Title | ||
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BiLO-CPDP: bi-level programming for automated model discovery in cross-project defect prediction |
Abstract | ||
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ABSTRACTCross-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. However, 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, dubbed BiLO-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 evaluate BiLO-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 being overwhelmingly superior to Auto-Sklearn and its single-level optimization variant on all cases. Furthermore, the unique bi-level formalization in BiLO-CPDP also permits to allocate more budget to the upper-level, which significantly boosts the performance. |
Year | DOI | Venue |
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2020 | 10.1145/3324884.3416617 | ASE |
Keywords | DocType | ISSN |
Cross-project defect prediction, transfer learning, classification techniques, automated parameter optimization, configurable software and tool | Conference | 1527-1366 |
ISBN | Citations | PageRank |
978-1-7281-7281-1 | 3 | 0.36 |
References | Authors | |
0 | 4 |
Name | Order | Citations | PageRank |
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Ke Li | 1 | 75 | 4.86 |
Zilin Xiang | 2 | 3 | 0.69 |
Tao Chen | 3 | 599 | 29.93 |
Kay Chen Tan | 4 | 2767 | 164.86 |