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.

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.

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 On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1145 ◽  
Author(s):  
Shweta Rani ◽  
Bharti Suri ◽  
Rinkaj Goyal
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Test Execution ◽  
Mutation Strategy

Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

scholarly journals Bi-Objective Constraint and Hybrid Optimizer for the Test Case Prioritization

2019 ◽  
Vol 8 (6) ◽  
pp. 3436-3448
Keyword(s):  
Taylor Series ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Optimal Test ◽  
Average Percentage ◽  
Branch Coverage

Regression testing is performed to make conformity that any changes in software program do not disturb the existing characteristics of the software. As the software improves, the test case tends to grow in size that makes it very costly to be executed, and thus the test cases are needed to be prioritized to select the effective test cases for software testing. In this paper, a test case prioritization technique in regression testing is proposed using a novel optimization algorithm known as Taylor series-based Jaya Optimization Algorithm (Taylor-JOA), which is the integration of Taylor series in Jaya Optimization Algorithm (JOA). The optimal test cases are selected based on the fitness function, modelled depending on the constraints, namely fault detection and branch coverage. The experimentation of the proposed Taylor-JOA is performed with the consideration of the evaluation metrics, namely Average Percentage of Fault Detected (APFD) and the Average Percentage of Branch Coverage (APBC). The APFD and the APBC of the proposed Taylor-JOA is 0.995, and 0.9917, respectively, which is high as compared to the existing methods that show the effectiveness of the proposed Taylor-JOA in the task of test case prioritization

scholarly journals Software Test Data Generation based on Path Testing using Genetic Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4466-4473
Keyword(s):  
Genetic Algorithms ◽  
Test Data ◽  
Iterative Algorithms ◽  
Continuous Functions ◽  
Software Test ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Optimal Solutions ◽  
Automated Test

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. Genetic Algorithms, one of the automation techniques, are iterative algorithms and apply basic operations repeatedly in greed for optimal solutions or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to optimal solutions. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG-GA (Test Data Generation using Genetic Algorithms) that searches for test data in a discontinuous space. The goal of the work is to analyze the effectiveness of Genetic Algorithms in automated test data generation and to compare its performance over random sampling particularly for discontinuous spaces.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Sign in / Sign up

Export Citation Format

Share Document