Data Quality Matters: A Case Study on Data Label Correctness for Security Bug Report Prediction

In the research of mining software repositories, we need to label a large amount of data to construct a predictive model. The correctness of the labels will affect the performance of a model substantially. However, limited studies have been performed to investigate the impact of mislabeled instances...

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Veröffentlicht in:	IEEE transactions on software engineering Jg. 48; H. 7; S. 2541 - 2556
Hauptverfasser:	Wu, Xiaoxue, Zheng, Wei, Xia, Xin, Lo, David
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2022 IEEE Computer Society
Schlagworte:	Case studies Chromium Classification Computer bugs Cybersecurity Data models data quality Datasets Debugging label correctness Noise measurement Prediction models Predictive models Security Security bug report prediction Tuning
ISSN:	0098-5589, 1939-3520
Online-Zugang:	Volltext
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