scholarly journals Multiple-Searching Genetic Algorithm for Whole Test Suites

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 2011
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Multicast Routing ◽  
Test Suite ◽  
Test Cases ◽  
Coverage Criteria ◽  
Mutation Score ◽  
Problem Instances ◽  
Test Suite Generation ◽  
Test Suites

A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

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.

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.

Study of Test Cases Prioritization Based on Ant Colony Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2168-2172
Author(s):  
Lu Lu Chen ◽  
Ling Zhang
Keyword(s):  
Ant Colony Algorithm ◽  
Regression Testing ◽  
Time Limit ◽  
Ant Colony ◽  
Test Suite ◽  
Test Cases ◽  
Optimal Sequence ◽  
Effectiveness And Efficiency ◽  
Important Activity

Regression testing is an important activity to ensure the quality of software. In order to improve the efficiency of regression testing, in this paper, the author proposes to reorder test suite based on ant colony algorithm in regression testing, and compare the result with other common sort results. Through experiment, it shows that the method can get the optimal sequence of test cases under the time limit and it has been proven a superior method in both effectiveness and efficiency for test cases prioritization.

scholarly journals Efficient increasing of the mutation score during model-based test suite generation

2020 ◽  
pp. 331-340
Author(s):  
A. Kolchin ◽  
◽  
S. Potiyenko ◽  
T. Weigert ◽  
◽  
...  
Keyword(s):  
Test Suite ◽  
Observable Effect ◽  
Test Set ◽  
Efficient Search ◽  
Strong Case ◽  
Model Based ◽  
Mutation Score ◽  
Test Suite Generation ◽  
Test Scenarios

The purpose of the method is to increase the sensitivity of an automatically generated test suite to mutations of a model. Unlike existing methods for generating test scenarios that use the mutational approach to assess the resulting test set, the proposed method analyzes the possibility of detecting mutations on the fly, in the process of analyzing the model’s behavior space, by adding of special coverage goals. Two types of mutants manifestation are considered: deviations in the behavior of paths for (weak case) and in the observed output (strong case). A new algorithm is proposed for efficient search of a path with observable effect of a mutation.

scholarly journals Quantum­Inspired Genetic Algorithm for Solving the Test Suite Minimization Problem

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Genetic Algorithm ◽  
Minimization Problem ◽  
Test Suite ◽  
Test Cases ◽  
Special Importance ◽  
Quantum Superposition ◽  
Complete Problem ◽  
Minimal Subset ◽  
Np Complete ◽  
Test Requirements

Test Suite Minimization problem is a nondeterministic polynomial time (NP) complete problem insoftware engineering that has a special importance in software testing. In this problem, a subset with a minimalsize that contains a number of test cases that cover all the test requirements should be found. A brute­forceapproach to solving this problem is to assume a size for the minimal subset and then search to find if there is asubset of test cases with the assumed size that solves the problem. If not, the assumed minimal size is graduallyincremented, and the search is repeated. In this paper, a quantum­inspired genetic algorithm (QIGA) will beproposed to solve this problem. In it, quantum superposition, quantum rotation and quantum measurement willbe used in an evolutionary algorithm. The paper will show that the adopted quantum techniques can speed upthe convergence of the classical genetic algorithm. The proposed method has an advantage in that it reduces theassumed minimal number of test cases using quantum measurements, which makes it able to discover the minimalnumber of test cases without any prior assumptions.

scholarly journals K-Step Crossover Method based on Genetic Algorithm for Test Suite Prioritization in Regression Testing

2021 ◽  
Vol 27 (2) ◽  
pp. 170-189
Author(s):  
P. K. Gupta
Keyword(s):  
Genetic Algorithm ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Average Percentage ◽  
Code Coverage ◽  
Test Case Selection ◽  
Case Selection

Software is an integration of numerous programming modules  (e.g., functions, procedures, legacy system, reusable components, etc.) tested and combined to build the entire module. However, some undesired faults may occur due to a change in modules while performing validation and verification. Retesting of entire software is a costly affair in terms of money and time. Therefore, to avoid retesting of entire software, regression testing is performed. In regression testing, an earlier created test suite is used to retest the software system's modified module. Regression Testing works in three manners; minimizing test cases, selecting test cases, and prioritizing test cases. In this paper, a two-phase algorithm has been proposed that considers test case selection and test case prioritization technique for performing regression testing on several modules ranging from a smaller line of codes to huge line codes of procedural language. A textual based differencing algorithm has been implemented for test case selection. Program statements modified between two modules are used for textual differencing and utilized to identify test cases that affect modified program statements. In the next step, test case prioritization is implemented by applying the Genetic Algorithm for code/condition coverage. Genetic operators: Crossover and Mutation have been applied over the initial population (i.e. test cases), taking code/condition coverage as fitness criterion to provide a prioritized test suite. Prioritization algorithm can be applied over both original and reduced test suite depending upon the test suite's size or the need for accuracy. In the obtained results, the efficiency of the prioritization algorithms has been analyzed by the Average Percentage of Code Coverage (APCC) and Average Percentage of Code Coverage with cost (APCCc). A comparison of the proposed approach is also done with the previously proposed methods and it is observed that APCC & APCCc values achieve higher percentage values faster in the case of the prioritized test suite in contrast to the non-prioritized test suite.

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