Early Software Defects Density Prediction: Training the International Software Benchmarking Cross Projects Data Using Supervised Learning

Early Software Defects Density Prediction: Training the International Software Benchmarking Cross Projects Data Using Supervised Learning

Recent reviews of the literature indicate the need for empirical studies on cross-project defect prediction (CPDP) that would allow aggregation of the evidence and improve predictive performance. Most empirical studies predict defects at granularity levels of method, class, file, and module/package...

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Bibliographic Details
Main Authors: Touseef Tahir, Cigdem Gencel, Ghulam Rasool, Tariq Umer, Jawad Rasheed, Sook Fern Yeo, Taner Cevik
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Cross projects dataset
defect prediction
feature selection
fault prediction
ISBSG dataset
machine learning
Online Access:https://ieeexplore.ieee.org/document/10345541/

Internet

https://ieeexplore.ieee.org/document/10345541/

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