Software Defect Prediction Using Principal Component Analysis and Naïve Bayes Algorithm

Dhamayanthi, N.; Lavanya, B.

doi:10.1007/978-981-13-6459-4_24

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…K-nearest neighbor, support vector machine, and naive bayes classifiers, as well as the firefly algorithm, were utilized in [34] to categorize the features that were chosen. The authors of [35] suggested a framework that builds the prediction model using the Naive Bayes classification method and PCA for dimensionality reduction. The authors of [36] suggested a hybrid preprocessing strategy in which feature selection is followed by iterative partitioning filtering.…”

Section: Feature Selectionmentioning

confidence: 99%

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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Section: Feature Selectionmentioning

confidence: 99%

Software Defect Prediction Approaches Revisited

Shebl,

Afify,

Badr

2023

IJICIS

View full text Add to dashboard Cite

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“…Dhamayanthi et al [61] made use of the well-known statistical tool Principle Component Analysis as a feature reduction technique for solving the SFP problem. Using the extracted features by PCA, and the NB classifier was applied over seven projects from NASA Metrics Data Program.…”

Section: Review Of Related Work a Software Fault Predictionmentioning

confidence: 99%

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

et al. 2021

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Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.

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