Hyperparameter optimization to improve bug prediction accuracy

Osman, Haidar; Ghafari, Mohammad; Nierstrasz, Oscar

doi:10.1109/maltesque.2017.7882014

Cited by 37 publications

(20 citation statements)

References 22 publications

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“…Their results showed that automated parameter optimization increases the accuracy by up to 40 percentage points. Osman et al [3] investigated the optimization of KNN and SVM using a grid search. The results showed that prediction accuracy was improved by up to 20% in KNN and by up to 10% in SVM.…”

Section: Related Workmentioning

confidence: 99%

“…Hyperparameter optimization is basically the process of tuning the hyper-parameters of machine learning models or the process of finding the best hyper-parameters value; it is also known as model-selection or hyper-parameter tuning. Different classifiers have different features according to which the hyperparameters need to be optimized [2], [3]. Over the past years, various techniques have been proposed by researchers to examine the performance of machine learning classifiers in software bug prediction [2], [12].…”

Section: Introductionmentioning

confidence: 99%

“…The prime motivation behind our proposed algorithm for optimization is from Aydin et al [13], Tantithamthavorn et al [2], [12] and Osman et al [3]. Aydin et al [13] applied clonal selection (AIS based technique) to optimize SVM hyper-parameters and reported improvement in the accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Tantithamthavorn et al [2], [12] investigated automated parameter optimization on 20 classification algorithms using grid search, random search, GA, and DE and Caret automated parameter optimization technique for defect prediction models on 18 datasets and reported improved results. Osman et al [3] use hyper-parameter optimization on KNN and SVM and achieved an increase in Accuracy. Moreover, it is also stated that when a Genetic algorithm (GA), Random search (RS), Grid search (GS) and Differential Evolution (DE) are used for hyper-parameter optimization problems it increases computational costs and caused over-fitting.…”

Section: Introductionmentioning

confidence: 99%

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Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

et al. 2020

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Software testing is an important task in software development activities, and it requires most of the resources, namely, time, cost and effort. To minimize this fatigue, software bug prediction (SBP) models are applied to improve the software quality assurance (SQA) processes by predicting buggy components. The bug prediction models use machine learning classifiers so that bugs can be predicted in software components in some software metrics. These classifiers are characterized by some configurable parameters, called hyperparameters that need to be optimized to ensure better performance. Many methods have been proposed by researchers to predict the defective components but these classifiers sometimes not perform well when default settings are used for machine learning classifiers. In this paper, software bug prediction model is proposed which uses machine learning classifiers in conjunction with the Artificial Immune Network (AIN) to improve bug prediction accuracy through its hyper-parameter optimization. For this purpose, seven machine learning classifiers, such as support vector machine Radial base function (SVM-RBF), K-nearest neighbor (KNN) (Minkowski metric), KNN (Euclidean metric), Naive Bayes (NB), Decision Tree (DT), Linear discriminate analysis (LDA), Random forest (RF) and adaptive boosting (AdaBoost), were used. The experiment was carried out on bug prediction dataset. The results showed that hyper-parameter optimization of machine learning classifiers, using AIN and its applications for software bug prediction, performed better than when classifiers with their default hyper-parameters were used. INDEX TERMS Artificial immune network (AIN), artificial immune system (AIS), hyper-parameter optimization, optimized artificial immune network (opt-aiNet), software bug prediction (SBP).

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyper-Parameter Optimization of Classifiers, Using an Artificial Immune Network and Its Application to Software Bug Prediction

et al. 2020

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“…Other works focus on combined selection of both features and hyperparameters (Maali and Al-Jumaily, 2012;Yao et al, 2009;Sunkad, 2016). Osman et al (2017) showed empirically that optimized hyperparameters significantly improved performance for k-nearest neighbours algorithm while prediction accuracy of support vector machines either improved or was at least retained. k-Means (MacQueen, 1967) is one of the most popular and widely known techniques, used as standalone technique or in combination with others.…”

Section: Introductionmentioning

confidence: 99%

SVM and k-Means Hybrid Method for Textual Data Sentiment Analysis

Korovkinas¹,

Danėnas²,

Garšva³

2019

BJMC

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The goal of this paper is to propose a hybrid technique to improve Support Vector Machines classification accuracy using training data sampling and hyperparameter tuning. The proposed technique applies clustering to select training data and parameter tuning to optimize classifier effectiveness. The paper reports that better results were obtained using our proposed method in all experiments, compared to results of method presented in our previous work.

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