Cross-company defect prediction via semi-supervised clustering-based data filtering and MSTrA-based transfer learning
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 3461-3472 |
Journal / Publication | Soft Computing |
Volume | 22 |
Issue number | 10 |
Online published | 8 Mar 2018 |
Publication status | Published - May 2018 |
Link(s)
Abstract
Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to a target company. Unfortunately, larger irrelevant cross-company (CC) data usually make it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to within-company data may decrease the prediction model performance. To address such issues, we aim to provide an effective solution for CCDP. First, we propose a novel semi-supervised clustering-based data filtering method (i.e., SSDBSCAN filter) to filter out irrelevant CC data. Second, based on the filtered CC data, we for the first time introduce multi-source TrAdaBoost algorithm, an effective transfer learning method, into CCDP to import knowledge not from one but from multiple sources to avoid negative transfer. Experiments on 15 public datasets indicate that: (1) our proposed SSDBSCAN filter achieves better overall performance than compared data filtering methods; (2) our proposed CCDP approach achieves the best overall performance among all tested CCDP approaches; and (3) our proposed CCDP approach performs significantly better than with-company defect prediction models.
Research Area(s)
- Cross-company defect prediction, Multi-source TrAdaBoost, SSDBSCAN, Transfer learning
Citation Format(s)
Cross-company defect prediction via semi-supervised clustering-based data filtering and MSTrA-based transfer learning. / Yu, Xiao; Wu, Man; Jian, Yiheng; Bennin, Kwabena Ebo; Fu, Mandi; Ma, Chuanxiang.
In: Soft Computing, Vol. 22, No. 10, 05.2018, p. 3461-3472.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review