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Adaptive filter-framework for the quality improvement of open-source software analysis

: Hannemann, Anna; Hackstein, Michael; Klamma, Ralf; Jarke, Matthias

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Kowalewski, S. ; Gesellschaft für Informatik -GI-, Fachbereich Softwaretechnik:
Software Engineering 2013 : Fachtagung des GI-Fachbereichs Softwaretechnik, 26. Februar-1. März 2013 in Aachen
Bonn: GI, 2013 (GI-Edition - Lecture Notes in Informatics (LNI) - Proceedings 213)
ISBN: 978-3-88579-607-7
Tagung Software Engineering (SE) <9, 2013, Aachen>
Conference Paper, Electronic Publication
Fraunhofer FIT ()

Knowledge mining in Open-Source Software (OSS) brings a great benefit for software engineering (SE). The researchers discover, investigate, and even simulate the organization of development processes within open-source communities in order to understand the community-oriented organization and to transform its advantages into conventional SE projects. Despite a great number of different studies on OSS data, not much attention has been paid to the data filtering step so far. The noise within uncleaned data can lead to inaccurate conclusions for SE. A special challenge for data cleaning presents the variety of communicational and development infrastructures used by OSS projects. This paper presents an adaptive filter-framework supporting data cleaning and other preprocessing steps. The framework allows to combine filters in arbitrary order, defining which preprocessing steps should be performed. The filter-portfolio can by extended easily. A schema matching in case of cross-project analysis is available. Three filters - spam detection, quotation elimination and coreperiphery distinction - were implemented within the filter-framework. In the analysis of three large-scale OSS projects (BioJava, Biopython, BioPerl), the filtering led to a significant data modification and reduction. The results of text mining (sentiment analysis) and social network analysis on uncleaned and cleaned data differ significantly, confirming the importance of the data preprocessing step within OSS empirical studies.