MVSE: Effort-Aware Heterogeneous Defect Prediction via Multiple-View Spectral Embedding

Zhou, Xu; Ye, Sizhe; Zhang, Tao; Xia, Zhen; Pang, Shaoning; Wang, Yong; Tang, Yutian

doi:10.1109/qrs.2019.00015

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…It is utilized to embed cross-project data into a low-dimensional comparable feature space, followed by the measurement of the dissimilarity between the mapped domains based on dictionary learning techniques. Subsequently, in [19], they proposed a novel Multiple-View Spectral Embedding (MVSE) approach for HDP. MVSE combines multiple views of the data into a unified low-dimensional depiction that aptly captures the underlying structure of the data.…”

Section: Related Work 21 Heterogeneous Defect Predictionmentioning

confidence: 99%

“…To implement CCA+, HDP-KS, CTKCCA, MHCPDP, and WPDP, we employed Python programming, followed the prescribed settings outlined in their respective papers. We applied Z-score normalization to all data sets prior to executing these algorithms, as it is widely utilized in the field of software defect prediction [10,13,[15][16][17][18][19]21,43]. For the kernel function in MSHKM, the Gaussian kernel function was used.…”

Section: Experimental Designmentioning

confidence: 99%

“…For this situation, traditional CPDP methods are not applicable and have limitations. To solve the inconsistency of metrics between source and target projects, researchers have proposed heterogeneous defect prediction (HDP) approaches [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction

Yao

Liu

et al. 2023

Applied Sciences

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Heterogeneous defect prediction (HDP) is a significant research topic in cross-project defect prediction (CPDP), due to the inconsistency of metrics used between source and target projects. While most HDP methods aim to improve the performance of models trained on data from one source project, few studies have investigated how the number of source projects affects predictive performance. In this paper, we propose a new multi-source heterogeneous kernel mapping (MSHKM) algorithm to analyze the effects of different numbers of source projects on prediction results. First, we introduce two strategies based on MSHKM for multi-source HDP. To determine the impact of the number of source projects on the predictive performance of the model, we regularly vary the number of source projects in each strategy. Then, we compare the proposed MSHKM with state-of-the-art HDP methods and within-project defect prediction (WPDP) methods, in terms of three common performance measures, using 28 data sets from five widely used projects. Our results demonstrate that, (1) in the multi-source HDP scenario, strategy 2 outperforms strategy 1; (2) for MSHKM, a lower number of source projects leads to better results and performance under strategy 1, while n = 4 is the optimal number under strategy 2; (3) MSHKM performs better than related state-of-the-art HDP methods; and (4) MSHKM outperforms WPDP. In summary, our proposed MSHKM algorithm provides a promising solution for heterogeneous cross-project defect prediction, and our findings suggest that the number of source projects should be carefully selected to achieve optimal predictive performance.

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Section: Related Work 21 Heterogeneous Defect Predictionmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

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Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction

Yao

Liu

et al. 2023

Applied Sciences

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“…The main idea was to check if different models predict the same defective modules. Xu et al [14] considered the possibility of heterogeneous features in cross-project data and proposed a method called Multiple View Spectral Embedding (MVSE). They saw data in two different views and mapped it on a consistent space with maximum similarity.…”

Section: Previous Work 21 Just-in-time Software Defect Predictionmentioning

confidence: 99%

Defect Prediction Using Akaike and Bayesian Information Criterion

Albahli¹,

Yar²

2022

Computer Systems Science and Engineering

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“…Xu et al embedded the data from the two domains into a comparable feature space with a low dimensional, measures the difference between the two mapped domains of data using the dictionaries learned from them with the dictionary learning technique [10]. Xu et al used the spectrum embedding to map the source project and the target project from the high-dimensional space to the low-dimensional consistent space [11].Transfer learning is introduced into HDP to reduce the problem of data difference, which no longer requires two projects have the same feature dimension and distribution. Transfer learning is an important branch of machine learning.…”

mentioning

confidence: 99%

Heterogeneous Defect Prediction Based on Transfer Learning to Handle Extreme Imbalance

Jiang

Zhang

et al. 2020

Applied Sciences

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Software systems are now ubiquitous and are used every day for automation purposes in personal and enterprise applications; they are also essential to many safety-critical and mission-critical systems, e.g., air traffic control systems, autonomous cars, and Supervisory Control And Data Acquisition (SCADA) systems. With the availability of massive storage capabilities, high speed Internet, and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Maintaining a high quality of such complex systems while manually keeping the error rate at a minimum is a challenge. This paper proposed a heterogeneous defect prediction method considering class extreme imbalance problem in real software datasets. In the first stage, Sampling with the Majority method (SWIM) based on Mahalanobis Distance is used to balance the dataset to reduce the influence of minority samples in defect data. Due to the negative impact of uncorrelated features on the classification algorithm, the second stage uses ensemble learning and joint similarity measurement to select the most relevant and representative features between the source project and the target project. The third phase realizes the transfer learning from the source project to the target project in the Grassmann manifold space. Our experiments, conducted using nine projects of three public domain software defect libraries and compared with four existing advanced methods to verify the effectiveness of the proposed method in this paper. The experimental results indicate that the proposed method is more accurate in terms of Area under curve (AUC).Appl. Sci. 2020, 10, 396 2 of 15 identification of the defective samples. Although the misclassification of defective samples does not significantly reduce the global classification accuracy, the accuracy of defective samples will decline, which is inconsistent with the goal of software defect prediction. Zhou et al. proposed a model which combined attribute selection, sampling technologies and ensemble algorithm to solve the class imbalance problem [4]. Huda et al. introduced a new mixed sampling strategy to generate more pseudo samples from defective classes, and combined random oversampling, Majority Weighted Minority Oversampling Technique, and Fuzzy-Based Feature-Instance Recovery to construct an integrated classifier [5]. It was proven that the prediction performance of Heterogeneous Defect Prediction (HDP) can be improved by balancing defect dataset.At present, the research on SDP is mainly based on the defect prediction of homogeneous projects, which uses historical data of other projects to construct prediction model. The historical data have the same metrics as the target project, but they are distributed differently. Sufficient historical data are provided for the project to be predicted. However, the programming languages and application fields of different projects are often different, and the corresponding features and distribution are various. It is very difficult to construct a mod...

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MVSE: Effort-Aware Heterogeneous Defect Prediction via Multiple-View Spectral Embedding

Cited by 9 publications

References 35 publications

Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction

Multi-Source Heterogeneous Kernel Mapping in Software Defect Prediction

Defect Prediction Using Akaike and Bayesian Information Criterion

Heterogeneous Defect Prediction Based on Transfer Learning to Handle Extreme Imbalance

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