2020
DOI: 10.1002/spy2.103
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Proposed software faults detection using hybrid approach

Abstract: The major challenge is to validate software failure dataset by finding unknown model parameters used. For software assurance, previously many attempts were made based using classical classifiers as decision tree, Naïve Bayes, and k-nearest neighbor for software fault prediction. But the accuracy of fault prediction is very low as defect prone modules are very small as compared to defect-free modules.So, for solving modules fault classification problems and enhancing reliability accuracy, a hybrid algorithm pro… Show more

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Cited by 7 publications
(5 citation statements)
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“…Naive Bayes adalah teknik klasifikasi yang didasarkan pada Teorema Bayes dan asumsi independensi bersyarat antara fitur-fitur yang diberikan variabel kelas. Metode ini banyak digunakan dalam memprediksi cacat perangkat lunak karena keefektifannya dalam mengklasifikasikan kode statis [2]. Metode ini bekerja dengan menghitung probabilitas sebuah titik data termasuk dalam kelas tertentu berdasarkan probabilitas fitur-fiturnya.…”
Section: Pendahuluanunclassified
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“…Naive Bayes adalah teknik klasifikasi yang didasarkan pada Teorema Bayes dan asumsi independensi bersyarat antara fitur-fitur yang diberikan variabel kelas. Metode ini banyak digunakan dalam memprediksi cacat perangkat lunak karena keefektifannya dalam mengklasifikasikan kode statis [2]. Metode ini bekerja dengan menghitung probabilitas sebuah titik data termasuk dalam kelas tertentu berdasarkan probabilitas fitur-fiturnya.…”
Section: Pendahuluanunclassified
“…Dalam bidang manajemen risiko, penggunaan Bayesian Networks telah diusulkan untuk mendukung pengambilan keputusan dalam berbagai kegiatan desain perangkat lunak, berkontribusi pada pengelolaan risiko teknologi yang efektif dalam proyek software. [2]. Ini menunjukkan pentingnya memanfaatkan teknik dan metodologi canggih untuk mengatasi risiko dalam pengembangan dan pemeliharaan.…”
Section: Pendahuluanunclassified
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“…Moreover, the study 24 explored an empirical research on a dataset using the recommended hybrid method and the outcomes exhibited the enhanced performance of the proposed system when compared to the traditional methods. Similarly, the article 25 exhibited a classification outline that utilizes multi filter‐feature selection (MF‐FS) method and multi‐layer perceptron (MLP) for SFP.…”
Section: Review Of Existing Workmentioning
confidence: 99%
“…Results of AUC confirmed that the proposed FS approaches yielded significant enhancement in prediction performance for most of the applied classifiers. A hybrid feature selection technique hybridizing PSO and MGA was introduced by Banga et al [64] for improving SFP. Furthermore, bagging was also integrated with this approach for resolving the class imbalance problem.…”
Section: Review Of Related Work a Software Fault Predictionmentioning
confidence: 99%