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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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    DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM
    (Univ Nis, 2023) Arasteh, Bahman; Bouyer, Asgarali; Ghanbarzadeh, Reza; Rouhi, Alireza; Mehrabani, Mahsa Nazeri; Tirkolaee, Erfan Babaee
    Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments.
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    FIP: A fast overlapping community-based influence maximization algorithm using probability coefficient of global diffusion in social networks
    (Elsevier Ltd, 2023) Bouyer, Asgarali; Ahmadi Beni, Hamid; Arasteh, Bahman; Aghaee, Zahra; Ghanbarzadeh, Reza
    Influence maximization is the process of identifying a small set of influential nodes from a complex network to maximize the number of activation nodes. Due to the critical issues such as accuracy, stability, and time complexity in selecting the seed set, many studies and algorithms has been proposed in recent decade. However, most of the influence maximization algorithms run into major challenges such as the lack of optimal seed nodes selection, unsuitable influence spread, and high time complexity. In this paper intends to solve the mentioned challenges, by decreasing the search space to reduce the time complexity. Furthermore, It selects the seed nodes with more optimal influence spread concerning the characteristics of a community structure, diffusion capability of overlapped and hub nodes within and between communities, and the probability coefficient of global diffusion. The proposed algorithm, called the FIP algorithm, primarily detects the overlapping communities, weighs the communities, and analyzes the emotional relationships of the community's nodes. Moreover, the search space for choosing the seed nodes is limited by removing insignificant communities. Then, the candidate nodes are generated using the effect of the probability of global diffusion. Finally, the role of important nodes and the diffusion impact of overlapping nodes in the communities are measured to select the final seed nodes. Experimental results in real-world and synthetic networks indicate that the proposed FIP algorithm has significantly outperformed other algorithms in terms of efficiency and runtime.
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    Generating the structural graph-based model from a program source-code using chaotic forrest optimization algorithm
    (Wiley, 2023) Arasteh, Bahman; Ghanbarzadeh, Reza; Gharehchopogh, Farhad Soleimanian; Hosseinalipour, Ali
    One of the most important and costly stages in software development is maintenance. Understanding the structure of software will make it easier to maintain it more efficiently. Clustering software modules is thought to be an effective reverse engineering technique for deriving structural models of software from source code. In software module clustering, the most essential objectives are to minimize connections between produced clusters, maximize internal connections within created clusters, and maximize clustering quality. Finding the appropriate software system clustering model is considered an NP-complete task. The previously proposed approaches' key limitations are their low success rate, low stability, and poor modularization quality. In this paper, for optimal clustering of software modules, Chaotic based heuristic method using a forest optimization algorithm is proposed. The impact of chaos theory on the performance of the other SFLA-GA and PSO-GA has also been investigated. The results show that using the logistic chaos approach improves the performance of these methods in the software-module clustering problem. The performance of chaotic based FOA, SFLA-GA and PSO-GA is superior to the other heuristic methods in terms of modularization quality and stability of the results.
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    A hybrid chaos-based algorithm for data object replication in distributed systems
    (Taylor & Francis Ltd, 2024) Arasteh, Bahman; Gunes, Peri; Bouyer, Asgarali; Rouhi, Alireza; Ghanbarzadeh, Reza
    One of the primary challenges in distributed systems, such as cloud computing, lies in ensuring that data objects are accessible within a reasonable timeframe. To address this challenge, the data objects are replicated across multiple servers. Estimating the minimum quantity of data replicas and their optimal placement is considered an NP-complete optimization problem. The primary objectives of the current research include minimizing data processing costs, reducing the quantity of replicas, and maximizing the applied algorithms' reliability in replica placement. This paper introduces a hybrid chaos-based swarm approach using the modified shuffle-frog leaping algorithm with a new local search strategy for replicating data in distributed systems. Taking into account the algorithm's performance in static settings, the introduced method reduces the expenses associated with replica placement. The results of the experiment conducted on a standard data set indicate that the proposed approach can decrease data access time by about 33% when using approximately seven replicas. When executed several times, the suggested method yields a standard deviation of approximately 0.012 for the results, which is lower than the result existing algorithms produce. Additionally, the new approach's success rate is higher in comparison with existing algorithms used in addressing the problem of replica placement.
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    A metaheuristic approach based on coronavirus herd immunity optimiser for breast cancer diagnosis
    (Springer, 2024) Hosseinalipour, Ali; Ghanbarzadeh, Reza; Arasteh, Bahman; Gharehchopogh, Farhad Soleimanian; Mirjalili, Seyedali
    As one of the important concepts in epidemiology, herd immunity was recommended to control the COVID-19 pandemic. Inspired by this technique, the Coronavirus Herd Immunity Optimiser has recently been introduced, demonstrating promising results in addressing optimisation problems. This particular algorithm has been utilised to address optimisation problems widely; However, there is room for enhancement in its performance by making modifications to its parameters. This paper aims to improve the Coronavirus Herd Immunity Optimisation algorithm to employ it in addressing breast cancer diagnosis problem through feature selection. For this purpose, the algorithm was discretised after the improvements were made. The Opposition-Based Learning approach was applied to balance the exploration and exploitation stages to enhance performance. The resulting algorithm was employed in the diagnosis of breast cancer, and its performance was evaluated on ten benchmark functions. According to the simulation results, it demonstrates superior performance in comparison with other well-known approaches of the similar nature. The results demonstrate that the new approach performs well in diagnosing breast cancer with high accuracy and less computational complexity and can address a variety of real-world optimisation problems.
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    A Modified Horse Herd Optimization Algorithm and Its Application in the Program Source Code Clustering
    (Wiley-Hindawi, 2023) Arasteh, Bahman; Gunes, Peri; Bouyer, Asgarali; Gharehchopogh, Farhad Soleimanian; Banaei, Hamed Alipour; Ghanbarzadeh, Reza
    Maintenance is one of the costliest phases in the software development process. If architectural design models are accessible, software maintenance can be made more straightforward. When the software's source code is the only available resource, comprehending the program profoundly impacts the costs associated with software maintenance. The primary objective of comprehending the source code is extracting information used during the software maintenance phase. Generating a structural model based on the program source code is an effective way of reducing overall software maintenance costs. Software module clustering is considered a tremendous reverse engineering technique for constructing structural design models from the program source code. The main objectives of clustering modules are to reduce the quantity of connections between clusters, increase connections within clusters, and improve the quality of clustering. Finding the perfect clustering model is considered an NP-complete problem, and many previous approaches had significant issues in addressing this problem, such as low success rates, instability, and poor modularization quality. This paper applied the horse herd optimization algorithm, a distinctive population-based and discrete metaheuristic technique, in clustering software modules. The proposed method's effectiveness in addressing the module clustering problem was examined by ten real-world standard software test benchmarks. Based on the experimental data, the quality of the clustered models produced is approximately 3.219, with a standard deviation of 0.0718 across the ten benchmarks. The proposed method surpasses former methods in convergence, modularization quality, and result stability. Furthermore, the experimental results demonstrate the versatility of this approach in effectively addressing various real-world discrete optimization challenges.
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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.