Browsing by Author "Atoyebi, Jelili Olaniyi"

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    Parameter tuning in KNN for software defect prediction: an empirical analysis
    (Department of Computer Engineering, Universitas Diponegoro, Indonesia., 2019-10-31) Mabayoje, Modinat Abolore; Balogun, Abdullateef Oluwagbemiga; Jibril, Hajarah Afor; Atoyebi, Jelili Olaniyi; Mojeed, Hammed Adeleye; Adeyemo, Victor Elijah
    Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.
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    Wrapper Feature Selection based Heterogeneous Classifiers for Software Defect Prediction
    (Adeleke University, Ede., 2019-02) Mabayoje, Modinat Abolore; Balogun, Abdullateef Oluwagbemiga; Bello, Shade Musllimat; Atoyebi, Jelili Olaniyi; Mojeed, Hammed Adeleye; Ekundayo, Ayobami Halimat
    The performance of Software Defect Prediction (SDP) models depends on the quality of dataset used for training the models. The high dimensionality of software metric features has been noted as a data quality problem which affects the performance of SDP models. This makes it crucial to apply feature selection (FS) to SDP since FS can remove irrelevant and redundant software metric features. In this study, the effect of wrapper-based FS methods on classification techniques in SDP was investigated. The wrapper FS methods were based on different search methods; Best First Search (BFS), Genetic Search (GS), Greedy Stepwise Search (GSS) and Multi-Objective Evolutionary Search (MOES) so as to investigate their respective effect on classifiers in SDP. Five (5) publicly available software defect datasets were used. These datasets were classified by the individual classifiers which were carefully selected based on their characteristics hence the heterogeneity. Naïve Bayes (NB) was selected from Bayes category Classifier, K-Nearest Neighbor (KNN) was selected from Instance-Based Learner category and (J48) Decision Tree from Trees Function classifier. The experimental results clearly showed that the application of wrapper FS method to datasets before classification in SDP is better and should be encouraged as NB with GS based Wrapper Method had the best accuracy performance. It can be concluded that FS methods are capable of improving the performance of predictive models in SDP.