Identifying Features in Forks

Shurui Zhou, Stefan Stanciulescu, Olaf Lessnich, Yingfei Xiong, Andrzej Wasowski, Christian Kästner

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Abstract

Fork-based development has been widely used both in open source
communities and in industry, because it gives developers flexibility
to modify their own fork without affecting others. Unfortunately,
this mechanism has downsides: When the number of forks becomes
large, it is difficult for developers to get or maintain an overview of
activities in the forks. Current tools provide little help. We introduce
Infox, an approach to automatically identify non-merged features
in forks and to generate an overview of active forks in a project. The
approach clusters cohesive code fragments using code and network-
analysis techniques and uses information-retrieval techniques to
label clusters with keywords. The clustering is effective, with 90 %
accuracy on a set of known features. In addition, a human-subject
evaluation shows that Infox can provide actionable insight for
developers of forks.
OriginalsprogEngelsk
TitelProceedings of the 40th ACM/IEEE International Conference on Software Engineering (ICSE2018), Gothenburg, Sweden
Antal sider12
ForlagIEEE
Publikationsdato2018
ISBN (Trykt)978-1-4503-5638-1
DOI
StatusUdgivet - 2018
BegivenhedICSE 2018 40th International Conference on Software Engineering - Gothenburg, Sverige
Varighed: 27 maj 20183 jun. 2018
https://www.icse2018.org/

Konference

KonferenceICSE 2018 40th International Conference on Software Engineering
Land/OmrådeSverige
ByGothenburg
Periode27/05/201803/06/2018
Internetadresse

Emneord

  • Fork-based development
  • Non-merged features
  • Clustering techniques
  • Network analysis
  • Information retrieval

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