Genetic Feature Selection for Software Defect Prediction

Wahono, Romi Satria; Suryana, Nanna

doi:10.1166/asl.2014.5283

Cited by 46 publications

(20 citation statements)

References 23 publications

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“…AUC chosen because of clear statistical interpretation. Measurement of probability in fault-prone class given a higher rating compared non-fault-prone class [33]. In addition AUC has the potential to significantly increase convergence throughout empirical experiments in the prediction of software defects [34], because it separates predictive performance from operating conditions and represents the general size of the prediction.…”

Section: Resultsmentioning

confidence: 99%

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Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

Suntoro

Christanto

Indriyawati

2018

IJITEB

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The most important part in software engineering is a software defect prediction. Software defect prediction is defined as a software prediction process from errors, failures, and system errors. Machine learning methods are used by researchers to predict software defects including estimation, association, classification, clustering, and datasets analysis. Datasets of NASA Metrics Data Program (NASA MDP) is one of the metric software that researchers use to predict software defects. NASA MDP datasets contain unbalanced classes and high dimensional data, so they will affect the classification evaluation results to be low. In this research, data with unbalanced classes will be solved by the AdaCost method and high dimensional data will be handled with the Average Weight Information Gain (AWEIG) method, while the classification method that will be used is the Naïve Bayes algorithm. The proposed method is named AWEIG + AdaCost Bayesian. In this experiment, the AWEIG + AdaCost Bayesian algorithm is compared to the Naïve Bayesian algorithm. The results showed the mean of Area Under the Curve (AUC) algorithm AWEIG + AdaCost Bayesian yields better than just a Naïve Bayes algorithm with respectively mean of AUC values are 0.752 and 0.696.

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Section: Resultsmentioning

confidence: 99%

“…Weight Information Gain (WIG) is a method of weighting each of the most common variables of evaluation attributes [33]. To calculate information gain, first step are understand another rule called entropy.…”

Section: Weight Information Gain Algorithmmentioning

confidence: 99%

Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

Suntoro

Christanto

Indriyawati

2018

IJITEB

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“…Wahono and Herman [44] introduced a novel software defect predictor that depended on integration of a the genetic algorithm and the bagging technique. GA was utilized to choose the optimal and pertinent feature subset.…”

Section: Software Defect Prediction Depending On Ensemble Modelsmentioning

confidence: 99%

Software Defect Prediction Based on Feature Subset Selection and Ensemble Classification

Saifan¹,

Abu-wardih²

2020

ECTI-CIT

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Two primary issues have emerged in the machine learning and data mining community: how to deal with imbalanced data and how to choose appropriate features. These are of particular concern in the software engineering domain, and more specifically the field of software defect prediction. This research highlights a procedure which includes a feature selection technique to single out relevant attributes, and an ensemble technique to handle the class-imbalance issue. In order to determine the advantages of feature selection and ensemble methods we look at two potential scenarios: (1) Ensemble models constructed from the original datasets, without feature selection; (2) Ensemble models constructed from the reduced datasets after feature selection has been applied. Four feature selection techniques are employed: Principal Component Analysis (PCA), Pearson’s correlation, Greedy Stepwise Forward selection, and Information Gain (IG). The aim of this research is to assess the effectiveness of feature selection techniques using ensemble techniques. Five datasets, obtained from the PROMISE software depository, are analyzed; tentative results indicate that ensemble methods can improve the model's performance without the use of feature selection techniques. PCA feature selection and bagging based on K-NN perform better than both bagging based on SVM and boosting based on K-NN and SVM, and feature selection techniques including Pearson’s correlation, Greedy stepwise, and IG weaken the ensemble models’ performance.

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“…TSC adalah salah satu algoritma pengelompokan yang dikembangkan pertama kali oleh (Chiu, Fang, Chen, Wang, & Jeris, 2001) dan dirancang untuk menangani kumpulan data yang sangat besar. TSC mampu menangani variabel kontinu dan variabel kategorikal (Chiu et al, 2001), (Satish & Bharadhwaj, 2010 Dalam penelitian ini, metode yang diusulkan dievaluasi dengan menggunakan efektivitas classifier berdasarkan confusion matrix dengan evaluasi utama adalah area under the curve (AUC) seperti yang digunakan oleh (Laradji, Alshayeb, & Ghouti, 2015), (Rana, Mian, & Shamail, 2015), (Czibula, Marian, & Czibula, 2014), (Wahono & Herman, 2014), (Arar & Ayan, 2015) AUC memiliki potensi untuk secara signifikan meningkatkan konvergensi di seluruh eksperimen empiris dalam prediksi cacat perangkat lunak (Laradji et al, 2015) dan penggunaan AUC untuk meningkatkan perbandingan studi silang (Lessmann, Baesens, Mues, & Pietsch, 2008). Panduan dasar untuk mengklasifikasikan keakuratan tes diagnostik berdasarkan AUC sebagaimana dinyatakan oleh (Gorunescu, 2011) sebagai berikut:…”

Section: Metode Penelitianunclassified

“…Evaluasi lain dari metode yang diusulkan yaitu: recall atau sensitivity (SN) seperti yang digunakan oleh (Laradji et al, 2015), (Czibula et al, 2014), (Wahono & Herman, 2014); specipies (SP) dan precision (PR) seperti yang digunakan oleh (Czibula et al, 2014), (Wahono & Herman, 2014).…”

Section: Metode Penelitianunclassified

Mengatasi Imbalanced Class Pada Software Defect Prediction Menggunakan Two-Step Clustering-Based Undersampling dan Bagging Tehcnique

Akbar¹,

Kurniawan²,

Fauzi³

2019

JI. Jurnal. Informatika.

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Ketidakseimbangan kelas seringkali menjadi masalah di berbagai set data dunia nyata, di mana satu kelas (yaitu kelas minoritas) berisi sejumlah kecil titik data dan yang lainnya (yaitu kelas mayoritas) berisi sejumlah besar titik data. Sangat sulit untuk mengembangkan model yang efektif dengan menggunakan data mining dan algoritma machine learning tanpa mempertimbangkan preprocessing data untuk menyeimbangkan set data yang tidak seimbang. Random undersampling dan oversampling telah digunakan dalam banyak penelitian untuk memastikan bahwa kelas yang berbeda mengandung jumlah titik data yang sama. Dalam penelitian ini, kami mengusulkan kombinasi two-step clustering-based random undersampling dan bagging technique untuk meningkatkan nilai akurasi software defect prediction. Metode yang diusulkan dievaluasi menggunakan lima set data dari repositori program data metrik NASA dan area under the curve (AUC) sebagai evaluasi utama. Hasil telah menunjukkan bahwa metode yang diusulkan menghasilkan kinerja yang sangat baik untuk semua dataset (AUC> 0,9). Dalam hal SN, percobaan kedua mengungguli percobaan pertama di hampir semua dataset (3 dari 5 dataset). Sementara itu, dalam hal SP, percobaan pertama tidak mengungguli percobaan kedua di semua dataset. Secara keseluruhan percobaan kedua mengungguli dan lebih baik daripada percobaan pertama karena evaluasi utama dalam klasifikasi kelas yang tidak seimbang seperti SDP adalah AUC Oleh karena itu, dapat disimpulkan bahwa metode yang diusulkan menghasilkan kinerja yang optimal baik untuk set data skala kecil maupun besar.

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Genetic Feature Selection for Software Defect Prediction

Cited by 46 publications

References 23 publications

Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

Software Defect Prediction Using AWEIG+ADACOST Bayesian Algorithm for Handling High Dimensional Data and Class Imbalance Problem

Software Defect Prediction Based on Feature Subset Selection and Ensemble Classification

Mengatasi Imbalanced Class Pada Software Defect Prediction Menggunakan Two-Step Clustering-Based Undersampling dan Bagging Tehcnique

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