21,874 research outputs found

    Ensemble multiboost based on ripper classifier for prediction of imbalanced software defect data

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    Identifying defective software entities is essential to ensure software quality during software development. However, the high dimensionality and class distribution imbalance of software defect data seriously affect software defect prediction performance. In order to solve this problem, this paper proposes an Ensemble MultiBoost based on RIPPER classifier for prediction of imbalanced Software Defect data, called EMR_SD. Firstly, the algorithm uses principal component analysis (PCA) method to find out the most effective features from the original features of the data set, so as to achieve the purpose of dimensionality reduction and redundancy removal. Furthermore, the combined sampling method of adaptive synthetic sampling (ADASYN) and random sampling without replacement is performed to solve the problem of data class imbalance. This classifier establishes association rules based on attributes and classes, using MultiBoost to reduce deviation and variance, so as to achieve the purpose of reducing classification error. The proposed prediction model is evaluated experimentally on the NASA MDP public datasets and compared with existing similar algorithms. The results show that EMR-SD algorithm is superior to DNC, CEL and other defect prediction techniques in most evaluation indicators, which proves the effectiveness of the algorithm

    Heterogeneous Cross-Project Defect Prediction using Encoder and Transfer Learning

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    Heterogeneous cross-project defect prediction (HCPDP) aims to predict defects in new software projects using defect data from previous software projects where the source and target projects have some different metrics. Most existing methods only find linear relationships in the software defect features and datasets. Additionally, these methods use multiple defect datasets from different projects as source datasets. In this paper, we propose a novel method called heterogeneous cross-project defect prediction using encoder and transfer learning (ETL). ETL uses encoders to extract the important features from source and target datasets. Also, to minimize negative transfer during transfer learning, we used an augmented dataset that contains pseudo-labels and the source dataset. Additionally, we have used very limited data to train the model. To evaluate the performance of the ETL approach, 16 datasets from four publicly available software defect projects were used. Furthermore, we compared the proposed method with four HCPDP methods namely EGW, HDP&amp;#x005F;KS, CTKCCA and EMKCA, and one WPDP method from existing literature. The proposed method on average outperforms the baseline methods in terms of PD, PF, F1-score, G-mean and AUC.</p
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