defined, mature projects. In this sense, the ability to find similar projects that facilitate the undergoing development activities is of high importance. In this paper, we address the issue of mining open source software repositories to detect similar projects, which can be eventually reused by developers. We propose CrossSim as a novel approach to model the OSS ecosystem and to compute similarities among software projects. An evaluation on a dataset collected from GitHub shows that our proposed approach outperforms three well-established baselines.

Vorheriger Artikel Component attributes and their importance in decisions and component selection

Nächster Artikel Mining Association Rules from Code (MARC) to support legacy software management

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Titel: An automated approach to assess the similarity of GitHub repositories
verfasst von: Phuong T. Nguyen
Juri Di Rocco
Riccardo Rubei
Davide Di Ruscio
Publikationsdatum: 15.02.2020
Verlag: Springer US
Erschienen in: Software Quality Journal / Ausgabe 2/2020
Print ISSN: 0963-9314
Elektronische ISSN: 1573-1367
DOI: https://doi.org/10.1007/s11219-019-09483-0

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