Prediction of software defects using Twin Support Vector Machine

Agarwal, Sonali; Tomar, Divya; Siddhant,

doi:10.1109/iciscon.2014.6965232

Cited by 20 publications

(8 citation statements)

References 9 publications

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“…In other words, it can select those features that are more relevant and irredundant to the class label of the dataset among the features. Therefore, introducing FS methods into SDP can solve the high dimensionality problem [17][18][19]. FS is a vital data pre-processing step in classification processes as it improves the quality of data and consequently improves the predictive performance of the prediction models.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

et al. 2019

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Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models, and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

et al. 2019

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“…Eight metrics out of the 22 available metrics were selected, thus reducing the feature space by almost 63%. The selected metrics are 7 predictors (1,4,9,11,14,15,17) in addition to the target attribute 22 (description is shown in Appendix A).…”

Section: Svm Defect Prediction Using a Subset Of Metrics Reduced By Cfs Esmentioning

confidence: 99%

“…A support vector machine (SVM) is a well-known classification technique in machine learning. It has been tested on various applications in pattern recognition, including software defect prediction [4][5][6][7][8][9]. Vapnik developed the principle of SVM in 1995 [10].…”

Section: Introductionmentioning

confidence: 99%

The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study

Alshayeb

Alshammari

2021

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The ongoing development of computer systems requires massive software projects. Running the components of these huge projects for testing purposes might be a costly process; therefore, parameter estimation can be used instead. Software defect prediction models are crucial for software quality assurance. This study investigates the impact of dataset size and feature selection algorithms on software defect prediction models. We use two approaches to build software defect prediction models: a statistical approach and a machine learning approach with support vector machines (SVMs). The fault prediction model was built based on four datasets of different sizes. Additionally, four feature selection algorithms were used. We found that applying the SVM defect prediction model on datasets with a reduced number of measures as features may enhance the accuracy of the fault prediction model. Also, it directs the test effort to maintain the most influential set of metrics. We also found that the running time of the SVM fault prediction model is not consistent with dataset size. Therefore, having fewer metrics does not guarantee a shorter execution time. From the experiments, we found that dataset size has a direct influence on the SVM fault prediction model. However, reduced datasets performed the same or slightly lower than the original datasets.

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“…SVM has remarkable advantages as it utilizes the idea of structural risk minimization (SRM) principle which provides better generalization as well as reduces error in the training phase. As a result of its superior performance even in non-linear classification problems, it has been implemented in a diverse spectrum of research fields, ranging from text classification, face recognition, financial application, brain-computer interface, bio-medicine to human action recognition [1,12,54,93,99,100,148,188,193,204,205]. Although SVM has outperformed most other systems, it still has many limitations in dealing with complex data due to its high computational cost of solving QPPs and its performance highly depends upon the choice of kernel functions and its parameters.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review On Twin Support Vector Machines

Tanveer,

Rajani,

Rastogi

et al. 2021

Preprint

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Twin support vector machine (TSVM) and twin support vector regression (TSVR) are newly emerging efficient machine learning techniques which offer promising solutions for classification and regression challenges respectively. TSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. It requires to solve two small sized quadratic programming problems (QPPs) in lieu of solving single large size QPP in support vector machine (SVM) while TSVR is formulated on the lines of TSVM and requires to solve two SVM kind problems. Although there has been good research progress on these techniques; there is limited literature on the comparison of different variants of TSVR. Thus, this review presents a rigorous analysis of recent research in TSVM and TSVR simultaneously mentioning their limitations and advantages. To begin with we first introduce the basic theory of TSVM and then focus on the various improvements and applications of TSVM, and then we introduce TSVR and its various enhancements. Finally, we suggest future research and development prospects.

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Prediction of software defects using Twin Support Vector Machine

Cited by 20 publications

References 9 publications

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

The Effect of the Dataset Size on the Accuracy of Software Defect Prediction Models: An Empirical Study

Comprehensive Review On Twin Support Vector Machines

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