Proposed Test Cases Research Articles

An explicit dynamics framework suited to highly non-smooth interface behaviors

Dynamic systems, and in particular mechanical structures, may be subjected to non-smooth loadings such as impacts or shocks. Moreover, their behavior itself may exhibit more or less non-smooth evolutions, as when fracture occurs. Therefore, robust simulation models are of interest to capture such behaviors. A particular focus is made herein on time-stepping explicit dynamics schemes to allow efficient simulations, and non-smoothness is embedded within the discrete resolution model, so that robust simulations can be obtained, with a minimum number of numerical parameters. The original contributions of this article lie in the way the non-smooth behavior is formulated to be embedded in an explicit dynamics framework. This study focuses on the solver for dynamics with non-smooth interface behavior, rather than on the behavior models themselves. The applications concern non-smooth interface behaviors at macroscopic scale, between displacement jump on the 2D interface surface with no thickness, and interfacial force distributions acting on the bodies apart the interface. The proposed test cases which can serve as benchmarks for simulation codes, concern in a first step contact and perfectly plastic interface behavior (for illustrative purpose, on a 0D example). The last numerical test deals with contact, friction, fracture and adhesion for an extrinsic perfectly brittle interface behavior, to exemplify the feasibility on a full 3D finite element model.

Multi-granularity coverage criteria for deep reinforcement learning systems

Deep reinforcement learning (DRL) systems are progressively being deployed in safety- and security-critical domains, such as self-driving cars and unmanned aerial vehicles, raising concerns about their trustworthiness. DRL, a integration of deep learning (DL) and reinforcement learning (RL) principles, represents a machine learning technique. Existing DL coverage criteria primarily focus on neuron coverage, overlooking the unique features of RL, thus falling short in assessing the test adequacy of DRL systems.This paper introduces the first set of coverage criteria designed to systematically measure the elements of DRL systems exercised by test inputs. DRL elements, including states, actions, and policies, are leveraged to define coverage criteria that consider multi-levels and multi-granularities. Furthermore, these coverage criteria undergo optimization through the application of combinatorial coverage principles. State coverage is employed as feedback to guide test case selection for DRL systems. Empirical studies have been conducted to assess the performance of the proposed coverage criteria on five well-known DRL environments. The experiments demonstrate the effectiveness of these coverage criteria in detecting unexpected behaviors, highlighting that the proposed test case selection guided by state coverage serves as an effective strategy.

Proposed Test Case Generation Model using Fuzzy Logic (TCGMFL)

This research addresses the pressing need to enhance software testing, specifically focusing on white-box testing and basis path generation. Software testing is a linchpin in the software development process, ensuring software operates flawlessly and aligns with its intended objectives. However, this phase often incurs substantial time and resource investments. The primary aim of this study is to introduce an efficient and automated approach for basis path generation, a crucial component of white-box testing. The model commences by transforming source code into a tailored control flow graph (CFG), streamlining the automated generation of test paths. Central to this model is an algorithm for generating test paths (AGTP), meticulously traversing CFG nodes from source to destination. The algorithm’s design aims to comprehensively cover all test paths within the CFG. To enhance testing process efficiency, the model employs k-means clustering to generate and cluster inputs. Path coverage is rigorously assessed for each cluster, and fuzzy logic is used to determine the optimal path. The overarching goals of this research are to reduce time and financial costs associated with software testing while maintaining precision and efficiency. The model’s effectiveness in generating test cases is confirmed through the examination of multiple examples, underscoring its valuable contribution to software testing. This study marks a significant advancement toward more effective and cost-efficient software testing methodologies.

Comprehensive Safety Evaluation of Highly Automated Vehicles at the Roundabout Scenario

A highly automated vehicle (HAV) is a safety-critical system. Therefore, a verification and validation (V&V) process that rigorously evaluates the safety of HAVs is necessary before their release to the market. In this paper, we propose an interaction-aware safety evaluation framework for the HAV and apply it to the roundabout entering scenario. Instead of assuming that the primary other vehicles (POVs) take predetermined maneuvers, we model the POVs as game-theoretic agents. To capture a wide variety of interactions between the POVs and the vehicle under test (VUT), we use level- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> game theory and social value orientation (SVO) to characterize the interactive behaviors and train a diverse library of POVs using reinforcement learning. The game-theoretic library, together with initial conditions, form a rich testing space for the two-POV roundabout scenario. On the other hand, we propose an adaptive test case generation scheme based on adaptive sampling, stochastic optimization and upper confidence bound (UCB) algorithm to efficiently generate customized challenging cases for the VUT from the testing space. In simulations, the proposed testing space design captured a wide range of interactive patterns at the roundabout scenario. The proposed test case generation scheme was found to cover the failure modes of the VUT more effectively compared to other test case generation approaches.

Using deep learning for selenium web UI functional tests: A case-study with e-commerce applications

The application of test cases for detecting the faults within the software is called software testing. Manual testing is laborious and time-consuming hence automation tools to test software were introduced. Despite the use of automation tools at the User Interface (UI) level of the test pyramid, the limitations of current automation tools like automated test case generation and automated repairing of fragile tests still force us to carry out a large amount of manual testing. In this paper, we propose a novel method using AI to address the given challenges. With our proposed method test cases are automatically generated from the structure of the UI using a pipelined architecture of object detection, text detection and NLP models. We show that the test cases generated by the proposed framework can be translated into executable test scripts using a simple parser. The proposed method generates an average of 98.8% correct executable test cases for the applications under study. We also show the capability of the proposed method in generating new tests automatically when the application is modified. The proposed method generates an average of 98.605% correct executable test cases when the UI is modified for the applications under study. We also empirically prove that a GPU implementation of the proposed framework results in just an additional average runtime of 0.92 seconds per test case which is significantly low given the benefits of automated generation of test scripts and automated repairing of fragile tests.

Anisotropic adaptive body-fitted meshes for CFD

Mesh adaptation for immersed solid is one of the most challenging topics in computational mechanics. In this contribution, we propose a novel anisotropic adaptive body-fitted mesh method in particular for Computational Fluid Dynamics (CFD) where the boundary layers and the high gradient of a solution at a fluid–solid interface play a major role. It is characterized by its simplicity to implement, and by its generality to tackle complex geometries without additional efforts. It combines two successive iterations, knowing that the order of each one is important. First, a gradient based metric construction uses the gradients of the level-set of any immersed object to generate an anisotropic well adapted mesh. Since the elements are well stretched along the interface, and therefore the interface passes through these elements, a second step is implemented and applied. It is based on R-adaptation and swapping, to provide a sharp anisotropic fitted mesh. The proposed test cases, with and without a CFD solution, show the flexibility and the accuracy of the proposed automated h- and r- adaption framework designed for immersed methods.

A new C1 4-node flat laminated shell element based on a generalized plate formulation

The performance of an element with C1 continuity in the thickness direction was investigated within the scope of unified plate and shell formulations considering skew elements and the shear locking problem. A remedy for the shear locking pathology was found in the form of an algorithm which applies a penalty factor when the element is thinner than LE/H=2. Moreover, the proposed test cases showed the ability of Caliri’s Generalized Formulation - CGF to better control the through-the-thickness behavior of the structure at the boundary conditions. Consistent results were obtained even for skew meshes and laminated shells.

Optimal Micro-Siting of Weathervaning Floating Wind Turbines

This paper presents a novel tool for optimizing floating offshore wind farms based on weathervaning turbines. This solution is grounded on the ability of the assembly (wind turbine plus floater) to self-orientate into the wind direction, as this concept is allowed to freely pivot on a single point. This is a passive yaw potential solution for floating wind farms currently in the demonstration phase. A genetic algorithm is proposed for optimizing the levelised cost of energy by determining the geographical coordinates of the pivot points (i.e., the position over which the assembly can rotate to self-orient to the incoming wind direction). A tailored evaluation module is proposed to take into account the weathervaning motion around the pivot point depending on the incoming wind direction. The results obtained show the suitability of the proposed method to solve the addressed problem under realistic conditions. Additionally, the influence of the feasible region defined by the plot and the maximum area occupied on floating offshore wind farm design are also analysed in the proposed test cases. These deployable area constraints are of great importance for the viability of this technology, as it requires more space than classical solutions anchored to a fixed point.

Tri-level regression testing using nature-inspired algorithms

A software needs to be updated to survive in the customers’ ever-changing demands and the competitive market. The modifications may produce undesirable changes that require retesting, known as regression testing, before releasing it in the public domain. This retesting cost increases with the growth of the software test suite. Thus, regression testing is divided into three techniques: test case prioritization, selection, and minimization to reduce costs and efforts. The efficiency and effectiveness of these techniques are further enhanced with the help of optimization techniques. Therefore, we present the regression testing using well-known algorithms, genetic algorithm, particle swarm optimization, a relatively new nature-inspired approach, gravitational search algorithm, and its hybrid with particle swarm optimization algorithm. Furthermore, we propose a tri-level regression testing, i.e., it performs all the three methods in succession. Nature-inspired algorithms prioritize the test cases on code coverage criteria. It is followed by selecting the modification-revealing test cases based on the proposed adaptive test case selection approach. The last step consists of the removal of redundant test cases. The hybrid algorithm performed well for the average percentage of statement coverage, and the efficiency of genetic algorithm and particle swarm optimization is better comparatively. The proposed test case selection method can select at least 75% modification-revealing test cases using nature-inspired algorithms. Additionally, it minimizes the test suite with full statement coverage and almost negligible fault coverage loss. Overall, the simulation results show that the proposed hybrid technique outperformed the other algorithms.

Fuel-Efficient Powered Descent Guidance on Large Planetary Bodies via Theory of Functional Connections.

In this paper we present a new approach to solve the fuel-efficient powered descent guidance problem on large planetary bodies with no atmosphere (e.g., Moon or Mars) using the recently developed Theory of Functional Connections. The problem is formulated using the indirect method which casts the optimal guidance problem as a system of nonlinear two-point boundary value problems. Using the Theory of Functional Connections, the problem's linear constraints are analytically embedded into a functional, which maintains a free-function that is expanded using orthogonal polynomials with unknown coefficients. The constraints are always analytically satisfied regardless of the values of the unknown coefficients (e.g., the coefficients of the free-function) which converts the two-point boundary value problem into an unconstrained optimization problem. This process reduces the whole solution space into the admissible solution subspace satisfying the constraints and, therefore, simpler, more accurate, and faster numerical techniques can be used to solve it. In this paper a nonlinear least-squares method is used. In addition to the derivation of this technique, the method is validated in two scenarios and the results are compared to those obtained by the general purpose optimal control software, GPOPS-II. In general, the proposed technique produces solutions of accuracy. Additionally, for the proposed test cases, it is reported that each individual TFC-based inner-loop iteration converges within 6 iterations, each iteration exhibiting a computational time between 72 and 81 milliseconds, with a total execution time of 2.1 to 2.6 seconds using MATLAB. Consequently, the proposed methodology is potentially suitable for real-time computation of optimal trajectories.

A Priori Neural Networks Versus A Posteriori MOOD Loop: A High Accurate 1D FV Scheme Testing Bed

In this work we present an attempt to replace an a posteriori MOOD loop used in a high accurate Finite Volume (FV) scheme by a trained artificial Neural Network (NN). The MOOD loop, by decrementing the reconstruction polynomial degrees, ensures accuracy, essentially non-oscillatory, robustness properties and preserves physical features. Indeed it replaces the classical a priori limiting strategy by an a posteriori troubled cell detection, supplemented with a local time-step re-computation using a lower order FV scheme (ie lower polynomial degree reconstructions). We have trained shallow NNs made of only two so-called hidden layers and few perceptrons which a priori produces an educated guess (classification) of the appropriate polynomial degree to be used in a given cell knowing the physical and numerical states in its vicinity. We present a proof of concept in 1D. The strategy to train and use such NNs is described on several 1D toy models: scalar advection and Burgers’ equation, the isentropic Euler and radiative M1 systems. Each toy model brings new difficulties which are enlightened on the obtained numerical solutions. On these toy models, and for the proposed test cases, we observe that an artificial NN can be trained and substituted to the a posteriori MOOD loop in mimicking the numerical admissibility criteria and predicting the appropriate polynomial degree to be employed safely. The physical admissibility criteria is however still dealt with the a posteriori MOOD loop. Constructing a valid training data set is of paramount importance, but once available, the numerical scheme supplemented with NN produces promising results in this 1D setting.

Design of Proposed Test Case Prioritization Model for Test Sequence Generation and Validating Performance against Existing Methods

Regression testing is an effective tool or technique used for minimizing the efforts and resource consumption when a new version or the modification in the software project is tested. In such project, the testing is performed only on the selected modules or testcases. The sequence of the testcase execution also matter in regression testing. Researchers used various evolutionary, swarm based and mathematical methods for testcase prioritization and optimization. Researchers also used the fault based or coverage based measures for testcase prioritization. In this paper, the functional contribution of the recent research is provided. The research method, projects and the contribution of the work is discussed in this paper. In this paper, the framework of the proposed dynamic programming based weighted method is provided. The paper defined the work stages of the proposed model. The algorithm for test sequence generation is also provided in this paper. The analysis results of the work are provided against the greedy and dynamic programming based methods. The results are derived for cost and the number of testcases processed by the method. The results show that the proposed model improved the performance effectively.

A Modified IEEE 118-Bus Test Case for Geomagnetic Disturbance Studies–Part I: Model Data

Power grid test cases for geomagnetic disturbance (GMD) studies need particular data that are not provided by the typical IEEE transmission benchmarks. This two-part paper proposes a test case that contains required modeling details for time-domain simulation of a GMD within an electromagnetic transient program (EMT-type). Compared to existing load-flow-based GMD test cases, the proposed test case offers advantages, such as accurate representation of nonlinear transformer magnetization and interaction between the dc geomagnetically-induced currents and transformer saturation, additional var consumption, harmonics, and voltage regulation problems. Part I presents the test case system and parameter data, and Part II provides simulation results to enable software-to-software validation (future work) as a first step toward the establishment of the model as an official GMD benchmark.

The Matsuno baroclinic wave test case

Abstract. The analytic wave solutions obtained by Matsuno (1966) in his seminal work on equatorial waves provide a simple and informative way of assessing the performance of atmospheric models by measuring the accuracy with which they simulate these waves. These solutions approximate the solutions of the shallow-water equations on the sphere for low gravity-wave speeds such as those of the baroclinic modes in the atmosphere. This is in contrast to the solutions of the non-divergent barotropic vorticity equation, used in the Rossby–Haurwitz test case, which are only accurate for high gravity-wave speeds such as those of the barotropic mode. The proposed test case assigns specific values to the wave parameters (gravity-wave speed, zonal wave number, meridional wave mode and wave amplitude) for both planetary and inertia-gravity waves, and suggests simple assessment criteria suitable for zonally propagating wave solutions. The test is successfully applied to a spherical shallow-water model in an equatorial channel and to a global-scale model. By adding a small perturbation to the initial fields it is demonstrated that the chosen initial waves remain stable for at least 100 wave periods. The proposed test case can also be used as a resolution convergence test.

Robust relative navigation for spacecraft rendezvous using differential drag

This work is concerned with the development of relative navigation filters processing relative position measurements of two satellites that perform rendezvous via differential drag only along low Earth orbits. The filters are developed in the Leader Hill frame with an emphasis on differential drag uncertainty modeling. Embedding the atmosphere density daily high variability in a bitopic uncertainty yields a novel robust H∞ filter of the six relative motion states. For comparison two Kalman filters matched to other atmosphere density models are developed. Furthermore the navigation are embedded in a realistic guidance navigation and attitude control and determination architecture (GNC/ADC) for nanosatellites. The proposed test case features two nanosatellites at 300 km altitude switching from positive to negative differential drag modes via 3D rigid body rotation. The 3D attitude control system involves three reactions wheels and three magnetorquers. The satellites inertial attitudes are estimated using three rate gyroscopes and coarse vector measurements. The proposed GNC/ADC scheme is tested in a high fidelity simulation environment. With 1 m relative position measurement accuracy and 10° attitude pointing accuracy the steady-state along track navigation errors are 4 m, 7 m, and 8 m (RMS) for the H∞ filter and the Kalman filters, respectively. The H∞ filter also shows quicker convergence and lesser sensitivity to the atmosphere density high variability. The two satellites succeed in closing an initial along track separation distance from 7500 m down to 250 m within 10 orbits, while the final radial deviation is about 10 m.

Test Case Minimization Applying Firefly Algorithm

The objective of this study is to propose a test case minimization method performed on UML statechart to produce test cases that are optimal while considering higher coverage criteria. Current test case generation techniques consume a large amount of time and cost with less testing coverage, while generating large number or test cases, what results in difficulties in detecting the faults and errors in the tested systems. Many approaches on test case minimization use genetic algorithms, model checking, or graph search algorithms to reduce the number of generated test cases, also the using of UML diagrams to test the system requirements and design before implementing it in the coding phase. However, these studies lack concentration in achieving higher coverage criteria and minimization in the generated test cases in the same time. The proposed test case minimization method has the following steps: provide weight to the paths, calculate path coverage for each path, transform an immediate graph into an adjacency matrix, which later is used to apply firefly algorithm and generate optimal test cases. A review on of the previous similar research in this domain has been presented and analysed to identify the issues and gaps in this domain. The steps use to perform the test case minimization have been presented together with some example and formula used. Findings from this study shows that this test case minimization has successful covered more types of test coverage which are all state, all transition, all transition pairs and all-one-loop paths. At the same time, it is capable of minimizing the number of test cases.

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.

Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm

For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our proposed technique test cases are optimized by utilizing Improved Cuckoo Algorithm (ICSA). At that point, the advanced experiments are organized or prioritized by utilizing Particle Swarm Optimization algorithm (PSO). The Particle Swarm Optimization and Improved Cuckoo Algorithm (PSOICSA) estimation is a blend of Improved Cuckoo Search Algorithm(ICSA) and Particle Swarm Optimization (PSO). PSOICSA could be utilized to advance the test suite, and coordinate both ICSA and PSO for a superior outcome, when contrasted with their individual execution as far as experiment improvement.

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.

Research of Reuse Technology of Test Case Based on Function Calling Path

In the process of software testing, it requires a lot of manpower and time to design test cases. Test cases of high similarities can be modified to reduce the workload of test case design, and improve the test efficiency. A test case reuse method based on function calling path is proposed. According to the change functions and the correlation functions it determined the changed path to be tested. It selected the function calling path which has high degree of similarity with the changed path to be tested from the original function calling path set. The collection of reusable test cases were determined according to mapping relationship between function calling path and test case, and the reusable test cases were modified to cover the change paths. Experimental results have validated the effectiveness of the proposed test case reuse method, and further reduce the workload of designing test cases and regression testing costs.