scholarly journals Empirical Analysis of Rank Aggregation-Based Multi-Filter Feature Selection Methods in Software Defect Prediction

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 179
Author(s):  
Abdullateef O. Balogun ◽  
Shuib Basri ◽  
Saipunidzam Mahamad ◽  
Said Jadid Abdulkadir ◽  
Luiz Fernando Capretz ◽  
...  
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Rank Aggregation ◽  
Selection Problem ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Filter Methods ◽  
Rank List ◽  
Viable Solution

Selecting the most suitable filter method that will produce a subset of features with the best performance remains an open problem that is known as filter rank selection problem. A viable solution to this problem is to independently apply a mixture of filter methods and evaluate the results. This study proposes novel rank aggregation-based multi-filter feature selection (FS) methods to address high dimensionality and filter rank selection problem in software defect prediction (SDP). The proposed methods combine rank lists generated by individual filter methods using rank aggregation mechanisms into a single aggregated rank list. The proposed methods aim to resolve the filter selection problem by using multiple filter methods of diverse computational characteristics to produce a dis-joint and complete feature rank list superior to individual filter rank methods. The effectiveness of the proposed method was evaluated with Decision Tree (DT) and Naïve Bayes (NB) models on defect datasets from NASA repository. From the experimental results, the proposed methods had a superior impact (positive) on prediction performances of NB and DT models than other experimented FS methods. This makes the combination of filter rank methods a viable solution to filter rank selection problem and enhancement of prediction models in SDP.

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 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.

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 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.

scholarly journals Incremental Feature Selection Method for Software Defect Prediction

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1345-1353 ◽  
Keyword(s):  
Feature Selection ◽  
Software Metrics ◽  
Prediction Models ◽  
Search Algorithm ◽  
Feature Selection Method ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Defect Prediction Models ◽  
Selection Of

Software defect prediction models are essential for understanding quality attributes relevant for software organization to deliver better software reliability. This paper focuses mainly based on the selection of attributes in the perspective of software quality estimation for incremental database. A new dimensionality reduction method Wilk’s Lambda Average Threshold (WLAT) is presented for selection of optimal features which are used for classifying modules as fault prone or not. This paper uses software metrics and defect data collected from benchmark data sets. The comparative results confirm that the statistical search algorithm (WLAT) outperforms the other relevant feature selection methods for most classifiers. The main advantage of the proposed WLAT method is: The selected features can be reused when there is increase or decrease in database size, without the need of extracting features afresh. In addition, performances of the defect prediction models either remains unchanged or improved even after eliminating 85% of the software metrics.

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

2021 ◽  
Vol 24 (68) ◽  
pp. 72-88
Author(s):  
Mohammad Alshayeb ◽  
Mashaan A. Alshammari
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Prediction Models ◽  
Fault Prediction ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Dataset Size ◽  
Defect Prediction Models ◽  
Selection Algorithms

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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