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    An Automatic Software Testing Method to Discover Hard-to-Detect Faults Using Hybrid Olympiad Optimization Algorithm
    (Springer, 2024) Zheng, Leiqing; Arasteh, Bahman; Mehrabani, Mahsa Nazeri; Abania, Amir Vahide
    The enhancement of software system quality is achieved through a process called software testing, which is a time and cost-intensive stage of software development. As a result, automating software tests is recognized as an effective solution that can simplify time-consuming and arduous testing activities. Generating test data with maximum branch coverage and fault discovery capability is an NP-complete optimization problem. Various methods based on heuristics and evolutionary algorithms have been suggested to create test suites that provide the most feasible coverage. The main disadvantages of past approaches include inadequate branching coverage, fault detection rate, and unstable results. The main objectives of the current research are to improve the branch coverage rate, fault detection rate, success rate, and stability. This research has suggested an efficient technique to produce test data automatically utilizing a hybrid version of Olympiad Optimization Algorithms (OOA) in conjunction with genetic algorithm (GA) operators theory. Maximum coverage, fault detection capability, and success rate are the main characteristics of produced test data. Various experiments have been conducted on the nine standard benchmark programs. Regarding the results, the suggested method provides 99.92% average coverage, a success rate of 99.20%, an average generation of 5.76, and an average time of 7.97 s. Based on the fault injection experiment’s results, the proposed method can discover about 89% of the faults injected by mutation testing tools such as MuJava. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
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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.