scholarly journals AN INTEGRATED CLASSIFICATION-TREE METHODOLOGY FOR TEST CASE GENERATION

2000 ◽  
Vol 10 (06) ◽  
pp. 647-679 ◽  
Author(s):  
T. Y. CHEN ◽  
P. L. POON ◽  
T. H. TSE
Keyword(s):  
Classification Tree ◽  
Prototype System ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Generation System ◽  
Integrated Methodology ◽  
Tree Construction ◽  
Automated Test Data Generation ◽  
Tree Method

This paper describes an integrated methodology for the construction of test cases from functional specifications using the classification-tree method. It is an integration of our extensions to the classification-hierarchy table, the classification tree construction algorithm, and the classification tree restructuring technique. Based on the methodology, a prototype system ADDICT, which stands for AutomateD test Data generation system using the Integrated Classification-Tree method, has been built.

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.

Software test data generation technology based on polymorphic particle swarm evolutionary algorithm

2021 ◽  
pp. 1-13
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Test Case Generation ◽  
Particle Swarm Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Learning Factor ◽  
Swarm Algorithm

Combinatorial testing is a statute-based software testing method that aims to select a small number of valid test cases from a large combinatorial space of software under test to generate a set of test cases with high coverage and strong error debunking ability. However, combinatorial test case generation is an NP-hard problem that requires solving the combinatorial problem in polynomial time, so a meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, the particle swarm algorithm is more competitive in terms of coverage table generation scale and execution time. In this paper, we systematically review and summarize the existing research results on generating combinatorial test case sets using particle swarm algorithm, and propose a combinatorial test case generation method that can handle arbitrary coverage strengths by combining the improved one-test-at-a-time strategy and the adaptive particle swarm algorithm for the variable strength combinatorial test problem and the parameter selection problem of the particle swarm algorithm. To address the parameter configuration problem of the particle swarm algorithm, the four parameters of inertia weight, learning factor, population size and iteration number are reasonably set, which makes the particle swarm algorithm more suitable for the generation of coverage tables. For the inertia weights.

Key Problem of Component-Based Software Development

2014 ◽  
Vol 687-691 ◽  
pp. 1896-1899
Author(s):  
Ya Ping Cui
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Dynamic Test ◽  
Test Point ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Rate Of Increase ◽  
Coding Method ◽  
Path Coverage

In order to improve the component dynamic test efficiency, this paper proposes a keating component built-in test case generation method of genetic algorithm and designs the chromosome coding method. The test point and keating component facet description of dynamic test data generation method. Mass in order to improve the generation of test cases and add Yang the convergence speed of genetic algorithm. We improve the algorithm of the method for calculating the fitness function and fitness function not only consider the case of path coverage, but also considers the path coverage rate of increase, thus effectively improved the path coverage and reduce the Yang cases produce cost.

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