About: Test case is a research topic. Over the lifetime, 13080 publications have been published within this topic receiving 215534 citations. The topic is also known as: testcase & test.
Papers published on a yearly basis
TL;DR: The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces.
Abstract: This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .
TL;DR: This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research.
Abstract: Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks to identify the test cases that are relevant to some set of recent changes. Test case prioritization seeks to order test cases in such a way that early fault detection is maximized. This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research. Copyright © 2010 John Wiley & Sons, Ltd.
28 Oct 1999
TL;DR: This book discusses how to develop a Decision Table for Object-oriented Testing, and a Tester's Guide to the UML, and some Assertion Tools for Post-development Testing.
Abstract: List of Figures. List of Tables. List of Procedures. Foreword. Preface. Acknowledgments. I. PRELIMINARIES. 1. A Small Challenge. 2. How to Use This Book. Reader Guidance. Conventions. FAQs for Object-oriented Testing. Test Process. 3. Testing: A Brief Introduction. What Is Software Testing? Definitions. The Limits of Testing. What Can Testing Accomplish? Bibliographic Notes. 4. With the Necessary Changes: Testing and Object-oriented Software. The Dismal Science of Software Testing. Side Effects of the Paradigm. Language-specific Hazards. Coverage Models for Object-oriented Testing. An OO Testing Manifesto. Bibliographic Notes. II. MODELS. 5. Test Models. Test Design and Test Models. Bibliographic Notes. 6. Combinational Models. How Combinational Models Support Testing. How to Develop a Decision Table. Deriving the Logic Function. Decision Table Validation. Test Generation. Choosing a Combinational Test Strategy. Bibliographic Notes. 7. State Machines. Motivation. The Basic Model. The FREE State Model. State-based Test Design. Bibliographic Notes. 8. A Tester's Guide to the UML. Introduction. General-purpose Elements. Use Case Diagram. Class Diagram. Sequence Diagram. Activity Diagram. Statechart Diagram. Collaboration Diagram. Component Diagram. Deployment Diagram. Graphs, Relations, and Testing. Bibliographic Notes. III. PATTERNS. 9. Results-oriented Test Strategy. Results-oriented Testing. Test Design Patterns. Test Design Template. Documenting Test Cases, Suites, and Plans. Bibliographic Notes. 10. Classes. Class Test and Integration. Preliminaries. Method Scope Test Design Patterns. Category-Partition. Combinational Function Test. Recursive Function Test. Polymorphic Message Test. Class Scope Test Design Patterns. Invariant Boundaries. Nonmodal Class Test. Quasi-modal Class Test. Modal Class Test. Flattened Class Scope Test Design Patterns. Polymorphic Server Test. Modal Hierarchy Test. Bibliographic Notes. 11. Reusable Components. Testing and Reuse. Test Design Patterns. Abstract Class Test. Generic Class Test. New Framework Test. Popular Framework Test. Bibliographic Notes. 12. Subsystems. Subsystems. Subsystem Test Design Patterns. Class Association Test. Round-trip Scenario Test. Controlled Exception Test. Mode Machine Test. Bibliographic Notes. 13. Integration. Integration in Object-oriented Development. Integration Patterns. Subsystem/System Scope. Big Bang Integration. Bottom-up Integration. Top-down Integration. Collaboration Integration. Backbone Integration. Layer Integration. Client/Server Integration. Distributed Services Integration. High-frequency Integration. Bibliographic Notes. 14. Application Systems. Testing Application Systems. Test Design Patterns. Extended Use Case Test. Covered in CRUD. Allocate Tests by Profile. Implementation-specific Capabilities. Post-development Testing. Note on Testing Performance Objectives. Bibliographic Notes. 15. Regression Testing. Preliminaries. Test Patterns. Retest All. Retest Risky Use Cases. Retest by Profile. Retest Changed Code. Retest Within Firewall. Bibliographic Notes. IV. TOOLS. 16. Test Automation. Why Testing Must Be Automated. Limitations and Caveats. 17. Assertions. Introduction. Implementation-based Assertions. Responsibility-based Assertions. Implementation. The Percolation Pattern. Deployment. Limitations and Caveats. Some Assertion Tools. Bibliographic Notes. 18. Oracles. Introduction. Oracle Patterns. Comparators. Bibliographic Notes. 19. Test Harness Design. How to Develop a Test Harness. Test Case Patterns. Test Case/Test Suite Method. Test Case/Test Suite Class. Catch All Exceptions. Test Control Patterns. Server Stub. Server Proxy. Driver Patterns. TestDriver Superclass. Percolate the Object Under Test. Symmetric Driver. Subclass Driver. Private Access Driver. Test Control Interface. Drone. Built-in Test Driver. Test Execution Patterns. Command Line Test Bundle. Incremental Testing Framework. Fresh Objects. A Test Implementation Syntax. Bibliographic Notes. Appendix. BigFoot's Tootsie: A Case Study. Requirements. OOA/D for Capability-driven Testing. Implementation. Glossary. References. Index. 0201809389T04062001
TL;DR: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal as discussed by the authors, such as rate of fault detection, a measure of how quickly faults are detected within the testing process.
Abstract: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection, a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing, the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. We describe several techniques for using test execution information to prioritize test cases for regression testing, including: 1) techniques that order test cases based on their total coverage of code components; 2) techniques that order test cases based on their coverage of code components not previously covered; and 3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites.
TL;DR: Comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables, and the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code.
Abstract: In this paper we present the R package deSolve to solve initial value problems (IVP) written as ordinary differential equations (ODE), differential algebraic equations (DAE) of index 0 or 1 and partial differential equations (PDE), the latter solved using the method of lines approach. The differential equations can be represented in R code or as compiled code. In the latter case, R is used as a tool to trigger the integration and post-process the results, which facilitates model development and application, whilst the compiled code significantly increases simulation speed. The methods implemented are efficient, robust, and well documented public-domain Fortran routines. They include four integrators from the ODEPACK package (LSODE, LSODES, LSODA, LSODAR), DVODE and DASPK2.0. In addition, a suite of Runge-Kutta integrators and special-purpose solvers to efficiently integrate 1-, 2- and 3-dimensional partial differential equations are available. The routines solve both stiff and non-stiff systems, and include many options, e.g., to deal in an efficient way with the sparsity of the Jacobian matrix, or finding the root of equations. In this article, our objectives are threefold: (1) to demonstrate the potential of using R for dynamic modeling, (2) to highlight typical uses of the different methods implemented and (3) to compare the performance of models specified in R code and in compiled code for a number of test cases. These comparisons demonstrate that, if the use of loops is avoided, R code can efficiently integrate problems comprising several thousands of state variables. Nevertheless, the same problem may be solved from 2 to more than 50 times faster by using compiled code compared to an implementation using only R code. Still, amongst the benefits of R are a more flexible and interactive implementation, better readability of the code, and access to R’s high-level procedures. deSolve is the successor of package odesolve which will be deprecated in the future; it is free software and distributed under the GNU General Public License, as part of the R software project.
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