Software
S. A. H. Eshghazadi; E. Pira; M. Khodizadeh-Nahari; A. Rouhi
Abstract
Background and Objectives: Software testing plays a vital role in software development, aimed at verifying the reliability and stability of software systems. The generation of an effective test suite is key to this process, as it directly impacts the detection of defects and vulnerabilities. However, ...
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Background and Objectives: Software testing plays a vital role in software development, aimed at verifying the reliability and stability of software systems. The generation of an effective test suite is key to this process, as it directly impacts the detection of defects and vulnerabilities. However, for software systems with numerous input parameters, the combinatorial explosion problem hinders the creation of comprehensive test suites. This research introduces a novel approach using the β-Hill Climbing optimizer, an advanced variant of the traditional hill climbing algorithm, to efficiently generate optimal test suites.Methods: The β-Hill Climbing optimizer introduces a dynamic parameter, β, which facilitates a precise balance between exploration and exploitation throughout the search process. To evaluate the performance of this proposed strategy (referred to as BHC), it is compared with TConfig as a mathematical approach, PICT and IPOG as greedy algorithms, and GS, GALP, DPSO, WOA, BAPSO, and GSTG as meta-heuristic methods. These strategies are tested across a variety of configurations to assess their relative efficiency.Results: The reported results confirm that BHC outperforms the others in terms of the size of generated test suites and convergence speed. The statistical analysis of the experimental results on several different configurations shows that BHC outperforms TConfig as a mathematical strategy, PICT and IPOG as greedy strategies, GS, GALP, DPSO, WOA, BAPSO, and GSTG as meta-heuristics by 83%, 88%, 87%, 61%, 61%, 46%, 61%, 62%, and 70%, respectively.Conclusion: The BHC strategy presents a novel and effective approach to optimization, inspired by β-Hill Climbing optimizer for the generation of optimal test suite. Its superior performance in the generation of test suites with smaller size and higher convergence speed compared to other strategies.
Meta-heuristic Algorithms
E. Pira; Alireza Rouhi
Abstract
Background and Objectives: The development of effective meta-heuristic algorithms is crucial for solving complex optimization problems. This paper introduces the Society Deciling Process (SDP), a novel socio-inspired meta-heuristic algorithm that simulates the social categorization into deciles based ...
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Background and Objectives: The development of effective meta-heuristic algorithms is crucial for solving complex optimization problems. This paper introduces the Society Deciling Process (SDP), a novel socio-inspired meta-heuristic algorithm that simulates the social categorization into deciles based on metrics such as income, occupation, and education. The objective of this research is to introduce the SDP algorithm and evaluate its performance in terms of convergence speed and hit rate, comparing it with seven well-established meta-heuristic algorithms to highlight its potential in optimization tasks.Methods: The SDP algorithm's efficacy was evaluated using a comprehensive set of 14 general test functions, including benchmarks from the CEC 2019 and CEC 2022 competitions. The performance of SDP was compared against seven established meta-heuristic algorithms: Artificial Hummingbird Algorithm (AHA), Dwarf Mongoose Optimization algorithm (DMO), Reptile Search Algorithm (RSA), Snake Optimizer (SO), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Gazelle Optimization Algorithm (GOA). Statistical analysis was conducted using Friedman's rank and Wilcoxon signed-rank tests to assess the relative performance in terms of exploration, exploitation capabilities, and proximity to the optimum solution.Results: The results demonstrated that the SDP algorithm outperforms its counterparts in terms of convergence speed and hit rate across the selected test functions. In statistical tests, SDP showed significantly better performance in exploration and exploitation, leading to a higher proximity to the optimum solution compared to the other algorithms. Furthermore, when applied to five complex engineering design problems, the SDP algorithm exhibited superior performance, outmatching the state-of-the-art algorithms in terms of effectiveness and efficiency.Conclusion: The Society Deciling Process (SDP) algorithm introduces a novel and effective approach to optimization, inspired by societal structure dynamics. Its superior performance in convergence speed, exploration and exploitation capabilities, and application to complex engineering problems establishes SDP as a promising meta-heuristic algorithm. This research not only demonstrates the potential of socio-inspired algorithms in optimization tasks but also opens avenues for further enhancements in meta-heuristic algorithm designs.
