A novel Bayes defect predictor based on information diffusion function

Wu, Yaning; Huang, Song; Ji, Haijin; Zheng, Changyou; Bai, Chengzu

doi:10.1016/j.knosys.2017.12.015

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…To analyze the experimental results and demonstrate the superiority of TSboostDF, we used the Wilcoxon rank-sum test, Cliff's delta, and box plots, which are routinely used in SDP studies [17,35,37,38] .…”

Section: Discussionmentioning

confidence: 99%

A novel cross-project software defect prediction algorithm based on transfer learning

Tang

Huang

Zheng

et al. 2022

Tsinghua Sci. Technol.

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Software Defect Prediction (SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years. However, the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets. Another major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction (CPDP). In this work, we propose a transfer-leaning algorithm (TSboostDF) that considers both knowledge transfer and class imbalance for CPDP. The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.

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Section: Discussionmentioning

confidence: 99%

A novel cross-project software defect prediction algorithm based on transfer learning

Tang

Huang

Zheng

et al. 2022

Tsinghua Sci. Technol.

Self Cite

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“…The most suitable releases [ 34 ] from different software projects are selected as training data. We evaluate the performance of different selected methods on SDP in terms of recall (R), precision (P), and F-measure (F) [ 35 , 36 ]. The F-measure is defined as

where

…”

Section: Methodsmentioning

confidence: 99%

Kernel Entropy Component Analysis with Nongreedy L1-Norm Maximization

Huang

2018

Computational Intelligence and Neuroscience

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Kernel entropy component analysis (KECA) is a newly proposed dimensionality reduction (DR) method, which has showed superiority in many pattern analysis issues previously solved by principal component analysis (PCA). The optimized KECA (OKECA) is a state-of-the-art variant of KECA and can return projections retaining more expressive power than KECA. However, OKECA is sensitive to outliers and accused of its high computational complexities due to its inherent properties of L2-norm. To handle these two problems, we develop a new extension to KECA, namely, KECA-L1, for DR or feature extraction. KECA-L1 aims to find a more robust kernel decomposition matrix such that the extracted features retain information potential as much as possible, which is measured by L1-norm. Accordingly, we design a nongreedy iterative algorithm which has much faster convergence than OKECA's. Moreover, a general semisupervised classifier is developed for KECA-based methods and employed into the data classification. Extensive experiments on data classification and software defect prediction demonstrate that our new method is superior to most existing KECA- and PCA-based approaches. Code has been also made publicly available.

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“…Frequency References Data Normalization 11 [20], [55], [56], [72], [76], [79], [85], [88], [92], [104] [57], [80], [84], [87], [89] Data Normalization, and Feature Selection 4 [90], [91], [116], [117] Data Normalization, and Data Filtering 4 [58], [75], [82], [93] Data Imbalance, and Data Filtering 1 [94] Data Filtering, and Feature Selection 3 [31], [97], [100] Data Imbalance, and Feature Selection 1 [40] Data Normalization, Data Imbalance, and Data Filtering 5 [77], [78], [81], [83], [86] Data Normalization, Data Imbalance, and Feature Selection • deep belief network based on abstract syntax tree [108], [113] • correlation-based feature selection for feature subset selection [100], [111] • improved subclass discriminant analysis [61] • information flow algorithm [97] • feature selection using clusters of hybrid-data approach [59] • top-k feature subset based on number of occurrences of different metrics [109] • geodesic flow kernel feature selection [110] • similarity measure …”

Section: Techniquesmentioning

confidence: 99%

Cross-Project Defect Prediction: A Literature Review

Pal

Sillitti²

2022

IEEE Access

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Background: Software defect prediction models aim at identifying the potential faulty modules of a software project based on historical data collected from previous versions of the same project. Due to the lack of availability of software engineering data from the same project, the researchers proposed crossproject defect prediction (CPDP) models where the data collected from one or more projects are used to predict faults in other project. There are a number of approaches proposed with different levels of success and very limited repeatability. Goals: The purpose of this paper is to investigate the existing studies of cross-project models for defect prediction. It synthesizes the literature focusing on characteristics such as project type, software metrics, data preprocessing techniques, features selection approaches, classifiers, and performance measures used. Method: This paper follows the well-known Systematic Literature Review (SLR) approach proposed by Barbara Kitchenham in 2007. Results: Our finding shows that most of the article was published between 2015 and 2021. Moreover, the studies are mostly based on open-source datasets and the software metrics used to create the models are mainly product metrics. We also found out that most studies attempted to improve their models improving data preprocessing and feature selection approaches. Furthermore, logistic regression followed by naive bayes and random forest are the most adopted classifier techniques in such models. Finally, the f-measure followed by recall and AUC are the most preferred evaluation measure used to evaluate the performance of the models. Conclusions: This study provides an overview of the different approaches used to improve the CPDP models analyzing the different techniques used for data preprocessing, feature selection, and the selection of the classifiers. Moreover, we identified some aspects that need further investigation.

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A novel Bayes defect predictor based on information diffusion function

Cited by 9 publications

References 21 publications

A novel cross-project software defect prediction algorithm based on transfer learning

A novel cross-project software defect prediction algorithm based on transfer learning

Kernel Entropy Component Analysis with Nongreedy L1-Norm Maximization

Cross-Project Defect Prediction: A Literature Review

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