Set Of Test Cases Research Articles

A fast and high-fidelity multi-parameter thermal-field prediction system based on CFD and POD coupling: Application to the RPV insulation structure

The thermal performance of the reactor pressure vessel (RPV) insulation structure plays a crucial role in ensuring the operational safety of the reactor. In this study, a fast and high-fidelity prediction system is proposed, which is based on the snapshot ensemble generated by the computational fluid dynamics (CFD) method and incorporates proper orthogonal decomposition (POD) as its core component to predict the heat transfer performance of the RPV insulation structure. The proposed thermal-field prediction system is employed to examine the impacts of air inlet temperature, mass flow rate, and RPV outer wall temperature on the thermal performance parameters: average heat flux qout and temperature Tout of the RPV insulation outer wall, as well as the maximum local temperature Tmax of the concrete pit inner wall. The POD predictive results showcase that the maximum relative deviations of qout, Tout and Tmax for eight sets of off-design cases are 2.25 %, 3.04 %, and 2.37 %, respectively. Meanwhile, the maximum values of qout, Tout and Tmax are 90.13 W·m−2, 44.69 ℃, and 87.51 ℃, respectively. They are all less than their prescribed limits. The field distributions of heat flux and temperature in off-design cases exhibit consistency between the CFD and POD methods. Notably, in comparison to the CFD simulation method, the POD method capturing more than 99.9 % energy demonstrates significant computational time savings for 99.87 %. Moreover, a multivariate linear regression (MLR) model for thermal performance parameters is established based on the POD prediction datasets. The results indicate that the maximum relative deviations between the POD and MLR methods in predicting qout, Tout and Tmax for the 72 sets of test cases are 5.1 %, 7.9 % and 7.1 %, respectively. Meanwhile, 99.99 % CPU time is reduced for MLR model compared with CFD method. Obviously, the predictive system showcases excellent accuracy and efficiency in forecasting.

Generalization of cut-in pre-crash scenarios for autonomous vehicles based on accident data

The utilization of high-risk test cases constitutes an effective approach to enhance the safety testing of autonomous vehicles (AVs) and enhance their efficiency. This research paper presents a derivation of 2052 high-hazard pre-crash scenarios for testing autonomous driving, which were based on 23 high-hazard cut-in accident scenarios from the National Automobile Accident In-Depth Investigation System (NAIS) through combining importance sampling and combined testing methods. Compared to the direct combination of the original distribution after sampling, the proposed method has a 2.92 times higher crash rate of 69.32% for the test case set in this paper. It also has a 5.8 times higher rate of triggering Automatic Emergency Braking (AEB), improving hazardous scenario coverage. Using the proposed method, the generated parameters of the cut-in accident scenario test set were compared with those of the cut-in test scenarios included in existing Chinese autonomous driving test protocols and standards. The velocity of the ego-vehicle obtained using the proposed method matched those in the existing protocols, whereas the velocity, time gap, and time to collision of the target vehicle were significantly lower than those existing protocols indicating scenarios obtained from accident data can enrich the selection of testing scenarios for autonomous driving.

Basis path coverage testing of MPI programs based on multi-task evolutionary optimization

A Message-Passing Interface (MPI) program usually consists of several processes, and a target path of this program is composed of a target sub-path selected in each process. However, there may be coverage conflict between different target sub-paths in the target path, and there is currently no effective approach to avoid this problem. Therefore, this paper proposes an approach of basis path coverage testing of MPI programs based on multi-task evolutionary optimization, which is used to generate a test case set that meets the criteria of branch and sentence coverage. Firstly, the proposed approach samples a certain number of representative test inputs, and calculates the coverage level of each test input for each target sub-path in the target path, which is used to group the target sub-paths with coverage conflict. Then, we construct an optimization model for each group of the target sub-paths, which can guide test case generation with high effectiveness. Finally, each optimization model is solved by an evolutionary algorithm, and the efficiency of solving these optimization models is improved based on a multi-task framework. The proposed approach is applied to basis path coverage testing of seven benchmark MPI programs under test, and compared with several state-of-the-art approaches. Experimental results indicate that the proposed approach has significant advantages in test case generation, as well as performs good scalability.

Reliability Optimization of Traffic Routing in Train Communication Networks Based on Faulty Link Diagnosis

With the increasing adoption of industrial Ethernet in train communication network (TCN), the limitations of insufficient reliability and incompatibility between different Ethernet technologies have become increasingly apparent. To address these challenges, time‐sensitive networking (TSN) has emerged as a solution, enabling multi‐network convergence and enhancing data transmission determinism and reliability through techniques such as frame replication and elimination for reliability (FRER). However, the utilization of these policies will adversely affect schedulability due to increased network load. To address the issue of link failures in TCN topologies, a routing policy based on faulty link diagnosis is proposed. The proposed policy utilizes an improved max‐min ant system (IMMAS) for diagnosing and resolving faulty links. Furthermore, a routing optimization algorithm is introduced to enhance transmission reliability by planning alternative paths for traffic. To evaluate the performance of the proposed routing algorithm, multiple sets of test cases are conducted separately under different failure scenarios to compare them with other routing policies. The results demonstrate the superior reliability of the proposed routing policy over alternative approaches. Additionally, the traffic utilizing the proposed routing policy show improved schedulability following integer linear programming (ILP) scheduling. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Multi-factor based Regression Test Case Prioritization using Fuzzy Logic

The maintenance level activity generally done after the modification in the software to check whether it is functioning right or not is termed as regression testing. Test case prioritization, a key practice, involves strategically ordering test cases based on specific criteria to enhance the efficiency of fault detection within a condensed time frame. The fuzzy rule base serves as an alternative to the conventional crisp value set, offering a nuanced approach beyond binary outcomes (Yes or No). The primary objective of this research is to address critical factors often overlooked in existing literature on prioritization. Notably, prevalent approaches focus on singular factors during test case prioritization, highlighting the need for a comprehensive technique. To enhance the prioritization of test cases, there is a demand for a method that considers multi-factors or combinations thereof, ultimately increasing effectiveness. This paper introduces an innovative approach a multi-factors regression test-case prioritization technique utilizing fuzzy rules. The methodology aims to optimize the prioritization of test cases, striking a balance between effectiveness and time efficiency. Fuzzy rules are formulated to assess the effectiveness of a prioritized set of test cases in developing the proposed approach. A user-friendly tool has been developed to facilitate the application of this technique, allowing users to input relevant factors and subsequently prioritize test cases accordingly. Through extensive experiments using the developed tool, the effectiveness of the proposed approach has been validated. The results demonstrate that the priority lists of test cases generated for different projects, considering multi-factors, show greater promise compared to techniques relying solely on a single factor for prioritization.

Investigating the readability of test code

ContextThe readability of source code is key for understanding and maintaining software systems and tests. Although several studies investigate the readability of source code, there is limited research specifically on the readability of test code and related influence factors.ObjectiveIn this paper, we aim at investigating the factors that influence the readability of test code from an academic perspective based on scientific literature sources and complemented by practical views, as discussed in grey literature.MethodsFirst, we perform a Systematic Mapping Study (SMS) with a focus on scientific literature. Second, we extend this study by reviewing grey literature sources for practical aspects on test code readability and understandability. Finally, we conduct a controlled experiment on the readability of a selected set of test cases to collect additional knowledge on influence factors discussed in practice.ResultsThe result set of the SMS includes 19 primary studies from the scientific literature for further analysis. The grey literature search reveals 62 sources for information on test code readability. Based on an analysis of these sources, we identified a combined set of 14 factors that influence the readability of test code. 7 of these factors were found in scientific and grey literature, while some factors were mainly discussed in academia (2) or industry (5) with only limited overlap. The controlled experiment on practically relevant influence factors showed that the investigated factors have a significant impact on readability for half of the selected test cases.ConclusionOur review of scientific and grey literature showed that test code readability is of interest for academia and industry with a consensus on key influence factors. However, we also found factors only discussed by practitioners. For some of these factors we were able to confirm an impact on readability in a first experiment. Therefore, we see the need to bring together academic and industry viewpoints to achieve a common view on the readability of software test code.

ChatGPT Translation of Program Code for Image Sketch Abstraction

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter’s capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI’s computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development.

Combinatorial Test Case Generation Based on ROBDD and Improved Particle Swarm Optimization Algorithm

In applications of software testing, the cause–effect graph method is an approach often used to design test cases by analyzing various combinations of inputs with a graphical approach. However, not all inputs have equal impacts on the results, and approaches based on exhaustive testing are generally time-consuming and laborious. As a statute-based software-testing method, combinatorial testing aims to select a small but effective number of test cases from the large space of all possible combinations of the input values for the software to be tested, and to generate a set of test cases with a high degree of coverage and high error detection capability. In this paper, the reduced ordered binary decision diagram is utilized to simplify the cause–effect graph so as to reduce the numbers of both the inputs and test cases, thereby saving the testing cost. In addition, an improved particle swarm optimization algorithm is proposed to significantly reduce the computation time needed to generate test cases. Experiments on several systems show that the proposed method can generate excellent results for test case generation.

Automatic testing of runtime enforcers with Test4Enforcers

Users regularly use apps to access services in a range of domains, such as health, productivity, entertainment, and business. The safety and correctness of the runtime behavior of these apps is thus a key concern for users. Indeed, unreliable apps may generate dissatisfaction, frustration and issues to users.Runtime enforcement techniques can be used to implement software enforcers that monitor executions and apply corrective actions when needed, potentially preventing misbehaviors and failures. However, enforcers might be faulty themselves, applying the wrong actions or missing to apply the right actions.To address this problem, this paper presents Test4Enforcers, an approach to automatically test software enforces. Test4Enforcers relies on an enforcement model describing the strategy that shall be applied at runtime to correct misbehaviors. Test4Enforcers first uses the enforcement model to derive a specification of the test cases that shall be executed to validate any software enforcer implemented from the given model. Then, it automatically turns the test specification into a set of concrete test cases that can be executed against apps augmented with the enforcers.We evaluated Test4Enforces with a set of 3,135 faults injected in the enforcers derived from 13 enforcement models. Results show that Test4Enforcers can automatically reveal 64% of the faults, while existing approaches relying on crash detection can only reveal 6% of the faults. Test4Enforcers is also practical since testing an enforcer required 9 min, in the worst case.

A method of test case set generation in the commutativity test of reduce functions

A method of test case set generation in the commutativity test of reduce functions

ICVTest: A Practical Black-Box Penetration Testing Framework for Evaluating Cybersecurity of Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are equipped with extensive electronic control units which offer convenience but also pose significant cybersecurity risks. Penetration testing, recommended in ISO/SAE 21434 “Road vehicles—Cybersecurity engineering”, is an effective approach to identify cybersecurity vulnerabilities in ICVs. However, there is limited research on vehicle penetration testing from a black-box perspective due to the complex architecture of ICVs. Additionally, no penetration testing framework has been proposed to guide security testers on conducting penetration testing for the whole vehicle. The lack of framework guidance results in the inexperienced security testers being uncertain about the processes to follow for conducting penetration testing. Moreover, the inexperienced security testers are unsure about which tests to perform in order to systematically evaluate the vehicle’s cybersecurity. To enhance the penetration testing efficiency of ICVs, this paper presents a black-box penetration testing framework, ICVTest. ICVTest proposes a standardized penetration testing process to facilitate step-by-step completion of the penetration testing, thereby addressing the issue of inexperienced testers lacking guidance on how to initiate work when confronted with ICV. Also, ICVTest includes 10 sets of test cases covering hardware and software security tests. Testers can select appropriate test cases based on the specific cybersecurity threats faced by the target object, thereby reducing the complexity of penetration testing tasks. Furthermore, we have developed a vehicle cybersecurity testing platform for ICVTest that seamlessly integrates various testing tools. The platform enables even novice testers to conduct vehicle black-box penetration testing in accordance with the given guidance which addresses the current industry’s challenge of an overwhelming number of testing tasks coupled with a shortage of skilled professionals. For the first time, we propose a comprehensive black-box penetration testing framework and implement the framework in the form of a cybersecurity testing platform. We apply ICVTest to evaluate an electric vehicle manufactured in 2021 for assessing the framework’s availability. With the aid of ICVTest, even testers with limited experience in automotive penetration can effectively evaluate the security risks of ICVs. In our experiments, numerous cybersecurity vulnerabilities were identified involving in-vehicle sensors, remote vehicle control systems, and in-vehicle controller area network (CAN) bus.

Enhancing Performance of Software Testing Automation using Selenium Web Grid

Web application testing is crucial to ensuring software quality and dependability. Selenium, a popular open-source testing framework, is widely used for automating web browsers to perform various testing tasks. One of the key components of Selenium is Selenium WebGrid, a distributed testing infrastructure that allows running tests in parallel across multiple nodes. This paper presents a comprehensive study on measuring the performance of Selenium WebGrid. The objective is to evaluate its efficiency, scalability, and reliability in executing test scripts across different browsers and operating systems. The study compares the performance of Selenium WebGrid with other testing approaches, such as traditional Selenium Grid and single-node Selenium execution. The methodology employed in this study involves designing a set of representative test cases that simulate real-world scenarios. These test cases cover a range of typical web application functionalities, including form submissions, page navigation, and data validation. The performance metrics considered for evaluation include execution time, resource utilization, and test throughput. To measure the performance of Selenium WebGrid, a testbed environment is set up consisting of multiple nodes with different configurations. The test scenarios are executed on the WebGrid infrastructure, and the performance metrics are collected and analyzed. The results are then compared with those obtained from traditional Selenium Grid and single-node Selenium execution to identify the performance advantages and limitations of Selenium WebGrid. It demonstrates the improved scalability and efficiency of WebGrid in executing tests across multiple nodes simultaneously. The results also provide insights into the resource requirements and trade-offs associated with using Selenium WebGrid compared to other testing approaches. This work adds to the body of knowledge by presenting empirical data on Selenium WebGrid's performance characteristics. It serves as a valuable resource for software testing professionals and researchers interested in leveraging Selenium WebGrid for distributed web application testing.

Discontinuous Galerkin methods for axisymmetric flows

In this article, high order discontinuous Galerkin methods for axisymmetric flows are developed. A first work is performed to put the equations in a so called canonical form to gather terms of similar nature, and also for ensuring the three dimensional conservativity. Then the numerical scheme is developed. Instead of relying on the strong axisymmetric formulation, we rely on the θ-averaged form of the standard three-dimensional discontinuous Galerkin method for the Navier–Stokes system. This allows to derive a numerical scheme without ambiguity on the source terms. The numerical scheme is new and makes link between different formulations previously proposed. A wide and progressive set of test cases, especially designed for axisymmetric problems is proposed. The numerical scheme is tested using these cases and the optimal order of accuracy is obtained.

Sensitivity Analysis of Transient Forced Response of Mistuned Bladed Disks Under Complex Excitation and Varying Rotation Speed

Abstract During accelerations and decelerations of gas-turbine engines, the transient vibration effects in bladed disk vibration become significant and the transient response needs to be efficiently and accurately assessed. This paper develops an effective method for efficient calculations of the sensitivity of the transient vibration response for a mistuned-bladed disk under varying rotation speeds. The sensitivities are calculated concerning two major factors: (i) the blade mistuning and (ii) the rotor acceleration/deceleration rates. The method uses the large-scale finite element modeling of the bladed disk to accurately describe the dynamic properties of the mistuned bladed disk. The method can be applied for a case when the loading is the transient load, with the amplitude and frequency spectrum changing with rotation speed. The dependency of the modal characteristics on the rotation speed is included in the formulation of the sensitivity analysis method. Efficient reduced order models are used to reduce the computational cost, which can adapt to the effects of the varying rotation speed on the natural frequencies and mode shapes of the mistuned bladed disk under transient conditions. The expressions for the sensitivities are derived analytically which provides high accuracy and speed of the calculations. The method has been implemented in a computer code and thoroughly verified using a set of test cases. The studies of transient forced response sensitivity have been performed for realistic bladed disk models.

An almost fail-safe a-posteriori limited high-order CAT scheme

In this paper we blend the high order Compact Approximate Taylor (CAT) numerical schemes with an a posteriori Multi-dimensional Optimal Order Detection (MOOD) paradigm to solve hyperbolic systems of conservation laws in 2D. The resulting scheme presents high accuracy on smooth solutions, essentially non-oscillatory behavior on irregular ones, and almost fail-safe property concerning positivity issues. The numerical results on a set of sanity test cases and demanding ones are presented assessing the appropriate behavior of the CAT-MOOD scheme.

ESSENT: an arithmetic optimization algorithm with enhanced scatter search strategy for automated test case generation

As one of the main research tasks in software testing, automated test case generation based on path coverage (ATCG-PC) aims to achieve maximum path coverage with a minimized set of test cases. In ATCG-PC, the correlation among the dimensions of test cases is widely utilized in academia to minimize the search efforts of the search-based algorithm. Nevertheless, the information related to target path selection is not utilized, which leads to blind decision-making by the search-based algorithm during the target path selection. Therefore, this paper proposes an enhanced scatter search strategy by using opposition-based learning. An arithmetic optimization algorithm is also proposed to solve ATCG-PC based on the enhanced scatter search strategy, namely, ESSENT. The ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path, and generates new test cases that cover the target path by modifying the dimensions of the existing test cases. The performance of the ESSENT algorithm is evaluated on six iFogSim subprograms and six Stanford coreNLP subprograms. Experiment results show that the ESSENT algorithm achieves a higher convergence rate than other state-of-the-art algorithms. Furthermore, it enables maximum path coverage with fewer test cases.

Comparative Analysis of Electronic Structures Calculations: A Simple Test Case Set for Kohn-Sham Density Functional Theory and Hartree-Fock Methods

A comparative analysis on the performance of Kohn-Sham density functional theory (KS-DFT) and Hartree-Fock (HF) methods to obtain reliable energy and electronic properties has been performed in this study using a simple test case. It is crucial to re-emphasize the key differences between these methods to address common conceptual difficulties that occur among freshmen studying basic computational chemistry. The results suggested that the eigenvalue theorem in determining ionization potential could be well implemented in the HF but not in the KS-DFT method. The total energy difference between ionized and non-ionized species was an appropriate procedure to calculate the first ionization potential within the KS-DFT method. The HOMO-LUMO gap in the HF was larger than the gaps obtained from the KS-DFT method. Among all of the performed calculation methods, the B3LYP hybrid functional provided better total energy where the eigenvalues were located between the HF and the LDA/GGA functionals.

Enhancing smart grid resilience with deep learning anomaly detection prior to state estimation

State estimation is a critical process in modern power system control centers that operate under strict real-time requirements. Among its key post-processing components are bad data detection and identification, with the latter being more complex and potentially compromising the system’s real-time performance. This paper proposes a novel component in the state estimation process called Anomaly Detection and Identification Module (ADIM), whose goal is to detect anomalies, or erroneous data points, prior to the state estimation process. Our work presents a deep learning-based method that exhibits a commendable level of accuracy in detecting anomalies within a continuous stream of measurements. ADIM’s capabilities are demonstrated through comprehensive experiments on a set of test cases that effectively encompass various network configurations, transformer types, and load scenarios. It is shown that ADIM can detect and identify anomalies effectively, significantly reducing the need for the bad data detection component and improving overall state estimation responsiveness. Our work lays the foundation for developing a detection- and identification-based anomaly system for power system measurements.

A low diffusion flux-split scheme for all Mach number flows

A low diffusion version of the Harten–Lax–Leer convective pressure split (HLL-CPS) scheme for resolving the shear layers and the flow features at low Mach numbers is presented here. The low diffusion HLL-CPS scheme is obtained by reconstructing the velocities at the cell interface with the face normal Mach number and a pressure function. Asymptotic analysis of the modified scheme shows a correct scaling of the pressure at low Mach numbers and a significant reduction in numerical dissipation. The robustness of the HLL-CPS scheme for strong shock is improved by reducing the contribution of the contact wave in the vicinity of the shock. The improvement in robustness for strong shock is demonstrated analytically through linear perturbation and matrix stability analyses. A set of numerical test cases are solved to demonstrate the efficacy of the proposed scheme over a wide range of Mach numbers.

A simple HLLE-type scheme for all Mach number flows

A simple HLLE-type scheme is proposed for all Mach number flows. In the proposed scheme, no extra wave structure is added in the HLLE scheme to resolve the shear wave while the contact wave is resolved by adding a wave structure similar to the HLLEM scheme. The resolution of the shear layers and the flow features at low Mach numbers is achieved by a velocity reconstruction method based on the face normal Mach number. Robustness against the numerical instabilities is achieved by scaling the velocity reconstruction function in the vicinity of shock with a multi-dimensional pressure sensor. The ability of the proposed scheme to resolve low Mach flow features is demonstrated through asymptotic analysis while the stability of the proposed scheme for strong shock is demonstrated through linear perturbation and matrix stability analyses. A set of numerical test cases are solved to show that the proposed scheme is free from numerical shock instability problems at high speeds and is capable of resolving the flow features at very low Mach numbers.