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

Abstract

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).

Enhancing data security using global mutation based novel artificial immune network optimization in public cloud storage system

“…Also, numerous benchmark functions namely Sphere, Himmelblau, Vincent, Grienwank, Discus, Six hump camel's back and Rastrigin (F 1 (H) to F 7 (H)) consisting of variable values, global optimal range, dimensional ranges, as well as local optimal value as mentioned in Table 3. The proposed GM‐NAINO algorithm is evaluated with various other optimization algorithms such as quasi opposition based modified levy flight distribution (QMLFD), artificial immune network optimization (AINO) algorithm, 48 black widow optimization (BWO) algorithm, 49 and cuckoo search (CS) optimization algorithm 50 as represented in Table 4. Here we utilized three different metrics namely the SD, best value and the average value are determined in obtaining the best value.…”

A Hadoop Based Framework Integrating Machine Learning Classifiers for Anomaly Detection in the Internet of Things

“…The proposed model has an overall 19% improvement in accuracy in comparison with the state of the art techniques. Figure 9 signifies the performance comparison of different machine learning approaches on the edge layer of ToN-IoT dataset with the proposed approach [57,[60][61][62][63][64]. The proposed model integrates modified Tomek link under-sampling and hyper parameter tuning using AiNet approach for the supervised machine learning classifiers which enhances the overall performance on both the datasets.…”

“…An optimization version (Opt-aiNet) which can be used in optimization problems is available in aiNet [60]. A population is grown in Opt-aiNet which consist of a network of antibodies and the population size can be dynamically adjustable.…”

“…Adaboost [61] Random Forest [62] SVM[RBF] [64] Multi Layer Perceptron [64] HAST-IDS [63] LSTM [64] CNN [64] LuNet [57] Densely-ResNet [60] Proposed Approach Figure 9. Comparison of the proposed scheme with existing approaches on ToN-IoT dataset.…”

GM-NAINO: Global Mutation based Novel Artificial Immune Network Optimization for Enhancing Data Security in Public Cloud Storage System

“…In addition to this, numerous test functions namely Sphere, Himmelblau, Vincent, Grienwank, Discus, Six hump camels back and Rastrigin (F1(H) to F7(H)) consisting of variable values, global optimal value, dimensional ranges as well as local optimal value as mentioned in table 3. The proposed GM-NAINO algorithm is evaluated with various other optimization algorithms such as quasi opposition based Modified Levy Flight Distribution (QMLFD), Artificial Immune Network Optimization (AINO) algorithm [28], Black Widow Optimization (BWO) algorithm [29] and Cuckoo search (CS) optimization algorithm [30] as represented in table 4. Here we utilized three different metrics namely the standard deviation, best value and the average value are determined in obtaining the best value.…”