An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction

An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction

Ranking-oriented cross-project defect prediction (ROCPDP), which ranks software modules of a new target industrial project based on the predicted defect number or density, has been suggested in the literature. A major concern of ROCPDP is the distribution difference between the source project (aka....

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Bibliographic Details
Main Authors: Haoyu Luo, Heng Dai, Weiqiang Peng, Wenhua Hu, Fuyang Li
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Sensors
Subjects:
fault prediction
machine learning
data selection
Online Access:https://www.mdpi.com/1424-8220/21/22/7535

Internet

https://www.mdpi.com/1424-8220/21/22/7535

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