scholarly journals Test Case Optimization for Enhancing System Software Quality using Genetic Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 68-75
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Test Suite ◽  
Test Case ◽  
Product Lines ◽  
Reduction Techniques ◽  
Mutation Operators ◽  
Effective Manner ◽  
Software Programs ◽  
Test Suite Reduction

Software Product Lines (SPLs) embraces an enormous capacity of feature mixtures which cause challenges for evaluating software programs. Testsuite optimization plays major role to develope the quality of SPLs. In combinatorial testing (CT), pair wise fault coverage maximization and test case reduction accomplishes a substantial role for shrinking the testing cost of software programs. Many research works have been developed and designed for CT using different test suite reduction techniques. However Fuzzy clustering and TSRSO techniques do not provide a finest solution for test suite optimization problem. For that, Genetic Algorithm (GA) Technique is recommended and designed for test suite reduction in CT. Metaheuristic genetic algorithm delivers optimum solution in an effective manner. GA chooses and consolidates the testcases in a testsuite based on some principles such that maximum faults covered with minimum execution time. In Proposed GA, finest individuals are nominated for reproduction in order to create descendants of the succeeding generation. In addition, GA is a superior type of evolutionary algorithms generate finest solutions to optimization problems using selection, crossover and mutation operators. Consequently, GA is applied for resolving test suite reduction problem in CT

scholarly journals Regression Test Suite Reduction using an Hybrid Technique Based on BCO And Genetic Algorithm

2013 ◽  
pp. 26-33
Author(s):  
Bharti Suri ◽  
Isha Mangal ◽  
Varun Srivastava
Keyword(s):  
Optimization Problems ◽  
Minimum Time ◽  
Test Suite ◽  
Test Case ◽  
Hybrid Technique ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Test Suite Reduction ◽  
The Given ◽  
Swarm Intelligence Algorithm

Regression testing is a maintenance activity that is performed to ensure the validity of modified software. The activity takes a lot of time to run the entire test suite and is very expensive. Thus it becomes a necessity to choose the minimum set of test cases with the ability to cover all the faults in minimum time. The paper presents a new test case reduction hybrid technique based on Genetic algorithms(GA) and bee colony optimization (BCO) .GA is an evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. BCO is a swarm intelligence algorithm. The proposed approach adopts the behavior of bees to solve the given problem. It proves to be optimistic approach which provides optimum results in minimum time.

Intelligent Genetic Algorithm Crossover Operators for Market-Driven Design

2016 ◽  
Author(s):  
Kevin M. Young ◽  
Scott M. Ferguson
Keyword(s):  
Genetic Algorithm ◽  
Market Share ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Product Line ◽  
Product Lines ◽  
Related Information ◽  
Mutation Operators ◽  
Intelligent Genetic Algorithm ◽  
Future Work

Heuristic algorithms have been adopted as a means of developing solutions for complex problems within the design community. Previous research has looked into the implications of genetic algorithm tuning when applied to solving product line optimization problems. This study investigates the effects of developing informed heuristic operators for product line optimization problems, specifically in regards to optimizing the market share of preference of an automobile product line. Informed crossover operators constitute operators that use problem-related information to inform their actions within the algorithm. For this study, a crossover operator that alters its actions based on the relative market share of preference for each product within product lines was found to be most effective. The presented results indicate a significant improvement in computational efficiency and increases in market share of preference when compared to a standard scattered crossover approach. Future work in this subject will investigate the development of additional informed selection and mutation operators, as well as problem informed schema.

scholarly journals Optimum Test Suite using Fault-Type Coverage Based Ant Colony Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Test Suite ◽  
Computational Time ◽  
Test Case ◽  
Test Cases ◽  
Product Lines ◽  
Fault Type ◽  
Testing Cost ◽  
Test Suite Reduction

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.

Investigating Different Metrics for Evaluation and Selection of Mutation Operators for Java

2021 ◽  
Vol 31 (03) ◽  
pp. 311-336
Author(s):  
Shweta Rani ◽  
Bharti Suri
Keyword(s):  
Cost Reduction ◽  
Test Suite ◽  
Cost Benefits ◽  
Powerful Technique ◽  
Reduction Techniques ◽  
Mutation Operators ◽  
Different Types ◽  
Java Programs ◽  
Selective Mutation ◽  
Selection Of

Mutation testing is a successful and powerful technique, specifically designed for injecting the artificial faults. Although it is effective at revealing the faults, test suite assessment and its reduction, however, suffer from the expense of executing a large number of mutants. The researchers have proposed different types of cost reduction techniques in the literature. These techniques highly depend on the inspection of mutation operators. Several metrics have been evolved for the same. The selective mutation technique is most frequently used by the researchers. In this paper, the authors investigate different metrics for evaluating the traditional mutation operators for Java. Results on 13 Java programs indicate how grouping few operators can impact the effectiveness of an adequate and minimal test suite, and how this could provide several cost benefits.

scholarly journals Test Case Reduction Using Ant Colony Optimization for Object Oriented Program

2015 ◽  
Vol 5 (6) ◽  
pp. 1424
Author(s):  
Sudhir Kumar Mohapatra ◽  
Srinivas Prasad
Keyword(s):  
Ant Colony Optimization ◽  
System Development ◽  
Object Oriented ◽  
Ant Colony ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Reduction Techniques ◽  
Maintenance Stage ◽  
The Cost

Software testing is one in all the vital stages of system development. In software development, developers continually depend upon testing to reveal bugs. Within the maintenance stage test suite size grow due to integration of new functionalities. Addition of latest technique force to make new test case which increase the cost of test suite. In regression testing new test case could also be added to the test suite throughout the entire testing process. These additions of test cases produce risk of presence of redundant test cases. Because of limitation of time and resource, reduction techniques should be accustomed determine and take away. Analysis shows that a set of the test case in a suit should satisfy all the test objectives that is named as representative set. Redundant test case increase the execution price of the test suite, in spite of NP-completeness of the problem there are few sensible reduction techniques are available. During this paper the previous GA primarily based technique proposed is improved to search out cost optimum representative set using ant colony optimization.

scholarly journals Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Ajay Shrestha ◽  
Ausif Mahmood
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Biology ◽  
Optimization Problems ◽  
Search Space ◽  
Island Model ◽  
Fine Tuning ◽  
Small Subset ◽  
Combinatorial Optimization Problems ◽  
Mutation Operators ◽  
New Concepts

Genetic Algorithm (GA) is a metaheuristic used in solving combinatorial optimization problems. Inspired by evolutionary biology, GA uses selection, crossover, and mutation operators to efficiently traverse the solution search space. This paper proposes nature inspired fine-tuning to the crossover operator using the untapped idea of Mitochondrial DNA (mtDNA). mtDNA is a small subset of the overall DNA. It differentiates itself by inheriting entirely from the female, while the rest of the DNA is inherited equally from both parents. This unique characteristic of mtDNA can be an effective mechanism to identify members with similar genes and restrict crossover between them. It can reduce the rate of dilution of diversity and result in delayed convergence. In addition, we scale the well-known Island Model, where instances of GA are run independently and population members exchanged periodically, to a Continental Model. In this model, multiple web services are executed with each web service running an island model. We applied the concept of mtDNA in solving Traveling Salesman Problem and to train Neural Network for function approximation. Our implementation tests show that leveraging these new concepts of mtDNA and Continental Model results in relative improvement of the optimization quality of GA.

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.

scholarly journals Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

2018 ◽  
Vol 8 (11) ◽  
pp. 2253 ◽  
Author(s):  
Yang Xue
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Optimization Problems ◽  
Planning Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mutation Operators ◽  
Comparison Results ◽  
Path Planning Problem

In many areas, such as mobile robots, video games and driverless vehicles, path planning has always attracted researchers’ attention. In the field of mobile robotics, the path planning problem is to plan one or more viable paths to the target location from the starting position within a given obstacle space. Evolutionary algorithms can effectively solve this problem. The non-dominated sorting genetic algorithm (NSGA-II) is currently recognized as one of the evolutionary algorithms with robust optimization capabilities and has solved various optimization problems. In this paper, NSGA-II is adopted to solve multi-objective path planning problems. Three objectives are introduced. Besides the usual selection, crossover and mutation operators, some practical operators are applied. Moreover, the parameters involved in the algorithm are studied. Additionally, another evolutionary algorithm and quality metrics are employed for examination. Comparison results demonstrate that non-dominated solutions obtained by the algorithm have good characteristics. Subsequently, the path corresponding to the knee point of non-dominated solutions is shown. The path is shorter, safer and smoother. This path can be adopted in the later decision-making process. Finally, the above research shows that the revised algorithm can effectively solve the multi-objective path planning problem in static environments.

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