• Corpus ID: 11262211

Automatic Software Structural Testing by Using Evolutionary Algorithms for Test Data Generations

  title={Automatic Software Structural Testing by Using Evolutionary Algorithms for Test Data Generations},
  author={Maha Alzabidi and Ajay Kumar},
Summary Software testing is an important activity of the software development process. It is a critical element of software quality assurance. Structural-oriented test methods which define test cases on the basis of internal program structure are widely used. Evolutionary testing is a promising approach for automation of structural test case design, which search test data that fulfill given structural test criteria by manner of evolutionary computation. In this article we investigate the… 

Genetic Algorithm Technique In Program Path Coverage For Improving Software Testing

This paper used Genetic Algorithm technique for improving the efficiency of software testing by identifying the error prone path in a program by using Genetic Algorithms approach that optimize and select the program path which are weighted in accordance with the errorprone path.

Effective Test Data Generation using Genetic Algorithms

This paper proposes to improve software-testing efficiency with suitable optimization techniques by using genetic algorithms for generating the test data that can cover the most error-prone path; so that emphasis can be given on testing these paths firstly.

Software Test Case Reduction using Genetic Algorithm : A Modified Approach

A Genetic Algorithm based methodology has been proposed which can significantly reduce the test suite and leads to optimization of the efficiency of software testing.

Search-based software test data generation using evolutionary computation

Search-based Software Engineering has been utilized for a number of software engineering activities. One area where Search-Based Software Engineering has seen much application is test data


The purpose of this paper is to provide optimized results by improving the efficiency of GA by applying genetic algorithm to optimize test cases on program i.e. HCF of two numbers.


Various Genetic Algorithm (GA) based test methods which will be having different parameters to automate the structural-oriented test data generation on the basis of internal program structure to improve the overall performance of genetic algorithm in search space exploration and exploitation fields with better convergence rate are presented.

A genetic algorithm based approach for prioritization of test case scenarios in static testing

A technique for optimizing static testing efficiency by identifying the critical path clusters using genetic algorithm and calculating the information flow complexity associated with each node of the control flow graph generated from the source code is proposed.

A Survey on Software Testing Techniques using Genetic Algorithm

This research paper presents a survey of GA approach for addressing the various issues encountered during software testing, and highlights use of evolutionary algorithms for automatic test generation.

Generation of Search Based Test Data on Acceptability Testing Principle

The results suggest that the acceptability based algorithm is better than the reliability based path testing and condition testing techniques in both of these categories, and may significantly reduce the time of search based test data generation significantly outperforms Random testing.

Adapting Gene Expression Programming to Quality Assurance Standards

The obtained results showed that using GEP to test software systems is more rapid and of better quality compared to random search approaches which end up with creating a large volume of useless test cases.



Automatic Test Data Generation For Structural Testing Of Embedded Software Systems By Evolutionary Testing

This paper presents an evolutionary test environment, which performs fully automatic test data generation for most structural test methods and reports on the results gained from the testing of real-world software modules.

Automatic Test Data Generation for Data Flow Testing Using a Genetic Algorithm

  • M. Girgis
  • Computer Science
    J. Univers. Comput. Sci.
  • 2005
An automatic test data generation technique that uses a genetic algorithm, which is guided by the data flow dependencies in the program, to search for test data to cover its def-use associations, to evaluate the effectiveness of the proposed GA compared to the random testing technique.

Genetic algorithms for dynamic test data generation

This paper discusses experiments with a test generation problem that is harder than the ones discussed in earlier literature-the authors use a larger program and more complex test adequacy criteria and finds a widening gap between a technique based on genetic algorithms and those based on random test generation.

The automatic generation of software test data using genetic algorithms

Genetic Algorithms have been used successfully to automate the generation of test data for software developed in ADA83 and the results show that the GAs give most improvements over random testing when these subdomains are small.

Evolutionary Testing of Embedded Systems

The evolutionary test is a new, promising approach for the automation of test case design and can be used to automate both the testing of functional and non-functional properties.

Breeding software test cases with genetic algorithms

This paper focuses on breeding software test cases using genetic algorithms as part of a software testing cycle, an evolving fitness function that relies on a fossil record of organisms results in interesting search behaviours, based on the concepts of novelty, proximity, and severity.

Data Generation for Path Testing

Two stochastic search algorithms for generating test cases that execute specified paths in a program are presented and it is shown that SA and GA are superior to KA in the number of executed paths, and GA is faster than SA; however, KA, when it succeeds in finding the solution, is the fastest.

Automatic Software Test Data Generation

In software testing, it is often desirable to find test inputs that exercise specific program features. Finding these inputs manually, is extremely time consuming, especially, when the software being

Software Testing Techniques

The aim is to compare different testing techniques and what efforts it takes to find genuine failures on particular level of test, e.g. unit/component level, and on other test levels, such as integrated and system test level.

Art of Software Testing

Comprehensively covers psychological and economic principles, managerial aspects of testing, test tools, high-order testing, code inspections, and debugging, and programming students will find this reference work indispensible.