BiLO-CPDP：跨项目缺陷预测中自动模型发现的双层编程
BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction
摘要：
跨项目缺陷预测（CPDP）通过将迁移学习器与分类器相结合，从类似项目中借用数据，已经成为在目标项目的可用数据不足时预测软件缺陷的一种有前途的方法。然而，开发这样的模型是具有挑战性的，因为很难确定迁移学习器和分类器的正确组合以及它们的最佳超参数设置。在本文中，我们提出了一个工具，dubbedBiLO-CPDP，这是第一次制定的自动CPDP模型发现从双层编程的角度来看。特别地，双层编程以分层方式用两个嵌套层进行优化。具体来说，上层优化例程旨在搜索迁移学习器和分类器的正确组合，而嵌套的下层优化例程旨在优化相应的超参数设置。为了评估BiLO-CPDP，我们在20个项目上进行了实验，将其与现有的21种CPDP技术进行比较，以及其单级优化变体和Auto-Sklearn，一个最先进的自动化机器学习工具。实证结果表明，BiLO-CPDP在70%的项目上的预测性能优于所有其他21种现有的CPDP技术，同时在所有情况下都压倒性地优于Auto-Sklearn及其单级优化变体。此外，BiLO-CPDP中独特的双层形式化还允许将更多的预算分配给上层，这显着提高了性能。
英文摘要：
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 machine learning tool. Empirical results show that BiLO-CPDP champions better prediction performance than all other 21 existing CPDP techniques on 70% of the projects, while be-ing overwhelmingly superior to Auto-Sklearn and its single-level optimization variant on all cases. Furthermore, the unique bi-level formalization inBiLO-CPDP also permits to allocate more budget to the upper-level, which significantly boosts the performance.
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