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BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction by Li, Ke; Xiang, Zilin; Chen, Tao; Tan, Kay Chen is a scholarly article available to read on EtoBox.

What is BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction about?

Cross-Project Defect Prediction (CPDP), which borrows data from similar projects by combining a transfer learner with a classifier, have emerged as a promising way to predict software defects when the available data about the target project is insufficient. How-ever, developing such a model is challenge because it is difficult to determine the right combination of transfer learner and classifier along with their optimal hyper-parameter settings. In this paper, we propose a tool, dubbedBiLO-CPDP, which is the first of its kind to formulate the automated CPDP model discovery from the perspective of bi-level programming. In particular, the bi-level programming proceeds the optimization with two nested levels in a hierarchical manner. Specifically, the upper-level optimization routine is designed to search for the right combination of transfer learner and classifier while the nested lower-level optimization routine aims to optimize the corresponding hyper-parameter settings.To evaluateBiLO-CPDP, we conduct experiments on 20 projects to compare it with a total of 21 existing CPDP techniques, along with its single-level optimization variant and Auto-Sklearn, a state-of-the-art automated

Author

Li, Ke; Xiang, Zilin; Chen, Tao; Tan, Kay Chen

Published

2020

Language

EN