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Foundations of Computing and Decision Sciences

The Journal of Poznan University of Technology

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CiteScore 2016: 0.75

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2300-3405
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Software Measurement and Defect Prediction with Depress Extensible Framework

Lech Madeyski
  • Corresponding author
  • Lech Madeyski is with the Faculty of Computer Science and Management, Wroclaw University of Technology, Poland.
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Marek Majchrzak
  • Marek Majchrzak is with the Faculty of Computer Science and Management, Wroclaw University of Technology and Capgemini Poland.
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Published Online: 2014-12-20 | DOI: https://doi.org/10.2478/fcds-2014-0014

Abstract

Context. Software data collection precedes analysis which, in turn, requires data science related skills. Software defect prediction is hardly used in industrial projects as a quality assurance and cost reduction mean. Objectives. There are many studies and several tools which help in various data analysis tasks but there is still neither an open source tool nor standardized approach. Results. We developed Defect Prediction for software systems (DePress), which is an extensible software measurement, and data integration framework which can be used for prediction purposes (e.g. defect prediction, effort prediction) and software changes analysis (e.g. release notes, bug statistics, commits quality). DePress is based on the KNIME project and allows building workflows in a graphic, end-user friendly manner. Conclusions. We present main concepts, as well as the development state of the DePress framework. The results show that DePress can be used in Open Source, as well as in industrial project analysis.

Keywords: mining in software repositories; software metrics; KNIME; defect prediction

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About the article

Published Online: 2014-12-20

Published in Print: 2014-12-01


Citation Information: Foundations of Computing and Decision Sciences, ISSN (Online) 2300-3405, DOI: https://doi.org/10.2478/fcds-2014-0014.

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© by Lech Madeyski. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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