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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Vydané v:	IEEE transactions on software engineering Ročník 48; číslo 7; s. 2541 - 2556
Hlavní autori:	Wu, Xiaoxue, Zheng, Wei, Xia, Xin, Lo, David
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2022 IEEE Computer Society
Predmet:	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
On-line prístup:	Získať plný text
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