Assessing the Significant Impact of Concept Drift in Software Defect Prediction

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
Subtitle of host publicationProceedings
EditorsVladimir 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
PublisherIEEE
Pages53-58
Number of pages6
ISBN (Print)9781728126074
Publication statusPublished - Jul 2019

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157

Conference

Title43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
PlaceUnited States
CityMilwaukee
Period15 - 19 July 2019

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)

Assessing the Significant Impact of Concept Drift in Software Defect Prediction. / Kabir, Md Alamgir; Keung, Jacky W.; Bennin, Kwabena E.; Zhang, Miao.

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. IEEE, 2019. p. 53-58 8754363 (Proceedings - International Computer Software and Applications Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review