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Assessing the significant impact of concept drift in software defect prediction
City University of Hong Kong, CHN.
City University of Hong Kong, CHN.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
City University of Hong Kong, CHN.
2019 (English)In: Proceedings - International Computer Software and Applications Conference, IEEE Computer Society , 2019, p. 53-58Conference paper, Published paper (Refereed)
Abstract [en]

Concept drift is a known phenomenon in software data analytics. It refers to the changes in the data distribution over time. The performance of analytic and prediction models degrades due to the changes in the data over time. To improve prediction performance, most studies propose that the prediction model be updated when concept drift occurs. In this work, we investigate the existence of concept drift and its associated effects on software defect prediction performance. We adopt the strategy of an empirically proven method DDM (Drift Detection Method) and evaluate its statistical significance using the chi-square test with Yates continuity correction. The objective is to empirically determine the concept drift and to calibrate the base model accordingly. The empirical study indicates that the concept drift occurs in software defect datasets, and its existence subsequently degrades the performance of prediction models. Two types of concept drifts (gradual and sudden drifts) were identified using the chi-square test with Yates continuity correction in the software defect datasets studied. We suggest concept drift should be considered by software quality assurance teams when building prediction models. © 2019 IEEE.

Place, publisher, year, edition, pages
IEEE Computer Society , 2019. p. 53-58
Keywords [en]
Concept drift detection, Defect prediction, Empirical software engineering, Software quality, Streaming data, Computer software selection and evaluation, Data Analytics, Defects, Forecasting, Quality assurance, Software testing, Statistical tests, Concept drifts, Application programs
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-18761DOI: 10.1109/COMPSAC.2019.00017Scopus ID: 2-s2.0-85072713940ISBN: 9781728126074 (print)OAI: oai:DiVA.org:bth-18761DiVA, id: diva2:1361813
Conference
43rd IEEE Annual Computer Software and Applications Conference, COMPSAC, 15 July 2019 through 19 July 2019
Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2019-10-17Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf