Yazar "Aghaei, Babak" seçeneğine göre listele

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    Constraint-based heuristic algorithms for software test generation
    (Elsevier, 2024) Arasteh, Bahman; Aghaei, Babak; Ghanbarzadeh, Reza; Kalan, Reza
    While software testing is essential for enhancing a software system's quality, it can be time-consuming and costly during developing software. Automation of software testing can help solve this problem, streamlining time-consuming testing tasks. However, generating automated test data that maximally covers program branches is a complex optimization problem referred to as NP-complete and should be addressed appropriately. Although a variety of heuristic algorithms have already been suggested to create test suites with the greatest coverage, they have issues such as insufficient branch coverage, low rate of success in generating test data with high coverage, and unstable results. The main objective of the current chapter is to investigate and compare the coverage, success rate (SR), and stability of various heuristic algorithms in software structural test generation. To achieve this, the effectiveness of seven algorithms, genetic algorithm (GA), simulated annealing (SA), ant colony optimizer (ACO), particle swarm optimizer (PSO), artificial bee colony (ABC), shuffle frog leaping algorithm (SFLA), and imperialist competitive algorithm (ICA), are examined in automatically generating test data, and their performance is compared on the basis of various criteria. The experiment results demonstrate the superiority of the SFLA, ABC, and ICA to other examined algorithms. Overall, SFLA outperforms all other algorithms in coverage, SR, and stability. © 2024 Elsevier Inc. All rights reserved.
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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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    Single and multi-objective metaheuristic algorithms and their applications in software maintenance
    (Elsevier, 2024) Arasteh, Bahman; Sadegi, Razieh; Aghaei, Babak; Ghanbarzadeh, Reza
    The comprehension of software structure plays a significant role in efficiently maintaining software. Clustering software modules has been regarded as a successful method for extracting understandable structural models from source code, among other reverse engineering techniques. However, the problem of achieving the optimal model for clustering is considered NP-complete. The primary objective of Software Module Clustering (SMC) is minimizing inter-cluster connections, maximizing intra-cluster connections, and improving clustering quality. Different optimization algorithms (Seyyedabbasi, 2023) have been used to sort out the optimization problems. The majority of proposed methods to address SMC problem have shown some drawbacks, such as a lower rate of success, stability, and quality of modularization. This chapter reviews and compares seven heuristic algorithms that can be employed to solve software module clustering, namely PSO, GA, PSO-GA, COA, GWO, SCSO and OOA, in terms of achieving optimal clustering of software modules. Through experiments conducted on 10 real-world standard applications, the findings demonstrate that OOA, GWO, and SCSO perform better than the other methods in handling SMC. Notably, when the initial population of these methods is generated by the use of the logistic chaos technique instead of the random technique, these algorithms perform much better compared to others. The average quality of the modularity of the clusters created by OOA, GWO, and SCSO for the selected benchmark set is 3.937, 3.120, 3.107, respectively. The findings present an exploration of heuristic algorithms for optimal SMC; therefore, the positive impact of chaos theory is highlighted. OOA, GWO, SCSO, and COA demonstrate promising results, indicating their potential for practical application in the field of software maintenance and comprehension. By addressing the drawbacks of the evaluated methods, this chapter contributes to the advancement of software clustering techniques and facilitates the creation of more maintainable and efficient software systems.