Test Suite Research Articles

U-AEFA: Online and offline learning-based unified artificial electric field algorithm for real parameter optimization

Optimization problems in real-world scenarios require algorithms that effectively balance exploration and exploitation to avoid local optima and achieve global solutions. To address this, we propose a unified artificial electric field algorithm (U-AEFA) that integrates both offline learning (inspired by high-performing metaheuristic algorithms) and online learning (through historical data generated during evolution). U-AEFA introduces a unique three-layer population structure to enhance search efficiency, consisting of the best agent in the first layer, the top-performing agents in the second layer, and the remaining agents in the third layer. Key features of U-AEFA include (i) an effective Coulomb’s constant for improved exploration, (ii) a non-uniform mutation operator to mitigate premature convergence, and (iii) two acceleration coefficients for enhanced performance. These three factors constitute offline learning and have been implemented to improve different design elements of the algorithm. As part of online learning, it employs a difference vector reuse (DVR) strategy to evolve the first-layer agents. The algorithm is evaluated using the CEC 2017 test suite across multiple dimensions (10, 30, 50, 100), where it consistently outperforms seven state-of-the-art algorithms, demonstrating superior accuracy and convergence speed. Moreover, U-AEFA’s robustness is validated on 12 high-dimensional feature selection problems, further highlighting its effectiveness in solving complex optimization tasks. The source code of U-AEFA is available at

Multi-image encryption scheme using cross-plane coupling permutation and plain-by-plain wave diffusion

Images contain a wealth of visual information, are susceptible to unauthorized access due to their vulnerability and sensitivity. This paper designs a novel multi-image encryption scheme for protecting the privacy of images of different sizes and types. Initially, a 2D memristive hyperchaotic map (2D-MHM) is designed and subjected to various dynamic analyses and randomness evaluations. The results demonstrate that the proposed map possesses an exceptionally large parameter space, high Lyapunov exponent and sample entropy, and has successfully passed the entire suite of NIST test, verifying its feasibility for confidential communication. Then we present a multi-image encryption scheme combining cross-plane coupling permutation and plain-by-plain wave diffusion to realize random exchange and global variation of pixels in different planes. The performance evaluation and numerical analysis demonstrate that the scheme is resilient against multifarious types of attacks, possesses great security while effectively enhancing encryption efficiency. Finally, the proposed scheme is compared with advanced algorithms and its application in healthcare is discussed, exhibiting its superiority in multiple aspects.

Knowledge-Based Perturbation LaF-CMA-ES for Multimodal Optimization

Multimodal optimization presents a significant challenge in optimization problems due to the existence of multiple attraction basins. Balancing exploration and exploitation is essential for the efficiency of algorithms designed to solve these problems. In this paper, we propose the KbP-LaF-CMAES algorithm to address multimodal optimization problems based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) framework. The Leaders and Followers (LaF) and Knowledge-based Perturbation (KbP) strategies are the primary components of the KbP-LaF-CMAES algorithm. The LaF strategy is utilized to extensively explore the potential local spaces, where two cooperative populations evolve in synergy. The KbP strategy is employed to enhance exploration capabilities. Improved variants of CMA-ES are used to exploit specific domains containing local optima, thereby potentially identifying the global optimum. Simulation results on the test suite demonstrate that KbP-LaF-CMAES significantly outperforms other meta-heuristic algorithms.

Improving the Efficiency of Software Reliability Demonstration Testing by Introducing Testing Effectiveness

For highly reliable software systems, it is expensive, time consuming, or even infeasible to perform reliability testing via a conventional software reliability demonstration testing (SRDT) plan. Moreover, in the traditional SRDT approach, the various characteristics of the software system or test sets are not considered when making the testing schemes. Some studies have focused on the effect of software testability on SRDT, but only limited situations were discussed, and many theoretical and practical problems have been left unresolved. In this paper, an extended study on the quantitative relation between test effectiveness (TE) and test effort for SRDT is proposed. Theoretical derivation is put forward by performing statistical analysis for the test suite according to TE. The combinations of all the cases of zero-failure and revealed nonzero failure, as well as discrete-type software and continuous-type software, are studied with the corresponding failure probability models constructed. That is, zero-failure and nonzero failure, as well as discrete-type software and continuous-type software, respectively, constitute the symmetry and asymmetry of SRDT. Finally, we illustrated all the models and performed applications on the Siemens program suite. The experimental results show that within the same limitation of requirements and confidence levels, this approach can effectively reduce the number of test cases and the test duration, i.e., accelerate the test process and improve the efficiency of the SRDT.

MSAO-EDA: A Modified Snow Ablation Optimizer by Hybridizing with Estimation of Distribution Algorithm.

Metaheuristic algorithms provide reliable and effective methods for solving challenging optimization problems. The snow ablation algorithm (SAO) performs favorably as a physics-based metaheuristic algorithm. Nevertheless, SAO has some shortcomings. SAO is overpowered in its exploitation, has difficulty in balancing the proportion of global and local search, and is prone to encountering local optimum traps when confronted with complex problems. To improve the capability of SAO, this paper proposes a modified snow ablation algorithm hybrid distribution estimation algorithm named MSAO-EDA. In this work, a collaborative search framework is proposed where SAO and EDA can be organically integrated together to fully utilize the exploitation capability of SAO and the exploration capability of EDA. Secondly, an offset EDA approach that combines the optimal solution and the agent itself is used to replace SAO's exploration strategy for the purpose of enhancing SAO's exploration capability. Finally, the convergence of SAO is accelerated by selecting the next generation of agents through a greedy strategy. MSAO-EDA is tested on the CEC 2017 and CEC 2022 test suites and compared with EO, RIME, MRFO, CFOA, and four advanced algorithms, AFDBARO, CSOAOA, EOSMA, and JADE. The experimental results show that MSAO-EDA has excellent efficiency in numerical optimization problems and is a highly competitive SAO variant.

Mutation‐Guided Metamorphic Testing of Optimality in AI Planning

ABSTRACTAutonomous systems such as space‐ or underwater‐exploration robots or elderly people assistance robots often include an artificial intelligence (AI) planner as a component. Starting from the initial state of a system, an AI planner automatically generates sequential plans to reach final states that satisfy user‐specified goals. Generating plans having a minimum number of intermediate steps or taking the least time to execute is usually strongly desired, as these plans exhibit minimal costs. Unfortunately, testing if an AI planner generates optimal plans is almost impossible because the expected cost of these plans is usually unknown. Based on mutation adequacy test suite selection, this article proposes a novel metamorphic testing framework for detecting the lack of optimality in AI planners. The general idea is to perform a systematic but non‐exhaustive state space exploration from the initial state and to select mutant‐adequate states to instantiate new planning tasks as follow‐up test cases. We then check a metamorphic relation between the automatically generated solutions of the AI planner for these new test cases and the cost of the initial plan. We implemented this metamorphic testing framework in a tool called MorphinPlan. Our experimental evaluation shows that MorphinPlan can detect non‐optimal behaviour in both mutated AI planners and off‐the‐shelf, configurable planners. It also shows that our proposed mutation adequacy test selection strategy outperforms three alternative test generation and selection strategies, including both random state selection and random walks through the state space in terms of mutation scores.

Автоматизований метод перевірки та налагодження тестових сценаріїв на основі формальної моделі

Introduction. Model-based test cases generation is a popular strategy for test automation. It helps to reduce time spent on the development of a test suite and can improve level of coverage. However, many reports show shortage of such test cases in poor quality and doubtable efficiency. Purpose. The main goal of the proposed method is cost-effective validation, assessment, debugging and concretization of generated test cases. The method helps improve quality and efficiency of the test cases, make their scenario meaningful and goal-oriented. The method also develops debugging facilities and simplifies data dependency analysis and test scenario editing. Methods. We propose an automated post-processing method which allows to evaluate path that is examined by the test case, and to make safe changes to the path which will eliminate the shortcomings while leaving the coverage targets of the test case unharmed. The method is based on visualization of the path along the control flow graph of the model with additional information about factual evaluation history of all variables and possible alternative variants of behavior. For consistent substitution of certain values in the signal parameters, which would determine the artifacts of the test environment (such as, for example, files, databases, etc.) and check boundary cases (in predicates of conditions, indexing of arrays, etc.), a method of interactive specification of symbolic traces has been developed. Results. The role of the user in deciding whether to add a test case to the project test suite and make changes to it remains crucial, but to reduce labor intensity, the following processes are automated: evaluation of test scenarios according to certain objective characteristics (level of coverage, ability to detect defects, data cohesion, etc.); highlighting of possible alternatives for making corrections; consistent updating of computations for the corresponding corrections. A prototype was developed based on the proposed methods. The empirical results demonstrated a positive impact on the overall efficiency (ability to detect defects and reduce resource consumption) and quality (meaningfulness, readability, maintenance, usefulness for debugging, etc) of the generated test suites. The method allows to make automatically generated test cases trustable and usable. Conclusion. The proposed toolkit significantly reduces the time spent on researching the results of test generation and validation of the obtained tests and their editing. Unlike existing simulation methods, the proposed method not only informs about the values of variables, but also explores the history of their computations and additionally provides information about admissible alternatives. Further we plan to improve the process of localizing the causes of test failure at execution phase to speed up the search for defects.

Evolutionary Dynamic Constrained Multiobjective Optimization: Test Suite and Algorithm

Evolutionary Dynamic Constrained Multiobjective Optimization: Test Suite and Algorithm

RETORCH*: A Cost and Resource aware Model for E2E Testing in the Cloud

Moving testing to the Cloud overcomes time/resource constraints by leveraging an unlimited and elastic infrastructure, especially for testing levels like End-to-End (E2E) that require a high number of resources and/or execution time. However, it introduces new challenges to those already faced on-premises, like selecting the most suitable Cloud infrastructure and billing scheme. We propose the RETORCH* test execution model that estimates and compares the monetary cost of executing an E2E test suite with different Cloud alternatives, billing schemes, and test configurations. RETORCH* goes beyond the mere cost billed, and selects the solution that best aligns with the test team strategy using the data of on-premises prior executions and the tester's experience. This cost is broken down into the cost incurred to execute the test suite (testing cost) and possible unused infrastructure (overprovisioning cost). Based on these distinct costs, the test team can compare different Cloud and test configurations. RETORCH* has been evaluated using a real-world application's E2E test suite. We analyze how the different decisions taken when the suite is migrated to the Cloud impact the cost, highlighting how RETORCH* can help the tester during Cloud and test configuration to make a more informed decision.

A Shifted Boundary Method for the compressible Euler equations

The Shifted Boundary Method (SBM) is applied to compressible Euler flows, with and without shock discontinuities. The SBM belongs to the class of unfitted (or immersed, or embedded) finite element methods and avoids integration over cut cells (and the associated implementation/stability issues) by reformulating the original boundary value problem over a surrogate (approximate) computational domain. Accuracy is maintained by modifying the original boundary conditions using Taylor expansions. Hence the name of the method, that shifts the location and values of the boundary conditions. We specifically discuss the advantages the proposed method offers in avoiding spurious numerical artifacts in two scenarios: (a) when curved boundaries are represented by body-fitted polygonal approximations and (b) when the Kutta condition needs to be imposed in immersed simulations of airfoils. An extensive suite of numerical tests is included.

Multi-strategy improved runge kutta optimizer and its promise to estimate the model parameters of solar photovoltaic modules

Harnessing the potential of solar photovoltaic (PV) technology relies heavily on accurately estimating the model parameters of PV cells/modules using real current-voltage (I-V) data. Achieving optimal parameter values is essential for the performance and efficiency of PV systems, necessitating the use of advanced optimization techniques. In our endeavor, we introduce a multi-strategy improvement approach for the Runge Kutta (RUN) optimizer, a cutting-edge tool used for tackling this critical task in both single-diode and double-diode PV unit models. By aligning experimental and model-based estimated data, our approach seeks to reduce errors and improve the accuracy of PV system performance. We conduct meticulous analyses of two compelling case studies and the CEC 2020 test suite to showcase the versatility and effectiveness of our improved RUN (IRUN) algorithm. The first case study involves a standard dataset derived from the well-known R.T.C. France silicon solar cell, where IRUN performs favorably compared to competing methods, demonstrating its effectiveness. IRUN effectively manages the complex task of defining model parameters for an industrial PV module situated at the Engineering Faculty of Düzce University in Turkey. The real-world I-V data, obtained under optimal conditions with a temperature of 30°C and solar radiance of 1000W/m2, provide strong evidence of the practical applicability and real-world benefits of our innovative method. Additional analyses through three-diode and PV module models further confirm the efficacy of the IRUN. A mean absolute error of down to 6.5E-04 and root mean square error of down to 7.3668E-04 are achieved. Our approach provides a practical and efficient tool for improving the accuracy of PV systems, enhancing their performance when compared to existing methods.

Adapting the population size in CMA-ES using nearest-better clustering method for multimodal optimization

The covariance matrix adaptation evolution strategy (CMA-ES) is one of the advanced algorithms for solving continuous single-objective optimization. In this algorithm, the population size is an important parameter which represents the number of candidate points generated in each iteration. A larger population size allows for better exploration of the search space and is typically required for handling multimodal functions, while a smaller population size facilitates quicker convergence. Adapting the population size in this algorithm raises a question about how to increase/decrease/keep it in an efficient way to solve specific problems. In this paper, we propose a novel approach for this purpose based on the information of niches observed during the evolution process. Firstly, the nearest-better clustering (NBC) technique is employed to detect the number of niches in each iteration, then the information will be used to adapt the population size accordingly. Additionally, a quasi-Newton method, which serves as a local search, can be incorporated into the algorithm in order to speed it up. We evaluate the effectiveness of our method through numerical simulations on multimodal test functions, the black-box optimization benchmarking (BBOB) noiseless testbed, and the CEC 2014 and CEC 2017 benchmark testing suites.

Multi-Strategy Enhanced Crested Porcupine Optimizer: CAPCPO

Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

A Novel Reinforcement Learning-Based Particle Swarm Optimization Algorithm for Better Symmetry between Convergence Speed and Diversity

This paper introduces a novel Particle Swarm Optimization (RLPSO) algorithm based on reinforcement learning, embodying a fundamental symmetry between global and local search processes. This symmetry aims at addressing the trade-off issue between convergence speed and diversity in traditional algorithms. Traditional Particle Swarm Optimization (PSO) algorithms often struggle to maintain good convergence speed and particle diversity when solving multi-modal function problems. To tackle this challenge, we propose a new algorithm that incorporates the principles of reinforcement learning, enabling particles to intelligently learn and adjust their behavior for better convergence speed and richer exploration of the search space. This algorithm guides particle learning behavior through online updating of a Q-table, allowing particles to selectively learn effective information from other particles and dynamically adjust their strategies during the learning process, thus finding a better balance between convergence speed and diversity. The results demonstrate the superior performance of this algorithm on 16 benchmark functions of the CEC2005 test suite compared to three other algorithms. The RLPSO algorithm can find all global optimum solutions within a certain error range on all 16 benchmark functions, exhibiting outstanding performance and better robustness. Additionally, the algorithm’s performance was tested on 13 benchmark functions from CEC2017, where it outperformed six other algorithms by achieving the minimum value on 11 benchmark functions. Overall, the RLPSO algorithm shows significant improvements and advantages over traditional PSO algorithms in aspects such as local search strategy, parameter adaptive adjustment, convergence speed, and multi-modal problem handling, resulting in better performance and robustness in solving optimization problems. This study provides new insights and methods for the further development of Particle Swarm Optimization algorithms.

Colloidal particles as noise source for random number generation

In this work, we investigate colloidal particle patterns as a possible noise source for random number generation. We systematically analyze the minimum entropy of the noise source over different particle concentrations of {1, 3, 5, 7, 10, 12, 15} mg/ml according to the recommendations of the National Institute of Standards and Technology Special Publication 800-90B. The estimated minimum entropy of the non-independent and identically distributed particle pattern noise source is Hmin = 0.5896/1 bit at a particle amount of 5 mg/ml. For further entropy extraction on the noise source data, the secure hash algorithm is used to construct an entropy source. The randomness of the derived entropy source is verified according to the National Institute of Standards and Technology Special Publication 800-22 Rev. 1a and the accompanying statistical test suite. The entropy source passes all randomness tests of the statistical test suite and shows an estimated minimum entropy of Hmin = 0.9992/1 bit.

HSMAOA: An enhanced arithmetic optimization algorithm with an adaptive hierarchical structure for its solution analysis and application in optimization problems

The Arithmetic Optimization Algorithm (AOA) has recently gained significant attention as a novel meta-heuristic algorithm. However, it faces challenges such as premature convergence and entrapment in local optima when addressing complex optimization problems. To overcome these limitations, this paper proposes an enhanced AOA, termed the Self-Adaptive Hierarchical Arithmetic Optimization Algorithm (HSMAOA). The proposed method integrates three key strategies: Firstly, a spiral-guided random walk mechanism is introduced to improve global search ability. Secondly, a novel adaptive hierarchy leader and follower mechanism is proposed, which establishes a complete multi-branch tree hierarchy with decreasing branching degrees within the population, thereby increasing information exchange among population individuals to escape local optima. Finally, a differential mutation strategy based on ranked selection is introduced to enhance candidate solution quality. HSMAOA's performance was evaluated on the CEC2022 test suite against some state-of-the-art algorithms. Results, including optimization accuracy analysis, convergence curves, and various statistical tests, demonstrate HSMAOA's superior optimization capability and robustness. In addition, tests on eight engineering structure optimization problems, including the pressure vessel design problem, the multiple disk clutch brake design problem, and the step-cone pulley problem, and so forth, further validate its effectiveness. Thus, HSMAOA shows strong competitiveness in complex optimization tasks and potential for a wide range of applications, and is an advantageous and promising alternative solution for optimization problems.

Beyond brain injury: Examining the neuropsychological and psychosocial sequelae of post-traumatic epilepsy.

This study investigates neuropsychological and psychosocial outcomes in patients with traumatic brain injury (TBI) and post-traumatic epilepsy (PTE) compared to a healthy control group. Utilizing a quasi-experimental cross-sectional design, the research involved patients with TBI and PTE referred from a Taiwanese medical center. An age- and education-matched control group of healthy adults without traumatic injuries was also recruited. The study involved analyzing retrospective medical records and applying a comprehensive suite of neuropsychological tests and psychosocial questionnaires. Executive function measures revealed significantly reduced performance in both the TBI and PTE groups compared to controls. Specifically, the MoCA scores were lowest in the PTE group, followed by the TBI group, and highest in the controls. Measures of subjective symptomatology showed comparably elevated levels in both the TBI and PTE groups relative to controls. The research suggests that PTE may intensify the difficulties faced by individuals with TBI, but its impact on overall recovery might not be significant, considering the trajectory of the brain injury itself. Notably, the MoCA results indicate that cognitive deficits are more pronounced in PTE patients compared to those with TBI, underscoring the necessity for targeted neuropsychological assessments. Further investigation is essential to explore PTE's broader neuropsychological and psychosocial impacts. These findings advocate for tailored care strategies that address both neuropsychological and psychosocial needs, ensuring comprehensive management of TBI and PTE.

Evaluation and Comparison of the Performance of Different Hypervisors under Various Workloads of GIADEC

This research study evaluates and compares the performance of Hyper-V, Citrix Xen, and VMware ESXi to aid Ghana Integrated Aluminium Development Corporation (GIADEC) in informed hypervisor selection. The study focuses on identifying the optimal hypervisor for GIADEC, considering factors like effectiveness and efficient resource utilization. It aims to improve resource allocation, hypervisor selection, and performance optimization within GIADEC's IT infrastructure. Using a qualitative approach and extensive literature review, the study supports informed decision-making for hypervisor selection. Through benchmarking with Passmark Performance Test Suite and BootRacer application, each hypervisor underwent rigorous testing across various performance metrics. The study assesses performance aspects including installation and booting times, CPU, memory, disk, and network performance. Results highlight distinct advantages of each hypervisor: Hyper-V for quick virtualization deployment, VMware ESXi for superior CPU, memory, and network performance. Hyper-V excels in memory write and disk sequential read/write speeds, while VMware ESXi demonstrates low memory latency and superior network bandwidth. It is recommended that GIADEC should consider VMware ESXi due to its outstanding overall performance and suitability for diverse workload requirements. This study adds to the knowledge base on hypervisor performance evaluation and supports GIADEC's decision-making process for optimal hypervisor selection.

A robust audio steganography technique based on image encryption using different chaotic maps

The development of innovative methods for concealing critical data in multimedia files has exploded in information security in recent years. Cryptography and steganography cannot be used alone to protect data; rather, they can be combined and used in a single system. Audio steganography is among the most important information security techniques. It involves the concealment of information within audio signals to achieve covert communication. This paper introduces a comprehensive technique that integrates chaos Henon, Baker, and Arnold maps for image encryption with audio steganography to create a robust and secure audio steganography technique. First, the target image is encrypted using chaotic maps. Then, it is embeded within the high frequencies of the cover audio signal based on the Inverse Short Time Fourier Transform (ISTFT) to be transmitted to the destination through the channel. By integrating both encryption and concealment techniques, the cover audio signal quality can be preserved. Moreover, the hidden image security and robustness are improved, making the technique resistant to many types of attacks. The simulation results confirm that the suggested technique is robust in the presence of attacks. It achieves a distinct perceptual quality with an appreciated peak signal-to-noise ratio (PSNR) of 91.2 dB and a Mean Square Error (MSE) of 7.5 × 10–10. The randomness of the resulting encrypted image has successfully passed the National Institute of Standards and Technology (NIST) statistical test suite.

A novel hybrid algorithm based on improved marine predators algorithm and equilibrium optimizer for parameter extraction of solar photovoltaic models

In the field of solar photovoltaic (PV) systems, the accurate and reliable extraction of parameters from PV models is crucial for effective simulation, evaluation, and control. Although various optimization algorithms have been widely used for parameter extraction in solar PV systems, the accuracy and reliability of the parameters extracted by these methods usually fall short of the expected standards. To address these shortcomings, a novel hybrid algorithm that combines the improved marine predators algorithm (MPA) with the equilibrium optimizer (EO), named IMPAEO, is proposed. In the IMPAEO, an elite opposition-based learning strategy is introduced to expand the exploration range of the algorithm to enhance population diversity; an adaptive weight coefficient is employed to improve the updating rule of the MPA, facilitating a balance between global exploration and local exploitation; the EO operator is integrated to enhance the performance of the MPA in the local exploitation phase to improve the solution quality and convergence speed; and the linear population size reduction (LPSR) technique is introduced to dynamically reduce the population size and improve the search performance. The effectiveness of the proposed IMPAEO is validated using the CEC2017 benchmark test suite, which is also applied to extract parameters of the single diode model, the double diode model, and three different PV modules. Comparative analysis with other algorithms demonstrates that the IMPAEO exhibits significant advantages in terms of solution quality, convergence speed, and algorithm stability. Consequently, the proposed IMPAEO not only proves to be efficient and reliable in parameter extraction for solar PV models but also offers a promising alternative in this field.