Yazar "Arasteh, Keyvan" seçeneğine göre listele

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    A software defect prediction method using binary gray wolf optimizer and machine learning algorithms
    (Pergamon-Elsevier Science, 2024) Wang, Hao; Arasteh, Bahman; Arasteh, Keyvan; Gharehchopogh, Farhad Soleimanian; Rouhi, Alireza
    Context: Software defect prediction means finding defect-prone modules before the testing process which will reduce testing cost and time. Machine learning methods can provide valuable models for developers to classify software faulty modules. Problem: The inherent problem of the classification is the large volume of the training dataset's features, which reduces the accuracy and precision of the classification results. The selection of the effective features of the training dataset for classification is an NP-hard problem that can be solved using heuristic algorithms. Method: In this study, a binary version of the Gray Wolf optimizer (bGWO) was developed to select the most effective features of the training dataset. By selecting the most influential features in the classification, the precision and accuracy of the software module classifiers can be increased. Contribution: Developing a binary version of the gray wolf optimization algorithm to optimally select the effective features and creating an effective defect predictor are the main contributions of this study. To evaluate the effectiveness of the proposed method, five real-world and standard datasets have been used for the training and testing stages of the classifier. Results: The results indicate that among the 21 features of the train datasets, the basic complexity, sum of operators and operands, lines of codes, number of lines containing code and comments, and sum of operands have the greatest effect in predicting software defects. In this research, by combining the bGWO method and machine learning algorithms, accuracy, precision, recall, and F1 criteria have been considerably increased.
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    A bioinspired discrete heuristic algorithm to generate the effective structural model of a program source code
    (Elsevier, 2023) Arasteh, Bahman; Sadegi, Razieh; Arasteh, Keyvan; Gunes, Peri; Kiani, Farzad; Torkamanian-Afshar, Mahsa
    When the source code of a software is the only product available, program understanding has a substantial influence on software maintenance costs. The main goal in code comprehension is to extract information that is used in the software maintenance stage. Generating the structural model from the source code helps to alleviate the software maintenance cost. Software module clustering is thought to be a viable reverse engineering approach for building structural design models from source code. Finding the optimal clustering model is an NP-complete problem. The primary goals of this study are to minimize the number of connections between created clusters, enhance internal connections inside clusters, and enhance clustering quality. The previous approaches' main flaws were their poor success rates, instability, and inadequate modularization quality. The Olympiad optimization algorithm was introduced in this paper as a novel population-based and discrete heuristic algorithm for solving the software module clustering problem. This algorithm was inspired by the competition of a group of students to increase their knowledge and prepare for an Olympiad exam. The suggested algorithm employs a divide-and-conquer strategy, as well as local and global search methodologies. The effectiveness of the suggested Olympiad algorithm to solve the module clustering problem was evaluated using ten real-world and standard software benchmarks. According to the experimental results, on average, the modularization quality of the generated clustered models for the ten benchmarks is about 3.94 with 0.067 standard deviations. The proposed algorithm is superior to the prior algorithms in terms of modularization quality, convergence, and stability of results. Furthermore, the results of the experiments indicate that the proposed algorithm can be used to solve other discrete optimization problems efficiently. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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    A Bioinspired Test Generation Method Using Discretized and Modified Bat Optimization Algorithm
    (Mdpi, 2024) Arasteh, Bahman; Arasteh, Keyvan; Kiani, Farzad; Sefati, Seyed Salar; Fratu, Octavian; Halunga, Simona; Tirkolaee, Erfan Babaee
    The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program's branch coverage. The proposed method was implemented along with the previous one. The experiments' results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.
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    Detecting SQL injection attacks by binary gray wolf optimizer and machine learning algorithms
    (Springer London Ltd, 2024) Arasteh, Bahman; Aghaei, Babak; Farzad, Behnoud; Arasteh, Keyvan; Kiani, Farzad; Torkamanian-Afshar, Mahsa
    SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the application's database. SQL injection attacks can be detected using machine learning algorithms. The effective features should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the training dataset's smallest and most effective features. The main objective of this study is to enhance the accuracy, precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage, two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy, 99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by selecting 20% of the most effective features.
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    A quality-of-service aware composition-method for cloud service using discretized ant lion optimization algorithm
    (Springer London Ltd, 2024) Arasteh, Bahman; Aghaei, Babak; Bouyer, Asgarali; Arasteh, Keyvan
    In the cloud system, service providers supply a pool of resources in the form of a web service and the services are merged to provide the required composite services. Composing a quality-of-service aware web service is like the knapsack problem and this problem is NP-hard. Different artificial intelligence and heuristic methods have been used to achieve optimal or near-optimal composite services. In this paper, the Ant Lion optimization algorithm was modified and discretized to choose the appropriate web services from the existing services and to provide the optimal composite services. The QWS dataset contains a collection of 2507 real-world web services which are used to evaluate the proposed method. In this study, response time parameters, availability, throughput, success capability, reliability, and latency were used as the web service quality metrics. The results of the conducted experiments confirm that the provided composite service by the proposed method has considerably higher quality than the other related algorithms. Hence, the proposed method can be used in the cloud resource discovery layer.
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    A source-code aware method for software mutation testing using artificial bee colony algorithm
    (SPRINGER, 2022) Arasteh, Bahman; Imanzadeh, Parisa; Arasteh, Keyvan; Gharehchopogh, Farhad Soleimanian; Zarei, Bagher
    The effectiveness of software test data relates to the number of found faults by the test data. Software mutation test is used to evaluate the effectiveness of the software test methods and is one of the challenging fields of software engineering. In order to evaluate the capability of test data in finding the program faults, some syntactical changes are made in the program source code to cause faulty program; then, the generated mutants (faulty programs) and original program are executing with the corresponding test data. One of the main drawbacks of mutation testing is its computational cost. Indeed, high execution time of mutation testing is a challenging research problem. Reducing the time and cost of mutation test is the main objective of this paper. In the traditional mutation methods and tools the mutants are injected randomly in each instructions of a program. Meanwhile, in the real-world program, the probability of fault occurrences in the simple locations (instructions and data) of a program is negligible. With respect to the 80-20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. In the first stage of the proposed method, Artificial Bee Colony optimization algorithm is used to identifying the most fault prone paths of a program; in the next stage, the mutation operators (faults) are injected only on the identified fault-prone instructions and data. Regarding the results of conducted experiments on the standard benchmark programs, Compared to existing methods, the proposed method reduces 28.10% of the generated mutants. Reducing the number of generated mutants will reduce the cost of mutation testing. The traditional mutation testing tools (Mujava, Muclipse, Jester, Jumble) can perform the mutation testing with a lower cost using the method presented in this study.