2,144 research outputs found

    HYBRYDOWY, BINARNY ALGORYTM WOA OPARTY NA TRANSMITANCJI STOŻKOWEJ DO PROGNOZOWANIA DEFEKTÓW OPROGRAMOWANIA

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    Reliability is one of the key factors used to gauge software quality. Software defect prediction (SDP) is one of the most important factors which affects measuring software's reliability. Additionally, the high dimensionality of the features has a direct effect on the accuracy of SDP models. The objective of this paper is to propose a hybrid binary whale optimization algorithm (BWOA) based on taper-shape transfer functions for solving feature selection problems and dimension reduction with a KNN classifier as a new software defect prediction method. In this paper, the values of a real vector that represents the individual encoding have been converted to binary vector by using the four types of Taper-shaped transfer functions to enhance the performance of BWOA to reduce the dimension of the search space. The performance of the suggested method (T-BWOA-KNN) was evaluated using eleven standard software defect prediction datasets from the PROMISE and NASA repositories depending on the K-Nearest Neighbor (KNN) classifier. Seven evaluation metrics have been used to assess the effectiveness of the suggested method. The experimental results have shown that the performance of T-BWOA-KNN produced promising results compared to other methods including ten methods from the literature, four types of T-BWOA with the KNN classifier. In addition, the obtained results are compared and analyzed with other methods from the literature in terms of the average number of selected features (SF) and accuracy rate (ACC) using the Kendall W test. In this paper, a new hybrid software defect prediction method called T-BWOA-KNN has been proposed which is concerned with the feature selection problem. The experimental results have proved that T-BWOA-KNN produced promising performance compared with other methods for most datasets.Niezawodność jest jednym z kluczowych czynników stosowanych do oceny jakości oprogramowania. Przewidywanie defektów oprogramowania SDP (ang. Software Defect Prediction) jest jednym z najważniejszych czynników wpływających na pomiar niezawodności oprogramowania. Dodatkowo, wysoka wymiarowość cech ma bezpośredni wpływ na dokładność modeli SDP. Celem artykułu jest zaproponowanie hybrydowego algorytmu optymalizacji BWOA (ang. Binary Whale Optimization Algorithm) w oparciu o transmitancję stożkową do rozwiązywania problemów selekcji cech i redukcji wymiarów za pomocą klasyfikatora KNN jako nowej metody przewidywania defektów oprogramowania. W artykule, wartości wektora rzeczywistego, reprezentującego indywidualne kodowanie zostały przekonwertowane na wektor binarny przy użyciu czterech typów funkcji transferu w kształcie stożka w celu zwiększenia wydajności BWOA i zmniejszenia wymiaru przestrzeni poszukiwań. Wydajność sugerowanej metody (T-BWOA-KNN) oceniano przy użyciu jedenastu standardowych zestawów danych do przewidywania defektów oprogramowania z repozytoriów PROMISE i NASA w zależności od klasyfikatora KNN. Do oceny skuteczności sugerowanej metody wykorzystano siedem wskaźników ewaluacyjnych. Wyniki eksperymentów wykazały, że działanie rozwiązania T-BWOA-KNN pozwoliło uzyskać obiecujące wyniki w porównaniu z innymi metodami, w tym dziesięcioma metodami na podstawie literatury, czterema typami T-BWOA z klasyfikatorem KNN. Dodatkowo, otrzymane wyniki zostały porównane i przeanalizowane innymi metodami z literatury pod kątem średniej liczby wybranych cech (SF) i współczynnika dokładności (ACC), z wykorzystaniem testu W. Kendalla. W pracy, zaproponowano nową hybrydową metodę przewidywania defektów oprogramowania, nazwaną T-BWOA-KNN, która dotyczy problemu wyboru cech. Wyniki eksperymentów wykazały, że w przypadku większości zbiorów danych T-BWOA-KNN uzyskała obiecującą wydajność w porównaniu z innymi metodami

    Software Fault Prediction using Bio-Inspired Algorithms to Select the Features to be employed: An Empirical Study

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    In recent past, the use of bio-inspired algorithms got a significant attention in software fault predictions, where they can be used to select the most relevant features for a dataset aiming to increase the prediction accuracy of estimation techniques. The most-earlier and widely investigated algorithms are Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). More recently, researchers have analyzed other algorithms inspired from nature. In this paper, we consider GA and PSO as baseline/benchmark algorithms and evaluate their performances against seven recently-employed bio-inspired algorithms and metaheuristics, namely Ant Colony Optimization, Bat Search, Bee Search, Cuckoo Search, Harmony Search, Multi-Objective Evolutionary Algorithm, and Tabu Search, for feature selection in software fault prediction. We present experiments with seven open source datasets and three estimation techniques: Random Forest, Support Vector Regression, and Linear Regression. We found that it is not always true that the recently introduced algorithms outperform the earlier introduced algorithms

    Integrated bio-search approaches with multi-objective algorithms for optimization and classification problem

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    Optimal selection of features is very difficult and crucial to achieve, particularly for the task of classification. It is due to the traditional method of selecting features that function independently and generated the collection of irrelevant features, which therefore affects the quality of the accuracy of the classification. The goal of this paper is to leverage the potential of bio-inspired search algorithms, together with wrapper, in optimizing multi-objective algorithms, namely ENORA and NSGA-II to generate an optimal set of features. The main steps are to idealize the combination of ENORA and NSGA-II with suitable bio-search algorithms where multiple subset generation has been implemented. The next step is to validate the optimum feature set by conducting a subset evaluation. Eight (8) comparison datasets of various sizes have been deliberately selected to be checked. Results shown that the ideal combination of multi-objective algorithms, namely ENORA and NSGA-II, with the selected bio-inspired search algorithm is promising to achieve a better optimal solution (i.e. a best features with higher classification accuracy) for the selected datasets. This discovery implies that the ability of bio-inspired wrapper/filtered system algorithms will boost the efficiency of ENORA and NSGA-II for the task of selecting and classifying features

    complexFuzzy: A novel clustering method for selecting training instances of cross-project defect prediction

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    Over the last decade, researchers have investigated to what extent cross-project defect prediction (CPDP) shows advantages over traditional defect prediction settings. These works do not take training and testing data of defect prediction from the same project. Instead, dissimilar projects are employed. Selecting proper training data plays an important role in terms of the success of CPDP. In this study, a novel clustering method named complexFuzzy is presented for selecting training data of CPDP. The method is developed by determining membership values with the help of some metrics which can be considered as indicators of complexity. First, CPDP combinations are created on 29 different data sets. Subsequently, complexFuzzy is evaluated by considering cluster centers of data sets and comparing some performance measures including area under the curve (AUC) and F-measure. The method is superior to other five comparison algorithms in terms of the distance of cluster centers and prediction performance

    A review paper: optimal test cases for regression testing using artificial intelligent techniques

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    The goal of the testing process is to find errors and defects in the software being developed so that they can be fixed and corrected before they are delivered to the customer. Regression testing is an essential quality testing technique during the maintenance phase of the program as it is performed to ensure the integrity of the program after modifications have been made. With the development of the software, the test suite becomes too large to be fully implemented within the given test cost in terms of budget and time. Therefore, the cost of regression testing using different techniques should be reduced, here we dealt many methods such as retest all technique, regression test selection technique (RTS) and test case prioritization technique (TCP). The efficiency of these techniques is evaluated through the use of many metrics such as average percentage of fault detected (APFD), average percentage block coverage (APBC) and average percentage decision coverage (APDC). In this paper we dealt with these different techniques used in test case selection and test case prioritization and the metrics used to evaluate their efficiency by using different techniques of artificial intelligent and describe the best of all

    Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

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    The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

    Software Defect Prediction Based on Optimized Machine Learning Models: A Comparative Study

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    Software defect prediction is crucial used for detecting possible defects in software before they manifest. While machine learning models have become more prevalent in software defect prediction, their effectiveness may vary based on the dataset and hyperparameters of the model. Difficulties arise in determining the most suitable hyperparameters for the model, as well as identifying the prominent features that serve as input to the classifier. This research aims to evaluate various traditional machine learning models that are optimized for software defect prediction on NASA MDP (Metrics Data Program) datasets. The datasets were classified using k-nearest neighbors (k-NN), decision trees, logistic regression, linear discriminant analysis (LDA), single hidden layer multilayer perceptron (SHL-MLP), and Support Vector Machine (SVM). The hyperparameters of the models were fine-tuned using random search, and the feature dimensionality was decreased by utilizing principal component analysis (PCA). The synthetic minority oversampling technique (SMOTE) was implemented to oversample the minority class in order to correct the class imbalance. k-NN was found to be the most suitable for software defect prediction on several datasets, while SHL-MLP and SVM were also effective on certain datasets. It is noteworthy that logistic regression and LDA did not perform as well as the other models. Moreover, the optimized models outperform the baseline models in terms of classification accuracy. The choice of model for software defect prediction should be based on the specific characteristics of the dataset. Furthermore, hyperparameter tuning can improve the accuracy of machine learning models in predicting software defects

    Texture and Colour in Image Analysis

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    Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews
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