Gesellschaft für Informatik e.V.

Lecture Notes in Informatics


Software Engineering 2013 P-213, 143-156 (2013).

Gesellschaft für Informatik, Bonn
2013


Copyright © Gesellschaft für Informatik, Bonn

Contents

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

Anna Hannemann , Michael Hackstein , Ralf Klamma and Matthias Jarke

Abstract


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.


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Gesellschaft für Informatik, Bonn
ISBN 978-3-88579-607-7


Last changed 20.02.2014 13:02:20