Set Of Test Cases Research Articles

SOrTES: A Supportive Tool for Stochastic Scheduling of Manual Integration Test Cases

The main goal of software testing is to detect as many hidden bugs as possible in the final software product before release. Generally, a software product is tested by executing a set of test cases, which can be performed manually or automatically. The number of test cases which are required to test a software product depends on several parameters such as the product type, size, and complexity. Executing all test cases with no particular order can lead to waste of time and resources. Test optimization can provide a partial solution for saving time and resources which can lead to the final software product being released earlier. In this regard, test case selection, prioritization, and scheduling can be considered as possible solutions for test optimization. Most of the companies do not provide direct support for ranking test cases on their own servers. In this paper, we introduce, apply, and evaluate sOrTES as our decision support system for manual integration of test scheduling. sOrTES is a Python-based supportive tool which schedules manual integration test cases which are written in a natural language text. The feasibility of sOrTES is studied by an empirical evaluation which has been performed on a railway use-case at Bombardier Transportation, Sweden. The empirical evaluation indicates that around 40 % of testing failure can be avoided by using the proposed execution schedules by sOrTES, which leads to an increase in the requirements coverage of up to 9.6%.

Investigating the applicability of generating test cases for web applications based on traditional graph coverage

Web applications provide services to hundreds of millions of people throughout the world. However, developers face a range of problems and challenges in testing them, including the fact that web applications run on diverse and heterogeneous platforms and are written using diverse programming languages. Moreover, they can be dynamic, with their contents and structures determined by inputs from users, so they need to be tested to ensure their validity. In this paper, we investigate the ability to generate a set of test cases for web applications based on traditional graph coverage criteria. First, we extracted the in-link and out-link from given web applications in order to draw a web graph, before extracting the prime paths from the graph. After that, the invalid transitions were built from the prime paths. Finally, all the invalid transitions were extended with valid transitions. We evaluated our investigation process by using different sizes of web applications. Two cases studies were used in this paper, the first a small size application and the second a medium size. The results show how difficult it is to run a huge number of test cases generated manually using graph coverage criteria, even for a small web application.

Investigating the applicability of generating test cases for web applications based on traditional graph coverage

Web applications provide services to hundreds of millions of people throughout the world. However, developers face a range of problems and challenges in testing them, including the fact that web applications run on diverse and heterogeneous platforms and are written using diverse programming languages. Moreover, they can be dynamic, with their contents and structures determined by inputs from users, so they need to be tested to ensure their validity. In this paper, we investigate the ability to generate a set of test cases for web applications based on traditional graph coverage criteria. First, we extracted the in-link and out-link from given web applications in order to draw a web graph, before extracting the prime paths from the graph. After that, the invalid transitions were built from the prime paths. Finally, all the invalid transitions were extended with valid transitions. We evaluated our investigation process by using different sizes of web applications. Two cases studies were used in this paper, the first a small size application and the second a medium size. The results show how difficult it is to run a huge number of test cases generated manually using graph coverage criteria, even for a small web application.

Feedback Driven Grammar-Based Fuzzing

In this paper, we present a method for grammar-based fuzzing, which improves its penetration power. It is based on input data generation using a fuzzer feedback. Several other methods are prone to create an initial set of acceptable test cases before the actual fuzzing process, and hence are unable to use the runtime information to increase the generated input’s quality. The proposed method uses the coverage information gathered for each input sample and guides grammar-based input generation. This method uses more than 120 BNF (Backus-Naur Form) grammar rules described in ANTLR (Another Tool for Language Recognition) platform. Experimental results show that our method - feedback driven random test generation, has higher code coverage capabilities compared with the existing methods.

Modelling bubble induced turbulence for gas-liquid bubbly flow in a vertical pipe

A two-fluid model in the Eulerian-Eulerian framework has been implemented for the turbulent gas-liquid bubbly upward flow in a vertical pipe. The transport equations, i.e. Reynolds-Averaged Navier-Stokes equations for the two-fluid model, are discretized using the finite volume method. The effect of the disperse gas phase on the liquid phase turbulence, referred to as turbulence modulation, was accounted for through source terms in the transport equations for a low Reynolds number k-ε turbulence closure. The model was used to predict the turbulence kinetic energy and its dissipation rate, mean phasic velocities and volume fraction distribution for a set of test cases selected from the available literature. The focus of the present study was on assessing the model predictions using measurements of the turbulence kinetic energy of the liquid phase. A relatively novel aspect of the present analysis is the use of the budgets of the transport equations for the turbulence kinetic energy and dissipation rate to assess the effect of the turbulence modulation on the liquid turbulence field. Although the present model formulation is shown to adequately predict the effect of bubbles in some flows and perform better than some of the other model formulations, the overall conclusion is that the present approaches for incorporating turbulence modulation are at best partially successful and still need further development.

Multiphysics Modeling for Transient Analysis of Gas-Insulated Lines

In this paper, two discretization techniques are combined to present a coupled electro-thermal model for the transient analysis of gas-insulated transmission lines (GILs). The analysis of the thermal problem is based on the volume-element-method, representing heat transfer equations on a 2-D axisymmetric GIL model and determining temperature distribution over space and time. For the electrical formulation, the finite-difference time-domain method is adopted considering constant parameters calculated at the frequency of interest. The coupled electro-thermal problem in the case of transient conditions is solved by means of a bidirectional procedure. A set of test cases is imposed on the electric part, and transient analysis is conducted while considering the line coupled electro-thermal performance.

Test Case Optimization and Redundancy Reduction Using GA and Neural Networks

More than 50% of software development effort is spent in testing phase in a typical software development project. Test case design as well as execution consume a lot of time. Hence, automated generation of test cases is highly required. Here a novel testing methodology is being presented to test object-oriented software based on UML state chart diagrams. In this approach, function minimization technique is being applied and generate test cases automatically from UML state chart diagrams. Software testing forms an integral part of the software development life cycle. Since the objective of testing is to ensure the conformity of an application to its specification, a test “oracle” is needed to determine whether a given test case exposes a fault or not. An automated oracle to support the activities of human testers can reduce the actual cost of the testing process and the related maintenance costs. In this paper, a new concept is being presented using an UML state chart diagram and tables for the test case generation, artificial neural network as an optimization tool for reducing the redundancy in the test case generated using the genetic algorithm. A neural network is trained by the back-propagation algorithm on a set of test cases applied to the original version of the system.

A New Well-Balanced Reconstruction Technique for the Numerical Simulation of Shallow Water Flows with Wet/Dry Fronts and Complex Topography

This study develops a new well-balanced scheme for the one-dimensional shallow water system over irregular bed topographies with wet/dry fronts, in a Godunov-type finite volume framework. A new reconstruction technique that includes flooded cells and partially flooded cells and preserves the non-negative values of water depth is proposed. For the wet cell, a modified revised surface gradient method is presented assuming that the bed topography is irregular in the cell. For the case that the cell is partially flooded, this paper proposes a special reconstruction of flow variables that assumes that the bottom function is linear in the cell. The Harten–Lax–van Leer approximate Riemann solver is applied to evaluate the flux at cell faces. The numerical results show good agreement with analytical solutions to a set of test cases and experimental results.

Exploring output-based coverage for testing PHP web applications

In software testing, different testers focus on different aspects of the software such as functionality, performance, design, and other attributes. While many tools and coverage metrics exist to support testers at the code level, not much support is targeted for testers who want to inspect the output of a program such as a dynamic web application. To support this category of testers, we propose a family of output-coverage metrics (similar to statement, branch, and path coverage metrics on code) that measure how much of the possible output has been produced by a test suite and what parts of the output are still uncovered. To do that, we first approximate the output universe using our existing symbolic execution technique. Then, given a set of test cases, we map the produced outputs onto the output universe to identify the covered and uncovered parts and compute output-coverage metrics. In our empirical evaluation on seven real-world PHP web applications, we show that selecting test cases by output coverage is more effective at identifying presentation faults such as HTML validation errors and spelling errors than selecting test cases by traditional code coverage. In addition, to help testers understand output coverage and augment test cases, we also develop a tool called WebTest that displays the output universe in one single web page and allows testers to visually explore covered and uncovered parts of the output.

Conforming, non-conforming and non-matching discretization couplings in discrete fracture network simulations

Simulations of fluid flow in naturally fractured rocks have implications for several subsurface applications, including energy storage and extraction, and waste disposal. We are interested in flow in discrete fracture networks, which explicitly represent flow in fracture surfaces, but ignore the impact of the surrounding host rock. Fracture networks, generated from observations or stochastic simulations, will contain intersections of arbitrary length, and intersection lines can further cross, forming a highly complex geometry. As the flow exchange between fractures, thus in the network, takes place in these intersections, an adequate representation of the geometry is critical for simulation accuracy. In practice, the intersection dynamics must be handled by a combination of the simulation grid, which may or may not resolve the intersection lines, and the numerical methods applied on the grid. In this work, we review different classes of numerical approaches proposed in recent years, covering both methods that conform to the grid, and non-matching cases. Specific methods considered herein include finite elements, mixed and virtual elements and control volume methods. We expose our methods to an extensive set of test cases, ranging from artificial geometries designed to test difficult configurations, to a network extruded from a real fracture outcrop. The main outcome is guidances for choice of simulation models and numerical discretization with a trade off on the computational cost and solution accuracy.

A Comparison between Two Reduction Strategies for Shrouded Bladed Disks

Shrouded bladed disks exhibit a nonlinear dynamic behavior due to the contact interfaces at shrouds between neighboring blades. As a result, reduced order models (ROMs) are mandatory to compute the response levels during the design phase for high cycle fatigue (HCF) life assessment. In this paper, two reduction strategies for shrouded bladed disk reduction are presented. Both approaches rely on: (i) the cyclic symmetry of the linear bladed disk with open shrouds to perform only single sector calculations, (ii) the Craig–Bampton (CB) method to reduce the number of physical degrees of freedom (dofs). The two approaches are applied to a set of test cases in order to evaluate and compare their accuracy and the associated computational effort. Although both approaches allow for generating accurate ROMs, it is found that the numerical efficiency of the two methods depends on the ratio of the number of nodes at the inter-sector interfaces over the number of inner nodes of the elementary sector model.

Fuel relocation recovery implementation in Bison

During the initial rise to power in light water reactors (LWR), thermal gradients within a pellet cause radial and axial cracks to form in the fuel. The effect of these cracks is to reduce the pellet-cladding gap and accelerate the interaction between the fuel and cladding. This process is known as fuel relocation and may also include contributions from pellet eccentricity and cladding ovality. Since the cladding experiences both elevated temperatures and high external pressure due to the coolant, the cladding typically creeps inward further reducing the pellet-cladding gap. Once the gap is closed and pellet cladding mechanical interaction (PCMI) begins, both the thermal and mechanical behavior of the fuel is affected. The compressive forces exerted on the fuel due to contact with the cladding cause the fractured fuel sections to move back toward their original position which is termed relocation recovery. A model for this phenomena is implemented in the Bison fuel performance code and applied to a set of validation test cases. The predicted fuel rod diameter is compared to experimental measurements to evaluate the influence of relocation recovery over a range of operating conditions.

Modelling of Ship’s Trajectory Planning in Collision Situations by Hybrid Genetic Algorithm

Abstract Ship collision-avoidance trajectory planning aims at searching for a theoretical safe-critical trajectory in accordance with COLREGs and good seamanship. In this paper, a novel optimal trajectory planning based on hybrid genetic algorithm is presented for ship collision avoidance in the open sea. The proposed formulation is established based on the theory of the Multiple Genetic Algorithm (MPGA) and Nonlinear Programming, which not only overcomes the inherent deficiency of the Genetic Algorithm (GA) for premature convergence, but also guarantees the practicality and consistency of the optimal trajectory. Meanwhile, the encounter type as well as the obligation of collision avoidance is determined according to COLREGs, which is then considered as the restricted condition for the operation of population initialization. Finally, this trajectory planning model is evaluated with a set of test cases simulating various traffic scenarios to demonstrate the feasibility and superiority of the optimal trajectory.

Search‐based software engineering for constructing covering arrays

Search-based software engineering involves the application of optimisation methods to solve software engineering problems. One of the most significant difficulties in testing software systems is the effort needed to build the test suites required to validate a software system, which efficiently exposes faults. Given the importance of the software testing stage, a specific sub-area known as search-based software testing has become relevant in recent years. In this work, a search-based software testing algorithm for constructing covering arrays is proposed. A covering array is a combinatorial structure that can be used as a set of test cases. By utilising this algorithm, the authors reduce the size of 893 test suites.

Systematic evaluation of Business Process Management Systems

Selecting a Business Process Management Systems (BPMS) for an organization requires a thorough evaluation of its capabilities considering the whole support of the business process lifecycle and the organizational environment in which the BPMS will be used. In a previous work, we have proposed a methodology for the systematic evaluation of BPMS, ensuring the quality of the results and the repeatability of the evaluation process. The methodology envisions a quantitative and qualitative evaluation regarding the fulfillment of key features that BPMS must provide in the context of a given organization. However, it was mostly focused on required functional and non-technical aspects. In this paper, we present the extension of our methodology with a detailed definition of non-functional aspects to be evaluated, a set of test cases for their evaluation, and the development of a case study as a complimentary qualitative evaluation. We also performed a fine tuning of the methodology based on a comprehensive comparison with other existent methodologies and the provision of tool support. To illustrate the approach, we present results from the evaluation of open source and proprietary BPMS which constitute both a validation and assessment of our proposal and a contribution to knowledge regarding the capacities of selected BPMS technologies.

A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing.It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algorithm like Particle Swarm Optimization (PSO) solves optimization problem of automated combinatorial test case generation. Many authors have contributed in the field of combinatorial test case generation using PSO algorithms.We have reviewed some important research papers on automated test case generation for combinatorial testing using PSO. This paper provides a critical review of use of PSO and its variants for solving the classical optimization problem of automatic test case generation for conducting combinatorial testing.

Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GA

This article describes about the application of search-based techniques in regression testing and compares the performance of various search-based techniques for software testing. Test cases tend to increase exponentially as the software is modified. It is essential to remove redundant test cases from the existing test suite. Regression testing is very costly and must be performed in restricted ways to ensure the validity of the existing software. There exist different methods to improve the quality of test cases in terms of the number of faults covered, opposed to the number of statements covered in a minimum time. Different methods exist for this purpose, such as minimization, test case selection, and test case prioritization. In this article, search-based methods are applied to improve the quality of the test suite in order to select a minimum set of test cases which covers all the statements in a minimum time. The whole approach is named search based regression testing. In this paper, the performance of different metaheuristics for test suite minimization problem is also compared with a hybrid approach of ant colony optimization algorithm and genetic algorithm.

A Data Model for Using OpenStreetMap to Integrate Indoor and Outdoor Route Planning.

With a rapidly-growing volume of volunteered geographic information (VGI), there is an increasing trend towards using VGI to provide location-based services. In this study, we investigate using OpenStreetMap data to integrate indoor and outdoor route planning for pedestrians. To support indoor and outdoor route planning, in this paper, we focus on the connections inside buildings and propose a data model, which uses OSM primitives (nodes, ways and relations) and tags to capture horizontal and vertical indoor components, as well as the connection between indoor and outdoor environments. A set of new approaches is developed to support indoor modeling and mapping. Based on the proposed data model, we present a workflow that enables automatic generation of a routing graph and provide an algorithm to calculate integrated indoor-outdoor routes. We applied our data model to a set of test cases. The application results demonstrate the capability of our data model in modeling built environments and its feasibility for the integration of indoor and outdoor navigation.

Constructing Cost-Aware Functional Test-Suites Using Nested Differential Evolution Algorithm

Combinatorial testing can test software that has various configurations for multiple parameters efficiently. This method is based on a set of test cases that guarantee a certain level of interaction among parameters. Mixed covering array (MCA) can be used to represent a test-suite. Each row of the array corresponds to a test case. In general, a smaller size of MCA does not necessarily imply less testing time. There are certain combinations of parameter values which would take much longer time than other cases. Based on this observation, it is more valuable to construct MCAs that are better in terms of testing effort characterization other than size. We present a method to find cost-aware MCAs. The method contains two steps. First, simulated annealing algorithm is used to get an MCA with a small size. Then we propose a novel nested differential evolution algorithm to improve the solution with its testing effort. The experimental results indicate that our method succeeds in constructing cost-aware MCAs for real-world applications. The testing effort is significantly reduced compared with representative state-of-the-art algorithms.

A New Algorithm for Parallel Connected-Component Labelling on GPUs

Connected-component labelling remains an important and widely-used technique for processing and analysing images and other forms of data in various application areas. Different data sources produce components with different structural features and may be more or less suited to certain connected-component labelling algorithms. Although many efficient serial algorithms exist, determining connected-components on Graphical Processing Units (GPUs) is of interest as many applications use GPUs for processing other parts of the application and labelling on the GPU can avoid expensive memory transfers. The general problem of connected-component labelling is discussed and two existing GPU-based algorithms are discussed—label-equivalence and Komura-equivalence. A new GPU-based, parallel component-labelling algorithm is presented that identifies and eliminates redundant operations in the previous methods for rectilinear two- and three-dimensional datasets. A set of test-cases with a range of structural features and systems sizes is presented and used to evaluate the new labelling algorithm on modern NVIDIA GPU devices and compare it to existing algorithms. The results of the performance evaluation are presented and show that the new algorithm can provide a meaningful performance improvement over previous methods across a range of test cases.