An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial t-way test suite generation

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t -way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, metaheuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyperheuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS),using the t -way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance

Systematic Analysis of Search-Based Strategies for Combinatorial Test Suite Construction

This systematic literature review (SLR) investigates search-based strategies for generating combinatorial test suites using covering arrays (CAs) to efficiently test system interactions. Conducted following PRISMA guidelines, the review analyzes 91 primary studies published between 2003 and 2025, selected through a rigorous process from major academic databases. The identified strategies are categorized into five types: standard, mix, adaptive, hybrid, and hyper-heuristic, based on their underlying algorithmic approaches, including swarm intelligence, evolutionary algorithms, and hyper-heuristic techniques. Each strategy is examined in depth, evaluating its effectiveness in generating high-quality combinatorial test suites. The review also highlights challenges in applying these strategies to varying software testing scenarios. Based on the findings, it provides practical insights to enhance their application and effectiveness in real-world contexts. This work supports broader adoption of search-based testing to improve software quality and reduce defect rates

Hyper-heuristic strategy for input-output-based interaction testing

Software testing aims at exploring faults within software in order to ensure it meets all necessary specifications. Test case design strategies play key role in software testing. Classical test case design strategies, however, do not sufficiently include support for exploration of faults due to interaction between parameter values. New strategies known as t-way strategies (where t expresses interaction strength) have been developed for finding interaction faults. However, existing t-way strategies for input-output-based relationship (IOR) interaction testing mostly adopt greedy algorithms which often generate poor quality test data. Therefore, this paper presents the design of a new IOR test suite generation strategy called IOR_HH based on the exponential Monte Carlo with counter (EMCQ) hyper-heuristic. EMCQ is a parameter free hyper-heuristic which works as controller of the three implemented low-level meta-heuristic operators, namely crossover, peer learning and global pollination in the proposed IOR_HH strategy. Experimental results demonstrate the impact of the proposed strategy against existing computational strategies for IOR interaction testing

Assessing the chaotic map population initializations for sine cosine algorithm using the case study of pairwise test suite generation

Sine Cosine Algorithm (SCA) is a new population based meta-heuristic algorithm that exploits both the sine and cosine functions for its update operators. The main strength of SCA is its simplicity and straightforward implementation as well as provides no parameter control adjustment. For these reasons, SCA can be adopted in many optimization problems quickly and without much tuning. Despite the aforementioned advantages, SCA convergence can still be problematic depending on the initial starting positions of initial populations. In this work, we propose to assess the effectiveness of pseudo random (i.e., Random) as well as three chaotic map initializations (i.e., sine map, circle map, and logistic map) for SCA using the pairwise test case generation as our case study. The original SCA with random initialization (R_SCA) is outperformed on the adopted experiments by the proposed logistic map SCA (LM_SCA), circle map SCA (CM_SCA) and singer map SCA (SM_SCA)

An Efficient Read and Mark Mechanism for Multiple-choice Questions Using Optical Character Recognition

This research paper focuses on modifying the grading of multiple-choice questions (MCQs) to better the efficiency and incorrectness of educational tests. Conventional grading systems, such as optical mark recognition (OMR), have fundamental drawbacks, excluding the necessity for precise shading, time-wasting, and the use of special OMR sheets and OMR scanners. This conceptualization can be expensive and error-prone, especially if the MCQs papers are folded or unmarked. In comparison, the suggested OCR-based approach gives fundamental benefits in all necessary areas. It is less costly to use a simple scanner and software alternatively to costly OMR equipment. The method is motivated to be simple to set up and use. It importantly reduces error rates and marking time by employing precise OCR algorithms and processing greater amounts of answer sheets quickly. Moreover, the system is extremely accurate and scalable, allowing it to handle a rising amount of paper efficiently. It also has limited trust in external tools and is highly flexible and adaptable to different MCQ formats and grading settings. In General, the OCR-based approach outperforms existing methods by eliminating their shortcomings and delivering a trustworthy, time-saving alternative for automated MCQ grading

Utilizing the roulette wheel based social network search algorithm for substitution box construction and optimization

This paper introduces a new variant of a recent metaheuristic algorithm based on the Social Network Search algorithm (SNS), which is called the Roulette Wheel Social Network Search algorithm (SNS). As the name indicates, the main feature of RWSNS is the fact that the algorithm allows proportionate selection of its search operators (i.e., from imitation, conversation, disputation and innovation) through exploiting the roulette wheel. Additionally, RWSNS also incorporates the Piecewise map as replacement for the pseudo random generator during the population initialisation to ensure high nonlinearity and allow further solution diversification. Finally, unlike its predecessor, RWSNS also permits the systematic manipulation of candidate solutions around the global best agent through the swap operator to boost its search intensification process, as the global best candidate solution is often clustered and always lurking around the current local best. Results based on the construction of 8 × 8 substitution-box demonstrate that the proposed RWSNS exceeds other competing metaheuristic algorithms in two main S-box criteria, namely, the average nonlinearity score and strict avalanche criteria (i.e., SAC offset), whilst maintaining a commendable performance on bits independence criteria, differential approximation probability and linear approximation probability

An Efficient Read and Mark Mechanism for Multiple-choice Questions Using Optical Character Recognition

This research paper focuses on modifying the grading of multiple-choice questions (MCQs) to better the efficiency and incorrectness of educational tests. Conventional grading systems, such as optical mark recognition (OMR), have fundamental drawbacks, excluding the necessity for precise shading, time-wasting, and the use of special OMR sheets and OMR scanners. This conceptualization can be expensive and error-prone, especially if the MCQs papers are folded or unmarked. In comparison, the suggested OCR-based approach gives fundamental benefits in all necessary areas. It is less costly to use a simple scanner and software alternatively to costly OMR equipment. The method is motivated to be simple to set up and use. It importantly reduces error rates and marking time by employing precise OCR algorithms and processing greater amounts of answer sheets quickly. Moreover, the system is extremely accurate and scalable, allowing it to handle a rising amount of paper efficiently. It also has limited trust in external tools and is highly flexible and adaptable to different MCQ formats and grading settings. In General, the OCR-based approach outperforms existing methods by eliminating their shortcomings and delivering a trustworthy, time-saving alternative for automated MCQ grading

Exploring a Q-learning-based chaotic naked mole rat algorithm for S-box construction and optimization

This paper introduces a new variant of the metaheuristic algorithm based on the naked mole rat (NMR) algorithm, called the Q-learning naked mole rat algorithm (QL-NMR), for substitution box construction and optimization. Unlike most competing works (which typically integrate a single chaotic map into a particular metaheuristic algorithm), QL-NMR assembles five chaotic maps (i.e., Chebyshev, logistic, circle, Singer, and sinusoidal) as part of the algorithm itself. Using a Q-learning table, QL-NMR remembers the historical performance of each chaotic map during the S-box construction process allowing just-in-time adaptive selection based on its current performance. Experimental results for 8 × 8 S-box generation demonstrate that the proposed QL-NMR gives competitive performance against other existing works, particularly in terms of nonlinearity and strict avalanche criteria. To further demonstrate the effectiveness of our proposed work, we have subjected the QL-NMR for image segmentation using multilevel thresholding. The results confirm that QL-NMR gives better performance than its predecessor NMR. Finally, QL-NMR S-box also outperformed NMR S-box in image encryption

Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites

The teaching learning-based optimization (TLBO) algorithm has shown competitive performance in solving numerous real-world optimization problems. Nevertheless, this algorithm requires better control for exploitation and exploration to prevent premature convergence (i.e., trapped in local optima), as well as enhance solution diversity. Thus, this paper proposes a new TLBO variant based on Mamdani fuzzy inference system, called ATLBO, to permit adaptive selection of its global and local search operations. In order to assess its performances, we adopt ATLBO for the mixed strength t-way test generation problem. Experimental results reveal that ATLBO exhibits competitive performances against the original TLBO and other meta-heuristic counterparts

Hybrid Migrating Birds Optimization Strategy for t-way Test Suite Generation

Hybrid meta-heuristics algorithms have gained popularity in recent years to solve t-way test suite generation problems due to better exploration and exploitation capabilities of the hybridization. This paper presents the implementation of meta-heuristic search algorithms that are Migrating Birds Optimization (MBO) algorithm and Genetic Algorithm (GA) hybrid to a t-way test data generation strategy. The proposed strategy is called Elitist Hybrid MBO-GA Strategy (EMBO-GA). Based on the published benchmarking results, the result of these strategies is competitive with most existing strategies in terms of the generated test size in many of the parameter configurations. In the case where this strategy is not the most optimal, the resulting test size is sufficiently competitive