Assessing the Significant Impact of Concept Drift in Software Defect Prediction
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 |
Subtitle of host publication | Proceedings |
Editors | Vladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 53-58 |
Number of pages | 6 |
ISBN (print) | 9781728126074 |
Publication status | Published - Jul 2019 |
Publication series
Name | Proceedings - International Computer Software and Applications Conference |
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ISSN (Print) | 0730-3157 |
Conference
Title | 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 |
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Place | United States |
City | Milwaukee |
Period | 15 - 19 July 2019 |
Link(s)
Abstract
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.
Research Area(s)
- Concept drift detection, Defect prediction, Empirical software engineering, Software quality, Streaming data
Citation Format(s)
2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019: Proceedings. ed. / Vladimir Getov; Jean-Luc Gaudiot; Nariyoshi Yamai; Stelvio Cimato; Morris Chang; Yuuichi Teranishi; Ji-Jiang Yang; Hong Va Leong; Hossian Shahriar; Michiharu Takemoto; Dave Towey; Hiroki Takakura; Atilla Elci; Susumu Takeuchi; Satish Puri. Institute of Electrical and Electronics Engineers, Inc., 2019. p. 53-58 8754363 (Proceedings - International Computer Software and Applications Conference).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review