Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study

Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows data/knowledge from other projects to facilitate...

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Veröffentlicht in:	arXiv.org 2020-02
Hauptverfasser:	Li, Ke, Xiang, Zilin, Chen, Tao, Wang, Shuo, Kay Chen Tan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Automation Classification Computing costs Defects Machine learning Mathematical models Optimization Parameters Prediction models Software Software engineering
Online-Zugang:	Volltext
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