Data quality matters: A case study on data label correctness for security bug report prediction

• Main Authors: WU, Xiaoxue, ZHENG, Wei, XIA, Xin, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Computer bugs; Noise measurement; Predictive models; Security; Chromium; Tuning; Data models; Security bug report prediction; data quality; label correctness; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7436; https://ink.library.smu.edu.sg/context/sis_research/article/8439/viewcontent/DataQualityMatters_2022_av.pdf
• Institution: Singapore Management University

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...