Research on the method and tool for automated generation of test data for CTCS-3 on-board equipment

2015 ◽  
pp. 421-425
Author(s):  
Yong Zhang ◽  
Qian Huang ◽  
Yuntao Liang
Keyword(s):  
Test Data ◽  
Automated Generation

AUTOMATED GENERATION OF TEST TRAJECTORIES FOR EMBEDDED FLIGHT CONTROL SYSTEMS

2002 ◽  
Vol 12 (02) ◽  
pp. 175-200 ◽  
Author(s):  
BOJAN CUKIC ◽  
BRIAN J. TAYLOR ◽  
HARSHINDER SINGH
Keyword(s):  
Linear Regression ◽  
Control Systems ◽  
Test Data ◽  
Flight Control ◽  
Performance Metrics ◽  
Large Set ◽  
Data Generation ◽  
Statistical Regression ◽  
Automated Generation ◽  
Independent Variables

Automated generation of test cases is a prerequisite for fast testing. Whereas the research in automated test data generation addressed the creation of individual test points, test trajectory generation has attracted limited attention. In simple terms, a test trajectory is defined as a series of data points, with each (possibly multidimensional) point relying upon the value(s) of previous point(s). Many embedded systems use data trajectories as inputs, including closed-loop process controllers, robotic manipulators, nuclear monitoring systems, and flight control systems. For these systems, testers can either handcraft test trajectories, use input trajectories from older versions of the system or, perhaps, collect test data in a high fidelity system simulator. While these are valid approaches, they are expensive and time-consuming, especially if the assessment goals require many tests. We developed a framework for expanding a small, conventionally developed set of test trajectories into a large set suitable, for example, for system safety assurance. Statistical regression is the core of this framework. The regression analysis builds a relationship between controllable independent variables and closely correlated dependent variables, which represent test trajectories. By perturbing the independent variables, new test trajectories are generated automatically. Our approach has been applied in the safety assessment of a fault tolerant flight control system. Linear regression, multiple linear regression, and autoregressive techniques are compared. The performance metrics include the speed of test generation and the percentage of "acceptable" trajectories, measured by the domain specific reasonableness checks.

On the Automated Generation of Program Test Data

1976 ◽  
Vol SE-2 (4) ◽  
pp. 293-300 ◽  
Author(s):  
C.V. Ramamoorthy ◽  
S.-B.F. Ho ◽  
W.T. Chen
Keyword(s):  
Test Data ◽  
Automated Generation ◽  
Program Test

scholarly journals Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population Diversity

2021 ◽  
Vol 11 (10) ◽  
pp. 4673
Author(s):  
Tatiana Avdeenko ◽  
Konstantin Serdyukov
Keyword(s):  
Test Data ◽  
Fitness Function ◽  
Population Diversity ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Intelligent Support ◽  
Automated Generation ◽  
Code Coverage ◽  
Automated Test Data Generation

In the present paper, we investigate an approach to intelligent support of the software white-box testing process based on an evolutionary paradigm. As a part of this approach, we solve the urgent problem of automated generation of the optimal set of test data that provides maximum statement coverage of the code when it is used in the testing process. We propose the formulation of a fitness function containing two terms, and, accordingly, two versions for implementing genetic algorithms (GA). The first term of the fitness function is responsible for the complexity of the code statements executed on the path generated by the current individual test case (current set of statements). The second term formulates the maximum possible difference between the current set of statements and the set of statements covered by the remaining test cases in the population. Using only the first term does not make it possible to obtain 100 percent statement coverage by generated test cases in one population, and therefore implies repeated launch of the GA with changed weights of the code statements which requires recompiling the code under the test. By using both terms of the proposed fitness function, we obtain maximum statement coverage and population diversity in one launch of the GA. Optimal relation between the two terms of fitness function was obtained for two very different programs under testing.

A New Method to Generate Test Cases by Genetic Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2547-2550
Author(s):  
Yu Liu ◽  
Feng Qin Wang ◽  
Qiu Feng Han
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
New Method ◽  
Test Cases ◽  
Evaluation Function ◽  
Matching Problem ◽  
Automated Generation

Because of the powerful capability of global searching and robustness of genetic algorithm, so it can be well applied in the automated generation of test data.Establish a module to generate test cases automatically by genetic algorithms.We can find that genetic algorithm guide the generation of the test data by the evaluation function according to the constraints of the path.It will not only avoid the blindness of the data searching process with higher efficiency,but also need not to consider the matching problem of the generated test data and test sequences.It can even run effectively relying only on the fitness of the point in the searching space supplied by the evaluation function.This advantage makes it a powerful searching algorithm on the type of random searching.

scholarly journals An Algorithm for Finding the Optimal Path in Basis Path Testing using GABVIE Model

2020 ◽  
Vol 9 (3) ◽  
pp. 587-593
Keyword(s):  
Test Data ◽  
Optimal Path ◽  
Minimum Cost ◽  
Program Code ◽  
Automated Generation ◽  
Current Test ◽  
Development Life Cycle ◽  
Path Coverage ◽  
Black Box Testing ◽  
White Box Testing

Software testing is one of the most vital factors in software development life cycle. It is mainly used for testing the program code, known as white box testing and to test the functionality of the program, known as black box testing. Manual generation of test data is very costly, error vulnerable and time consuming task. Subsequently, there is a need to make the process automated as could be expected under the circumstances. This paper presented the automated generation of optimal path with intention of attaining the maximum coverage. The work being done considers the optimal path coverage in minimum cost. The task of generating test cases can be done through the concept Genetic Algorithm with importance of variable (GABVIE). The proposed algorithm additionally considers programs having numerous modules. This is vital as a large portion of the current test data generators not succeeded to establish the communication between the modules. The approach has been implemented on various program code and the outcomes got have been confirmed. The proposed work considers the white box testing.

Research on Automated Analysis Approach for Tests of Hydro-Turbine Generator Sets

2013 ◽  
Vol 340 ◽  
pp. 3-7
Author(s):  
Guo Cai Xie ◽  
Zhao Hui Li
Keyword(s):  
Test Data ◽  
Automated Analysis ◽  
Analysis Approach ◽  
Automated Identification ◽  
Turbine Generator ◽  
Test Analysis ◽  
Automated Generation ◽  
Hydro Turbine ◽  
Test Items ◽  
Generator Sets

To liberate labors and enhance the reliability and accuracy of test analysis results, an automated analysis approach for tests of hydro-turbine generator sets was proposed. This approach was consisted of three procedures, that is, test items automated identification, test data automated record and analysis, and test reports automated generation. At first, the test items were automatically identified; then the test data was recorded and the performance indices were calculated automatically when the test items ended; at last, the test reports were automatically generated in accordance with the fixed contents and styles.

Test Validation Without Measurement

2016 ◽  
Vol 32 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Emilie Lacot ◽  
Mohammad H. Afzali ◽  
Stéphane Vautier
Keyword(s):  
Test Data ◽  
Test Scores ◽  
Scientific Explanation ◽  
Test Validation ◽  
Clinical Tests ◽  
Ethical Perspective ◽  
Power Of Test ◽  
Memory Accuracy ◽  
Social Uses ◽  
Special Case

Abstract. Test validation based on usual statistical analyses is paradoxical, as, from a falsificationist perspective, they do not test that test data are ordinal measurements, and, from the ethical perspective, they do not justify the use of test scores. This paper (i) proposes some basic definitions, where measurement is a special case of scientific explanation; starting from the examples of memory accuracy and suicidality as scored by two widely used clinical tests/questionnaires. Moreover, it shows (ii) how to elicit the logic of the observable test events underlying the test scores, and (iii) how the measurability of the target theoretical quantities – memory accuracy and suicidality – can and should be tested at the respondent scale as opposed to the scale of aggregates of respondents. (iv) Criterion-related validity is revisited to stress that invoking the explanative power of test data should draw attention on counterexamples instead of statistical summarization. (v) Finally, it is argued that the justification of the use of test scores in specific settings should be part of the test validation task, because, as tests specialists, psychologists are responsible for proposing their tests for social uses.

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