scholarly journals A Proposal for Automatic Testing of GUIs Based on Annotated Use Cases

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
Pedro Luis Mateo Navarro ◽  
Diego Sevilla Ruiz ◽  
Gregorio Martínez Pérez
Keyword(s):  
Automatic Testing ◽  
Use Cases ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Test Coverage ◽  
Initial Test ◽  
New Approach ◽  
Coverage Analysis

This paper presents a new approach to automatically generate GUI test cases and validation points from a set of annotated use cases. This technique helps to reduce the effort required in GUI modeling and test coverage analysis during the software testing process. The test case generation process described in this paper is initially guided by use cases describing the GUI behavior, recorded as a set of interactions with the GUI elements (e.g., widgets being clicked, data input, etc.). These use cases (modeled as a set of initial test cases) are annotated by the tester to indicate interesting variations in widget values (ranges, valid or invalid values) and validation rules with expected results. Once the use cases are annotated, this approach uses the new defined values and validation rules to automatically generate new test cases and validation points, easily expanding the test coverage. Also, the process allows narrowing the GUI model testing to precisely identify the set of GUI elements, interactions, and values the tester is interested in.

scholarly journals Bio Inspired Approach for Generating Test data from User Stories

2021 ◽  
Vol 12 (2) ◽  
pp. 412-419
Author(s):  
A.Tamizharasi , Et. al.
Keyword(s):  
Limited Resources ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Major Research ◽  
User Stories ◽  
Research Challenge ◽  
Agile Model ◽  
Business Requirements

In Agile model where the software prototypes are developed frequently and also rapidly, testing becomes more critical. Generating an effective Test case for complex system is a challenging task involved in software testing. The major research challenge in this area includes the test case generation with limited resources, identifying the essential functional requirement that plays a crucial role and automation of the test case generation process. To solve this issue, a hybridized bio inspired approach is proposed to generate test cases from the user stories which accepts the business requirements as input, processed using NLP and develop functional test cases from it. The proposed algorithm is compared with other existing algorithms and the experimental results proved that the proposed algorithm is more efficient in many cases.  

scholarly journals Forest Optimization based Test Case Generation for Multiple Paths with Metamorphic Relations

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Efficient Method ◽  
Optimization Algorithm ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Initial Test ◽  
Time Consumption ◽  
Path Coverage ◽  
Multiple Paths ◽  
Metaheuristic Techniques

In general multiple paths are covered by multiple runs which is a time consuming task. Now a days, metaheuristic techniques are widely used for path coverage. In order to reduce the time, an efficient method is proposed based on Forest Optimization Algorithm (FOA) with Metamorphic Relations (MRs) that cover multiple paths at a time in one run unlike the traditional search based testing. In the proposed approach, initial test case is generated using FOA, the successive test cases are generated using MRs without undergoing several runs. The motive of using FOA is that the searching mechanism of this algorithm having resemblance with the branch / path coverage techniques of testing. To the best of our knowledge, FOA has not been implemented in software testing. The experimental results are compared with three existing work. The efficiency of simply FOA is also shown how it able to cover multiple paths. The results show that FOA with MRs is more efficient in terms of time consumption and number of paths covered.

scholarly journals Model-Based Test Suite Generation Using Mutation Analysis for Fault Localization

2019 ◽  
Vol 9 (17) ◽  
pp. 3492 ◽  
Author(s):  
Choi ◽  
Lim
Keyword(s):  
Mutation Analysis ◽  
Fault Localization ◽  
Test Suite ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
State Chart ◽  
Generation Cost ◽  
Test Suite Generation

Fault localization techniques reduce the effort required when debugging software, as revealed by previous test cases. However, many test cases are required to reduce the number of candidate fault locations. To overcome this disadvantage, various methods were proposed to reduce fault-localization costs by prioritizing test cases. However, because a sufficient number of test cases is required for prioritization, the test-case generation cost remains high. This paper proposes a test-case generation method using a state chart to reduce the number of test suites required for fault localization, minimizing the test-case generation and execution times. The test-suite generation process features two phases: fault-detection test-case generation and fault localization in the test cases. Each phase uses mutation analysis to evaluate test cases; the results are employed to improve the test cases according to the objectives of each phase, using genetic algorithms. We provide useful guidelines for application of a search-based mutational method to a state chart; we show that the proposed method improves fault-localization performance in the test-suite generation phase.

Automatic test case generation from requirements for industrial cyber-physical systems

2016 ◽  
Vol 64 (3) ◽  
Author(s):  
Roopak Sinha ◽  
Cheng Pang ◽  
Gerardo Santillán Martínez ◽  
Valeriy Vyatkin
Keyword(s):  
Cyber Physical Systems ◽  
Automatic Testing ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Functional Requirements ◽  
Automatic Test ◽  
Physical Systems ◽  
Model Driven ◽  
Automatic Test Case Generation

AbstractIndustrial cyber-physical systems require complex software to orchestrate heterogeneous mechatronic components and control physical processes. This software is typically developed and refined iteratively in a model-driven fashion. Testing such multi-dimensional systems is extremely difficult as subsequent refinements may not correspond accurately with previous system models.We propose a framework to generate test-cases from functional requirements at all stages in the model-driven engineering process. A requirements ontology initially created during requirements engineering is iteratively refined such that test-cases can be generated automatically. An industrial water process system case study illustrates the strengths of the proposed formalism. We also present an automatic test-case generation and execution tool called REBATE (REquirements Based Automatic Testing Engine).

scholarly journals Test Case Generation Process using Soft Computing Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 4824-4831
Keyword(s):  
Software Testing ◽  
Soft Computing ◽  
Test Data ◽  
Important Task ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Soft Computing Techniques ◽  
Case Development

Software testing is the SDLC's important and most expensive step. Software testing is difficult and time-consuming work requiring a great deal of money for software development. Testing is both an operation that is static and adaptive. Software testing process deals with the creation of test cases, checking and validating either passed or failed test cases. It is unidealistic to check only the discerning parts of the material as a whole at once. It is not possible to test the whole system once, so selected parts of the code are considered for analysis. Since the input space of the Product Under Test (PUT) can be very large, it is important to analyze a representative subset of test cases. During software testing, the most important task is to build appropriate test cases. An effective set of test cases can detect more errors. Software testing always requires high deficiencies. Test cases are constructed using the test data. In the automation of software testing, the important task is to generate test data according to a given level of competence. The improved test data are determined using the test case development methodology and the test data adequacy criterion being applied. For increase the level of automation and performance, these aspects of test case development need to be studied. This paper studies the various test case generation techniques using soft computing techniques like Genetic Algorithm, Artificial Bee colony methods. Further an evaluation criterion for the test case generation process, empirical study of Code Coverage and its importance is discussed.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

scholarly journals Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation

2021 ◽  
Vol 11 (21) ◽  
pp. 10166
Author(s):  
Leonard Stepien ◽  
Silvia Thal ◽  
Roman Henze ◽  
Hiroki Nakamura ◽  
Jacobo Antona-Makoshi ◽  
...  
Keyword(s):  
Safety Evaluation ◽  
Driving Safety ◽  
Test Case ◽  
Test Cases ◽  
Generation Process ◽  
Complex Environments ◽  
Automated Driving ◽  
Evaluation Approach ◽  
Naturalistic Driving ◽  
Real Traffic

Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by applying heuristics to naturalistic driving data. The targeted requirements of the generated test cases are severity, exposure, and realism. The methodology starts with the extraction of scenarios from the data and their split in two subsets—containing the relatively more critical scenarios and, respectively, the normal driving scenarios. Each subset is analysed separately, in regard to the parameter value distributions and occurrence of dependencies. Subsequently, a heuristic search-based approach is applied to generate test cases. The resulting test cases clearly discriminate between safety critical and normal driving scenarios, with the latter covering a wider spectrum than the former. The verification of the generated test cases proves that the proposed methodology properly accounts for both severity and exposure in the test case generation process. Overall, the current study contributes to fill a gap concerning the specific applicable methodologies capable of accounting for both severity and exposure and calls for further research to prove its applicability in more complex environments and scenarios.

Proposal of Iterative Genetic Algorithm for Test Suite Generation

2021 ◽  
Vol 12 (1) ◽  
pp. 111-130
Author(s):  
Ankita Bansal ◽  
Abha Jain ◽  
Abhijeet Anand ◽  
Swatantra Annk
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Computation Complexity ◽  
Test Suite Generation ◽  
Adaptive Nature ◽  
Efficiency And Reliability

Huge and reputed software industries are expected to deliver quality products. However, industry suffers from a loss of approximately $500 billion due to shoddy software quality. The quality of the product in terms of its accuracy, efficiency, and reliability can be revamped through testing by focusing attention on testing the product through effective test case generation and prioritization. The authors have proposed a test-case generation technique based on iterative listener genetic algorithm that generates test cases automatically. The proposed technique uses its adaptive nature and solves the issues like redundant test cases, inefficient test coverage percentage, high execution time, and increased computation complexity by maintaining the diversity of the population which will decrease the redundancy in test cases. The performance of the technique is compared with four existing test-case generation algorithms in terms of computational complexity, execution time, coverage, and it is observed that the proposed technique outperformed.

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