Software measurement and defect prediction with depress extensible framework
2014
scientific article
english
- mining in software repositories
- software metrics
- KNIME
- defectprediction
EN Context. Software data collection precedes analysis which, in turn,requires data science related skills. Software defect prediction is hardly used in in-dustrial projects as a quality assurance and cost reduction mean.Objectives. There are many studies and several tools which help in various data anal-ysis tasks but there is still neither an open source tool nor standardized approach.Results. We developed Defect Prediction for software systems (DePress), which is anextensible software measurement, and data integration framework which can be usedfor prediction purposes (e.g. defect prediction, effort prediction) and software changesanalysis (e.g. release notes, bug statistics, commits quality). DePress is based on theKNIME project and allows building workflows in a graphic, end-user friendly manner. Conclusions. We presContext. 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.ent main concepts, as well as the development state of the DePress framework. The results show that DePress can be used in Open Source, as wellas in industrial project analysis.
249 - 270
CC BY-NC-ND (attribution - noncommercial - no derivatives)
public
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