Yazar "Gunes, Peri" seçeneğine göre listele

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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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    Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
    (Mdpi, 2023) Pura, Turgut; Gunes, Peri; Gunes, Ali; Hameed, Ali Alaa
    By definition, an earthquake is a naturally occurring event. This natural event may be a disaster that causes significant damage, loss of life, and other economic effects. The possibility of predicting a natural event such as an earthquake will minimize the negative effects mentioned above. In this study, data collection, processing, and data evaluation regarding earthquakes were carried out. Earthquake forecasting was performed using the RNN (recurrent neural network) method. This study was carried out using seismic data with a magnitude of 3.0 and above of the Duzce Province between 1990 and 2022. In order to increase the learning potential of the method, the b and d values of earthquakes were calculated. The detection of earthquakes within a specific time interval in the Marmara region of Turkey, the classification of earthquake-related seismic data using artificial neural networks, and the generation of predictions for the future highlight the importance of this study. Our results demonstrated that the prediction performance could be significantly improved by incorporating the b and d coefficients of earthquakes, as well as the data regarding the distance between the Moon and the Earth, along with the use of recurrent neural networks (RNNs).
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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 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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    A Novel Metaheuristic Based Method for Software Mutation Test Using the Discretized and Modified Forrest Optimization Algorithm
    (Springer, 2023) Arasteh, Bahman; Gharehchopogh, Farhad Soleimanian; Gunes, Peri; Kiani, Farzad; Torkamanian-Afshar, Mahsa
    The number of detected bugs by software test data determines the efficacy of the test data. One of the most important topics in software engineering is software mutation testing, which is used to evaluate the efficiency of software test methods. The syntactical modifications are made to the program source code to make buggy (mutated) programs, and then the resulting mutants (buggy programs) along with the original programs are executed with the test data. Mutation testing has several drawbacks, one of which is its high computational cost. Higher execution time of mutation tests is a challenging problem in the software engineering field. The major goal of this work is to reduce the time and cost of mutation testing. Mutants are inserted in each instruction of a program using typical mutation procedures and tools. Meanwhile, in a real-world program, the likelihood of a bug occurrence in the simple and non-bug-prone sections of a program is quite low. According to the 80-20 rule, 80 percent of a program's bugs are discovered in 20% of its fault-prone code. The first stage of the suggested solution uses a discretized and modified version of the Forrest optimization algorithm to identify the program's most bug-prone paths; the second stage injects mutants just in the identified bug-prone instructions and data. In the second step, the mutation operators are only injected into the identified instructions and data that are bug-prone. Studies on standard benchmark programs have shown that the proposed method reduces about 27.63% of the created mutants when compared to existing techniques. If the number of produced mutants is decreased, the cost of mutation testing will also decrease. The proposed method is independent of the platform and testing tool. The results of the experiments confirm that the use of the proposed method in each testing tool such as Mujava, Muclipse, Jester, and Jumble makes a considerable mutant reduction.