scholarly journals Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Reranking Strategy

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Abdullateef Oluwagbemiga Balogun ◽  
Shuib Basri ◽  
Luiz Fernando Capretz ◽  
Saipunidzam Mahamad ◽  
Abdullahi Abubakar Imam ◽  
...  
Keyword(s):  
Feature Selection ◽  
Computational Cost ◽  
High Dimensionality ◽  
Computational Time ◽  
Defect Prediction ◽  
Feature Subset ◽  
Sequential Search ◽  
Software Defect Prediction ◽  
Local Maxima ◽  
Software Defect

Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.

The Use of Ensemble-Based Data Preprocessing Techniques for Software Defect Prediction

2014 ◽  
Vol 24 (09) ◽  
pp. 1229-1253 ◽  
Author(s):  
Kehan Gao ◽  
Taghi M. Khoshgoftaar ◽  
Amri Napolitano
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Measurement Data ◽  
Class Imbalance ◽  
Data Preprocessing ◽  
High Dimensionality ◽  
Training Dataset ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect

Software defect prediction models that use software metrics such as code-level measurements and defect data to build classification models are useful tools for identifying potentially-problematic program modules. Effectiveness of detecting such modules is affected by the software measurements used, making data preprocessing an important step during software quality prediction. Generally, there are two problems affecting software measurement data: high dimensionality (where a training dataset has an extremely large number of independent attributes, or features) and class imbalance (where a training dataset has one class with relatively many more members than the other class). In this paper, we present a novel form of ensemble learning based on boosting that incorporates data sampling to alleviate class imbalance and feature (software metric) selection to address high dimensionality. As we adopt two different sampling methods (Random Undersampling (RUS) and Synthetic Minority Oversampling (SMOTE)) in the technique, we have two forms of our new ensemble-based approach: selectRUSBoost and selectSMOTEBoost. To evaluate the effectiveness of these new techniques, we apply them to two groups of datasets from two real-world software systems. In the experiments, four learners and nine feature selection techniques are employed to build our models. We also consider versions of the technique which do not incorporate feature selection, and compare all four techniques (the two different ensemble-based approaches which utilize feature selection and the two versions which use sampling only). The experimental results demonstrate that selectRUSBoost is generally more effective in improving defect prediction performance than selectSMOTEBoost, and that the techniques with feature selection do help for getting better prediction than the techniques without feature selection.

scholarly journals A Novel Feature Selection Method Based on Maximum Likelihood Logistic Regression for Imbalanced Learning in Software Defect Prediction

2020 ◽  
Vol 17 (5) ◽  
pp. 721-730
Author(s):  
Kamal Bashir ◽  
Tianrui Li ◽  
Mahama Yahaya
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Feature Selection ◽  
Maximum Likelihood ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Optimal Feature Subset ◽  
Optimal Feature

The most frequently used machine learning feature ranking approaches failed to present optimal feature subset for accurate prediction of defective software modules in out-of-sample data. Machine learning Feature Selection (FS) algorithms such as Chi-Square (CS), Information Gain (IG), Gain Ratio (GR), RelieF (RF) and Symmetric Uncertainty (SU) perform relatively poor at prediction, even after balancing class distribution in the training data. In this study, we propose a novel FS method based on the Maximum Likelihood Logistic Regression (MLLR). We apply this method on six software defect datasets in their sampled and unsampled forms to select useful features for classification in the context of Software Defect Prediction (SDP). The Support Vector Machine (SVM) and Random Forest (RaF) classifiers are applied on the FS subsets that are based on sampled and unsampled datasets. The performance of the models captured using Area Ander Receiver Operating Characteristics Curve (AUC) metrics are compared for all FS methods considered. The Analysis Of Variance (ANOVA) F-test results validate the superiority of the proposed method over all the FS techniques, both in sampled and unsampled data. The results confirm that the MLLR can be useful in selecting optimal feature subset for more accurate prediction of defective modules in software development process

scholarly journals A Novel Rank Aggregation-Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Abdullateef O. Balogun ◽  
Shuib Basri ◽  
Saipunidzam Mahamad ◽  
Luiz Fernando Capretz ◽  
Abdullahi Abubakar Imam ◽  
...  
Keyword(s):  
Feature Selection ◽  
Rank Aggregation ◽  
Feature Subset Selection ◽  
Selection Problem ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Local Optima ◽  
Software Defect ◽  
Wrapper Feature Selection

The high dimensionality of software metric features has long been noted as a data quality problem that affects the performance of software defect prediction (SDP) models. This drawback makes it necessary to apply feature selection (FS) algorithm(s) in SDP processes. FS approaches can be categorized into three types, namely, filter FS (FFS), wrapper FS (WFS), and hybrid FS (HFS). HFS has been established as superior because it combines the strength of both FFS and WFS methods. However, selecting the most appropriate FFS (filter rank selection problem) for HFS is a challenge because the performance of FFS methods depends on the choice of datasets and classifiers. In addition, the local optima stagnation and high computational costs of WFS due to large search spaces are inherited by the HFS method. Therefore, as a solution, this study proposes a novel rank aggregation-based hybrid multifilter wrapper feature selection (RAHMFWFS) method for the selection of relevant and irredundant features from software defect datasets. The proposed RAHMFWFS is divided into two stepwise stages. The first stage involves a rank aggregation-based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non-disjoint rank list. In the second stage, the aggregated ranked features are further preprocessed by an enhanced wrapper feature selection (EWFS) method based on a dynamic reranking strategy that is used to guide the feature subset selection process of the HFS method. This, in turn, reduces the number of evaluation cycles while amplifying or maintaining its prediction performance. The feasibility of the proposed RAHMFWFS was demonstrated on benchmarked software defect datasets with Naïve Bayes and Decision Tree classifiers, based on accuracy, the area under the curve (AUC), and F-measure values. The experimental results showed the effectiveness of RAHMFWFS in addressing filter rank selection and local optima stagnation problems in HFS, as well as the ability to select optimal features from SDP datasets while maintaining or enhancing the performance of SDP models. To conclude, the proposed RAHMFWFS achieved good performance by improving the prediction performances of SDP models across the selected datasets, compared to existing state-of-the-arts HFS methods.

scholarly journals An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1274
Author(s):  
Abdullateef O. Balogun ◽  
Shuib Basri ◽  
Luiz Fernando Capretz ◽  
Saipunidzam Mahamad ◽  
Abdullahi A. Imam ◽  
...  
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Rank Aggregation ◽  
Selection Problem ◽  
High Dimensionality ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Filter Methods ◽  
Superior Effect

Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and naïve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter–filter relationships in selecting optimal features for SDP processes.

scholarly journals RFC: A feature selection algorithm for software defect prediction

2021 ◽  
Vol 32 (2) ◽  
pp. 389-398
Author(s):  
Xu Xiaolong ◽  
Chen Wen ◽  
Wang Xinheng
Keyword(s):  
Feature Selection ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Software Defect

Handling High-Dimensionality on Software Defect Prediction with FLDA

2021 ◽  
Author(s):  
Rizal Broer Bahaweres ◽  
Ellrica Dewi Herawati Jana ◽  
Irman Hermadi ◽  
Arif Imam Suroso ◽  
Yandra Arkeman
Keyword(s):  
High Dimensionality ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect

Search-Based Wrapper Feature Selection Methods in Software Defect Prediction: An Empirical Analysis

2020 ◽  
pp. 492-503 ◽  
Author(s):  
Abdullateef O. Balogun ◽  
Shuib Basri ◽  
Said A. Jadid ◽  
Saipunidzam Mahamad ◽  
Malek A. Al-momani ◽  
...  
Keyword(s):  
Feature Selection ◽  
Empirical Analysis ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Selection Methods ◽  
Software Defect ◽  
Wrapper Feature Selection

scholarly journals A Feature Selection Based Model for Software Defect Prediction

2014 ◽  
Vol 65 ◽  
pp. 39-58 ◽  
Author(s):  
Sonali Agarwal ◽  
Divya Tomar
Keyword(s):  
Feature Selection ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect
Sign in / Sign up

Export Citation Format

Share Document