scholarly journals An empirical study on software defect prediction with a simplified metric set

2015 ◽  
Vol 59 ◽  
pp. 170-190 ◽  
Author(s):  
Peng He ◽  
Bing Li ◽  
Xiao Liu ◽  
Jun Chen ◽  
Yutao Ma
Keyword(s):  
Empirical Study ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Metric Set

scholarly journals Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

IET Software ◽  
2020 ◽  
Vol 14 (7) ◽  
pp. 768-782
Author(s):  
Sushant Kumar Pandey ◽  
Deevashwer Rathee ◽  
Anil Kumar Tripathi
Keyword(s):  
Empirical Study ◽  
Extreme Learning Machine ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Learning Machine

An empirical study on pareto based multi-objective feature selection for software defect prediction

2019 ◽  
Vol 152 ◽  
pp. 215-238 ◽  
Author(s):  
Chao Ni ◽  
Xiang Chen ◽  
Fangfang Wu ◽  
Yuxiang Shen ◽  
Qing Gu
Keyword(s):  
Feature Selection ◽  
Empirical Study ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Multi Objective ◽  
Software Defect ◽  
Selection For

scholarly journals An Empirical Study on Software Defect Prediction Using CodeBERT Model

2021 ◽  
Vol 11 (11) ◽  
pp. 4793
Author(s):  
Cong Pan ◽  
Minyan Lu ◽  
Biao Xu
Keyword(s):  
Deep Learning ◽  
Software Engineering ◽  
Empirical Study ◽  
Empirical Studies ◽  
Language Model ◽  
Prediction Performance ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Cross Project

Deep learning-based software defect prediction has been popular these days. Recently, the publishing of the CodeBERT model has made it possible to perform many software engineering tasks. We propose various CodeBERT models targeting software defect prediction, including CodeBERT-NT, CodeBERT-PS, CodeBERT-PK, and CodeBERT-PT. We perform empirical studies using such models in cross-version and cross-project software defect prediction to investigate if using a neural language model like CodeBERT could improve prediction performance. We also investigate the effects of different prediction patterns in software defect prediction using CodeBERT models. The empirical results are further discussed.

scholarly journals Impact of Feature Selection Methods on the Predictive Performance of Software Defect Prediction Models: An Extensive Empirical Study

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1147 ◽  
Author(s):  
Abdullateef O. Balogun ◽  
Shuib Basri ◽  
Saipunidzam Mahamad ◽  
Said J. Abdulkadir ◽  
Malek A. Almomani ◽  
...  
Keyword(s):  
Feature Selection ◽  
Empirical Study ◽  
Prediction Models ◽  
Empirical Studies ◽  
Experimental Results ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Search Methods ◽  
Software Defect ◽  
The Impact

Feature selection (FS) is a feasible solution for mitigating high dimensionality problem, and many FS methods have been proposed in the context of software defect prediction (SDP). Moreover, many empirical studies on the impact and effectiveness of FS methods on SDP models often lead to contradictory experimental results and inconsistent findings. These contradictions can be attributed to relative study limitations such as small datasets, limited FS search methods, and unsuitable prediction models in the respective scope of studies. It is hence critical to conduct an extensive empirical study to address these contradictions to guide researchers and buttress the scientific tenacity of experimental conclusions. In this study, we investigated the impact of 46 FS methods using Naïve Bayes and Decision Tree classifiers over 25 software defect datasets from 4 software repositories (NASA, PROMISE, ReLink, and AEEEM). The ensuing prediction models were evaluated based on accuracy and AUC values. Scott–KnottESD and the novel Double Scott–KnottESD rank statistical methods were used for statistical ranking of the studied FS methods. The experimental results showed that there is no one best FS method as their respective performances depends on the choice of classifiers, performance evaluation metrics, and dataset. However, we recommend the use of statistical-based, probability-based, and classifier-based filter feature ranking (FFR) methods, respectively, in SDP. For filter subset selection (FSS) methods, correlation-based feature selection (CFS) with metaheuristic search methods is recommended. For wrapper feature selection (WFS) methods, the IWSS-based WFS method is recommended as it outperforms the conventional SFS and LHS-based WFS methods.

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