Software Measurement and Defect Prediction with Depress Extensible Framework
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 a...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Sciendo
2014-12-01
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| Series: | Foundations of Computing and Decision Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/fcds-2014-0014 |
| _version_ | 1818486187459870720 |
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| author | Madeyski Lech Majchrzak Marek |
| author_facet | Madeyski Lech Majchrzak Marek |
| author_sort | Madeyski Lech |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-10T16:19:39Z |
| format | Article |
| id | doaj.art-d804455fd5654db296dbea35e2c4aca5 |
| institution | Directory Open Access Journal |
| issn | 2300-3405 |
| language | English |
| last_indexed | 2024-12-10T16:19:39Z |
| publishDate | 2014-12-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Foundations of Computing and Decision Sciences |
| spelling | doaj.art-d804455fd5654db296dbea35e2c4aca52022-12-22T01:41:52ZengSciendoFoundations of Computing and Decision Sciences2300-34052014-12-0139424927010.2478/fcds-2014-0014fcds-2014-0014Software Measurement and Defect Prediction with Depress Extensible FrameworkMadeyski Lech0Majchrzak Marek1Lech Madeyski is with the Faculty of Computer Science and Management, Wroclaw University of Technology, Poland.Marek Majchrzak is with the Faculty of Computer Science and Management, Wroclaw University of Technology and Capgemini Poland.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.https://doi.org/10.2478/fcds-2014-0014mining in software repositoriessoftware metricsknimedefect prediction |
| spellingShingle | Madeyski Lech Majchrzak Marek Software Measurement and Defect Prediction with Depress Extensible Framework Foundations of Computing and Decision Sciences mining in software repositories software metrics knime defect prediction |
| title | Software Measurement and Defect Prediction with Depress Extensible Framework |
| title_full | Software Measurement and Defect Prediction with Depress Extensible Framework |
| title_fullStr | Software Measurement and Defect Prediction with Depress Extensible Framework |
| title_full_unstemmed | Software Measurement and Defect Prediction with Depress Extensible Framework |
| title_short | Software Measurement and Defect Prediction with Depress Extensible Framework |
| title_sort | software measurement and defect prediction with depress extensible framework |
| topic | mining in software repositories software metrics knime defect prediction |
| url | https://doi.org/10.2478/fcds-2014-0014 |
| work_keys_str_mv | AT madeyskilech softwaremeasurementanddefectpredictionwithdepressextensibleframework AT majchrzakmarek softwaremeasurementanddefectpredictionwithdepressextensibleframework |