On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction

Li, Zhiqiang; Jing, Xiao‐Yuan; Zhu, Xiaoke; Zhang, Hongyu; Xu, Bin; Song, Ying

doi:10.1109/tse.2017.2780222

Cited by 76 publications

(31 citation statements)

References 89 publications

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“…In this paper, we use two measures to evaluate the performance of DAFL: F-measure and G-measure. F-measure and G-measure are well-known measures [8], [15], [38], [46], [69], [70], which are applied to evaluate the prediction performance. We follow previous studies and use F-measure and G-measure as the indicators in our study.…”

Section: Evaluation Measurementioning

confidence: 99%

Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction

Sun

Jing

et al. 2020

IEEE Access

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Cross-project defect prediction (CPDP) aims to build a prediction model on existing source projects and predict the labels of target project. The data distribution difference between different projects makes CPDP very challenging. Besides, most existing CPDP methods usually require sufficient and labeled data. However, acquiring lots of labeled data for a new project is difficult while obtaining the unlabeled data is relatively easy. A desirable approach is building a prediction model on unlabeled data and labeled data. CPDP in this scenario is called cross-project semi-supervised defect prediction (CSDP). Recently, generative adversarial networks have achieved impressive results with these strong ability of learning data distribution and discriminative representation. For effectively learning the discriminative features of data from different projects, we propose a Discriminative Adversarial Feature Learning (DAFL) approach for CSDP. DAFL consists of feature transformer and project discriminator, which compete with each other. A feature transformer tries to generate feature representation, which learns the discriminant information and preserves intrinsic structure inferred from both labeled and unlabeled data. A project discriminator tries to discriminate source and target instances on the generated representation. Experiments on 16 projects show that DAFL performs significantly better than baselines. INDEX TERMS Cross-project defect prediction, adversarial learning, semi-supervised learning.

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Section: Evaluation Measurementioning

confidence: 99%

Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction

Sun

Jing

et al. 2020

IEEE Access

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“…Jing et al [24] proposed a unified metric representation (UMR) for heterogeneous defect data. More researches can be found in [25][26][27]. The experiments on 14 public heterogeneous datasets from four different companies indicated that the proposed approach was more effective in addressing the problem.…”

Section: Related Workmentioning

confidence: 99%

An Improved Method for Cross-Project Defect Prediction by Simplifying Training Data

et al. 2018

Mathematical Problems in Engineering

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Cross-project defect prediction (CPDP) on projects with limited historical data has attracted much attention. To the best of our knowledge, however, the performance of existing approaches is usually poor, because of low quality cross-project training data. The objective of this study is to propose an improved method for CPDP by simplifying training data, labeled as TDSelector, which considers both the similarity and the number of defects that each training instance has (denoted by defects), and to demonstrate the effectiveness of the proposed method. Our work consists of three main steps. First, we constructed TDSelector in terms of a linear weighted function of instances’ similarity and defects. Second, the basic defect predictor used in our experiments was built by using the Logistic Regression classification algorithm. Third, we analyzed the impacts of different combinations of similarity and the normalization of defects on prediction performance and then compared with two existing methods. We evaluated our method on 14 projects collected from two public repositories. The results suggest that the proposed TDSelector method performs, on average, better than both baseline methods, and the AUC values are increased by up to 10.6% and 4.3%, respectively. That is, the inclusion of defects is indeed helpful to select high quality training instances for CPDP. On the other hand, the combination of Euclidean distance and linear normalization is the preferred way for TDSelector. An additional experiment also shows that selecting those instances with more bugs directly as training data can further improve the performance of the bug predictor trained by our method.

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“…We employ two widely used evaluation measures to evaluate the performance of methods: G-measure [15] and AUC [16].…”

Section: Evaluation Measuresmentioning

confidence: 99%

Cross-Project Defect Prediction via Semi-Supervised Discriminative Feature Learning

Xing

Sun

et al. 2020

IEICE Trans. Inf. & Syst.

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Cross-project defect prediction (CPDP) is a feasible solution to build an accurate prediction model without enough historical data. Although existing methods for CPDP that use only labeled data to build the prediction model achieve great results, there are much room left to further improve on prediction performance. In this paper we propose a Semi-Supervised Discriminative Feature Learning (SSDFL) approach for CPDP. SSDFL first transfers knowledge of source and target data into the common space by using a fully-connected neural network to mine potential similarities of source and target data. Next, we reduce the differences of both marginal distributions and conditional distributions between mapped source and target data. We also introduce the discriminative feature learning to make full use of label information, which is that the instances from the same class are close to each other and the instances from different classes are distant from each other. Extensive experiments are conducted on 10 projects from AEEEM and NASA datasets, and the experimental results indicate that our approach obtains better prediction performance than baselines.

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On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction

Cited by 76 publications

References 89 publications

Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction

Adversarial Learning for Cross-Project Semi-Supervised Defect Prediction

An Improved Method for Cross-Project Defect Prediction by Simplifying Training Data

Cross-Project Defect Prediction via Semi-Supervised Discriminative Feature Learning

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