Comparative Analysis of Selected Heterogeneous Classifiers for Software Defects Prediction Using Filter-Based Feature Selection Methods

Akintola, Abimbola G.; Balogun, Abdullateef Oluwagbemiga; Lafenwa-Balogun, Fatimah B.; Mojeed, Hameed A

doi:10.46792/fuoyejet.v3i1.178

Cited by 23 publications

(24 citation statements)

References 6 publications

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“…Our findings on the positive effect of FS methods on prediction models are in accordance with research outcomes from existing empirical studies. Ghotra et al [28], Afzal and Torkar [26], and Akintola et al [18] in their respective studies also reported that FS methods had a positive effect on prediction models in SDP. However, our study explored the effect of FS methods on prediction models based on the search method which is different from existing empirical studies.…”

Section: Resultsmentioning

confidence: 93%

“…Akintola et al [18] performed a comparative analysis of classifiers based on FFS on SDP and their results gave credit to the usage of FFS, but there can still be further analysis using other FS methods. It has been proven empirically that wrappers obtain subsets with better performance than filter feature selection because the subsets were evaluated using a real modeling algorithm [33,34].…”

Section: Related Workmentioning

confidence: 99%

“…The proliferation of these mechanisms consequently generates a large number of metric values, leading to a high-dimensionality problem when many feature values are generated. In addition, some of these features (metrics) may be more relevant to the class (defective or non-defective) than others, and some may be redundant or irrelevant [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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: Resultsmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Akintola et al [9] played out an analysis of classifiers dependent on FFS on SDP and their outcomes offered credit to the use of FFS, yet there can even now be further examination utilizing different FS strategies. It has been demonstrated exactly that wrappers acquire subsets with preferable execution over channel include determination in light of the fact that the subsets were assessed utilizing a genuine modeling algorithm [10,11].…”

Section: Review Of Literaturementioning

confidence: 99%

Svs: Prediction Framework for Software Quality Enhancement through Data Mining Techniques

Visagan¹,

Sumathi²,

Sujatha³

2019

IJEAT

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Software Engineering has its origins in tackling the issue of development and maintenance of quality software. Software Quality has been defined in multiple ways but the broadest definition is that quality is the extent to which the customer is satisfied with the developed software. Data mining has the prospects of being applied to multiple domains and addressing the long standing issues faced by them. It has been successfully applied to uncover solutions to complex problems that have long confronted these domains. The proposed research is a step in the direction. It will attempt to apply existing data mining algorithms to data accumulated by software organizations in an attempt to extract useful patterns that can go a long way in addressing the issue of software quality. This work proposed Spacious Virtue Suggestion (SVS) Model for analyzing code based quality in software quality model. The first layer of this model is Extraction Layer that extracts the various attributes of software code used. After the extraction of the metrics attributes are constructed as a vector is considered as the feature vector for the second layer of the SVS Model. The second layer of the SVS model is Selection Layer which employs feature selection strategy to obtain significant metrics attributes for the software quality prediction by reducing the overlapping metrics attributes from the vector... The third layer of SVS Model is Prediction Layer which predict the good class from the training set and result shows the high accuracy in the proposed system.

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

Balogun

Basri

Jadid

et al. 2020

Intelligent Algorithms in Software Engineering

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Comparative Analysis of Selected Heterogeneous Classifiers for Software Defects Prediction Using Filter-Based Feature Selection Methods

Cited by 23 publications

References 6 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

Svs: Prediction Framework for Software Quality Enhancement through Data Mining Techniques

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

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