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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering
Subtitle of host publicationASE 2020
PublisherAssociation for Computing Machinery
Pages573-584
ISBN (Electronic)9781450367684
Publication statusPublished - Dec 2020

Publication series

NameProceedings - IEEE/ACM International Conference on Automated Software Engineering, ASE

Conference

Title35th IEEE/ACM International Conference on Automated Software Engineering (ASE 2020)
LocationVirtual
PlaceAustralia
CityMelbourne
Period22 - 25 September 2020

Abstract

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. 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.

Research Area(s)

  • automated parameter optimization, classification techniques, configurable software and tool, Cross-project defect prediction, Software defect analysis, transfer learning

Citation Format(s)

BiLO-CPDP : Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction. / Li, Ke; Xiang, Zilin; Chen, Tao; Tan, Kay Chen.

Proceedings - 2020 35th IEEE/ACM International Conference on Automated Software Engineering: ASE 2020. Association for Computing Machinery, 2020. p. 573-584 9285660 (Proceedings - IEEE/ACM International Conference on Automated Software Engineering, ASE).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review