scholarly journals COMPARATIVE ANALYSIS OF CLASSIFICATION METHODS FOR PREDICTION SOFTWARE FAULT PRONENESS USING PROCESS METRICS

2021 ◽  
Author(s):  
Anjali Bansal
Keyword(s):  
Prediction Performance ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Software Defect ◽  
Ensemble Techniques ◽  
Code Metrics ◽  
Independent Variable ◽  
Software Fault ◽  
Process Metrics

As we all know a lot of research has been done in the field of software defect prediction but most of them uses static code metrics as their independent variable. In this paper the main objective is to analyze the effect of process metrics on prediction performance using various classification and ensemble techniques. Also in this i have used both AUC and MCC measure to analyze the results. We can conclude that process metrics are as effective as static code metrics.

scholarly journals COMPARATIVE ANALYSIS OF CLASSIFICATION METHODS FOR PREDICTION SOFTWARE FAULT PRONENESS USING PROCESS METRICS

2021 ◽  
Author(s):  
Anjali Bansal
Keyword(s):  
Prediction Performance ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Software Defect ◽  
Ensemble Techniques ◽  
Code Metrics ◽  
Independent Variable ◽  
Software Fault ◽  
Process Metrics

As we all know a lot of research has been done in the field of software defect prediction but most of them uses static code metrics as their independent variable. In this paper the main objective is to analyze the effect of process metrics on prediction performance using various classification and ensemble techniques. Also in this i have used both AUC and MCC measure to analyze the results. We can conclude that process metrics are as effective as static code metrics.

A Comparison of Semi-Supervised Classification Approaches for Software Defect Prediction

2014 ◽  
Vol 23 (1) ◽  
pp. 75-82 ◽  
Author(s):  
Cagatay Catal
Keyword(s):  
Supervised Classification ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Data Set ◽  
Software Defect ◽  
Data Points ◽  
Supervised Classification Methods ◽  
Prediction Approach

AbstractPredicting the defect-prone modules when the previous defect labels of modules are limited is a challenging problem encountered in the software industry. Supervised classification approaches cannot build high-performance prediction models with few defect data, leading to the need for new methods, techniques, and tools. One solution is to combine labeled data points with unlabeled data points during learning phase. Semi-supervised classification methods use not only labeled data points but also unlabeled ones to improve the generalization capability. In this study, we evaluated four semi-supervised classification methods for semi-supervised defect prediction. Low-density separation (LDS), support vector machine (SVM), expectation-maximization (EM-SEMI), and class mass normalization (CMN) methods have been investigated on NASA data sets, which are CM1, KC1, KC2, and PC1. Experimental results showed that SVM and LDS algorithms outperform CMN and EM-SEMI algorithms. In addition, LDS algorithm performs much better than SVM when the data set is large. In this study, the LDS-based prediction approach is suggested for software defect prediction when there are limited fault data.

Software Defect Prediction Using Genetic Programming and Neural Networks

2020 ◽  
pp. 1577-1597
Author(s):  
Mohammed Akour ◽  
Wasen Yahya Melhem
Keyword(s):  
Neural Networks ◽  
Genetic Programming ◽  
Detailed Analysis ◽  
Software Quality ◽  
The Other ◽  
Defect Prediction ◽  
Data Repository ◽  
Software Defect Prediction ◽  
Classification Methods ◽  
Software Defect

This article describes how classification methods on software defect prediction is widely researched due to the need to increase the software quality and decrease testing efforts. However, findings of past researches done on this issue has not shown any classifier which proves to be superior to the other. Additionally, there is a lack of research that studies the effects and accuracy of genetic programming on software defect prediction. To find solutions for this problem, a comparative software defect prediction experiment between genetic programming and neural networks are performed on four datasets from the NASA Metrics Data repository. Generally, an interesting degree of accuracy is detected, which shows how the metric-based classification is useful. Nevertheless, this article specifies that the application and usage of genetic programming is highly recommended due to the detailed analysis it provides, as well as an important feature in this classification method which allows the viewing of each attributes impact in the dataset.

scholarly journals Process metrics for software defect prediction in object‐oriented programs

IET Software ◽  
2020 ◽  
Vol 14 (3) ◽  
pp. 283-292
Author(s):  
Qiao Yu ◽  
Shujuan Jiang ◽  
Junyan Qian ◽  
Lili Bo ◽  
Li Jiang ◽  
...  
Keyword(s):  
Object Oriented ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Process Metrics

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.

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