Software defect prediction based on kernel PCA and weighted extreme learning machine

Zhou, Xu; Liu, Jin; Luo, Xiapu; Yang, Zijiang; Zhang, Yifeng; Yuan, Peipei; Tang, Yutian; Zhang, Tao

doi:10.1016/j.infsof.2018.10.004

Cited by 136 publications

(51 citation statements)

References 48 publications

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“…To evaluate the proposed CFIW‐TNB, we selected 25 different open‐projects from the PROMISE data repository collected by Jureczko [35]; and they are extensively adopted in previous empirical studies [3,4,36]. Each module in these projects contain 20 static code features and a defective label (defective or clean).…”

Section: Methodsmentioning

confidence: 99%

Correlation feature and instance weights transfer learning for cross project software defect prediction

Zou

Qiu

et al. 2021

IET Software

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Due to the differentiation between training and testing data in the feature space, crossproject defect prediction (CPDP) remains unaddressed within the field of traditional machine learning. Recently, transfer learning has become a research hot-spot for building classifiers in the target domain using the data from the related source domains. To implement better CPDP models, recent studies focus on either feature transferring or instance transferring to weaken the impact of irrelevant cross-project data. Instead, this work proposes a dual weighting mechanism to aid the learning process, considering both feature transferring and instance transferring. In our method, a local data gravitation between source and target domains determines instance weight, while features that are highly correlated with the learning task, uncorrelated with other features and minimizing the difference between the domains are rewarded with a higher feature weight. Experiments on 25 real-world datasets indicate that the proposed approach outperforms the existing CPDP methods in most cases. By assigning weights based on the different contribution of features and instances to the predictor, the proposed approach is able to build a better CPDP model and demonstrates substantial improvements over the stateof-the-art CPDP models.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 99%

Correlation feature and instance weights transfer learning for cross project software defect prediction

Zou

Qiu

et al. 2021

IET Software

View full text Add to dashboard Cite

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“…So now reducing dimensions is needed to be done, which often refers to PCA in unsupervised modeling. PCA, principal component analysis, is a kind of mathematical method mainly to reduce dimensions and remove linear correlations [6]. First, assume that every record represents a point in a high dimensional space, so the data will be distributed like this (three dimensions): The data spreads differently in each direction, which means that it has different variance on each dimension.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Variables Creation in Fraud Detection-Based on New York Property Data

Yao¹

2020

Proceedings of the 2019 International Conference on Education Science and Economic Development (ICESED 2019)

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With the rapid development of technology, fraud has become an extremely serious problem in the US society, therefore makes fraud detection an urgent need to improve the situation as much as possible. Fraud detection refers to the process of finding anomalies in a huge bunch of data by building fraud algorithms and models that could predict the possible behaviour of the real world situation. This paper will mainly focus on the 'Variables Creation' process in fraud detection with the New York property data and discuss how to analyze a fraud problem and build variables based on New York property data. The whole process of fraud detection will render practical help to solve the property fraud problem.

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“…In the traditional works, Ohlsson et al [8] proposed the implementation of linear discriminant analysis in order to predict the defect in the source code of software. Meanwhile, Xu et al [9] exploited the power of decision trees to solve the problem. Similarly Xing et al [10] shifted the focus towards solving the problem by utilization of an SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As described earlier, there is much work available on the JIT effort aware system by using the traditional file, package or method level for the defect prediction [8][9][10] as well as supervised machine learning methods and unsupervised learning methods. Still, there is a huge gap in accuracy, and false prediction.…”

Section: Introductionmentioning

confidence: 99%

A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

Albahli

2019

Future Internet

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Since the introduction of just-in-time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods, which can predict the defect inducing changes in a software product. In order to predict these changes, it is important for a learning model to consider the nature of the dataset, its unbalancing properties and the correlation between different attributes. In this paper, we evaluated the importance of these properties for a specific dataset and proposed a novel methodology for learning the effort aware just-in-time prediction of defect inducing changes. Moreover, we devised an ensemble classifier, which fuses the output of three individual classifiers (Random forest, XGBoost, Multi-layer perceptron) to build an efficient state-of-the-art prediction model. The experimental analysis of the proposed methodology showed significant performance with 77% accuracy on the sample dataset and 81% accuracy on different datasets. Furthermore, we proposed a highly competent reinforcement learning technique to avoid false alarms in real time predictions.

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Software defect prediction based on kernel PCA and weighted extreme learning machine

Cited by 136 publications

References 48 publications

Correlation feature and instance weights transfer learning for cross project software defect prediction

Correlation feature and instance weights transfer learning for cross project software defect prediction

Variables Creation in Fraud Detection-Based on New York Property Data

A Deep Ensemble Learning Method for Effort-Aware Just-In-Time Defect Prediction

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