Application of Random Vector Functional Link Network for Software Defect Prediction

Malhotra, Ruchika; Aggarwal, Deepti; Garg, Priya

doi:10.1007/978-981-16-3097-2_11

Cited by 2 publications

(5 citation statements)

References 21 publications

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“…Malhotra et al [39] 6 NNs AUC, Friedman chi-squared test, and the Wilcoxon signed-rank test Statistical significance was evaluated.…”

Section: Methods Evaluation Measures Gapsmentioning

confidence: 99%

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Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Babatunde,

Ogundokun,

Adeoye

et al. 2023

Mathematics

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To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

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“…Malhotra et al [39] 6 NNs AUC, Friedman chi-squared test, and the Wilcoxon signed-rank test Statistical significance was evaluated.…”

Section: Methods Evaluation Measures Gapsmentioning

confidence: 99%

“…In a stringent CPDP environment, Malhotra, Khan, and Khera [39] developed a testing approach based on six distinct neural networks (NNs) on a dataset, including 20 softwares from the PROMISE repository. The optimum design was then compared with three suggested CPDP approaches that span a wide variety of circumstances.…”

Section: Related Workmentioning

confidence: 99%

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Babatunde,

Ogundokun,

Adeoye

et al. 2023

Mathematics

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“…Notably, six papers [26], [28], [37], [40], [45], [46] combine more than one category, such as (solve class imbalance issue & propose a new approach or edit an existing approach) or (feature selection & propose a new approach or edit an existing approach), already presented in previous categories.…”

Section: Edit An Existing Approach or Propose A New Approachmentioning

confidence: 99%

“…Redaktor [11], [12], [15], [20], [22], [38] 6 8% Prop [16], [37], [45], [68]-[70] 6 8% Columba (COL) [13], [42], [44], [59], [69] 5 7% Mozilla (MOZ) [13], [42], [44], [59], [69] 5 7%…”

Section: Datasetsmentioning

confidence: 99%

“…An SDP methodology based on heterogeneous feature selection and nested-stacking was suggested by the authors of [44]. A unique SDP method based on PCA and RVFL was proposed by the authors of [45]. The authors of [46] suggested a hybrid classifier that uses a bug tracker application to track and collect defect metrics while gaining semantic knowledge of the traits of the errors by employing an ontology of software flaws.…”

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confidence: 99%

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Software Defect Prediction Approaches Revisited

Shebl,

Afify,

Badr

2023

IJICIS

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A crucial field in software development and testing is Software Defect Prediction (SDP) because the quality, dependability, efficiency, and cost of the software are all improved by forecasting software defects at an earlier stage. Many existing models predict defects to facilitate software testing process for testers. A comprehensive review of these models from different perspectives is crucial to help new researchers enter this field and learn about its latest developments. Algorithms, method types, datasets, and tools were the only perspectives discussed in the current literature. A comprehensive study that takes into account a wide spectrum of viewpoints hasn't yet been published. Examining the development and advancement of SDP-related studies is the goal of this literature review. It provides a comprehensive and updated state-of-the-art that satisfies all stated criteria. Out of 591 papers retrieved from 6 reputable databases, 73 papers were eligible for analysis. This review addresses relevant research questions regarding techniques & method types, data details, tools, code syntax, semantics, structural and domain information. Motivation to conduct this comprehensive review is to equip the readers with the necessary information and keep them informed about the software defect prediction domain.

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Application of Random Vector Functional Link Network for Software Defect Prediction

Cited by 2 publications

References 21 publications

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Software Defect Prediction Approaches Revisited

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