Introduction To Genetic Algorithm For Software Testing

Need for Genetic Algorithm Testing:-

The test engineer develop the test case or test data for the software where they analyses the quality of software all the test cases are updated manually which consume lot of time for upgrading, also the requirement of mannul test engineer increases.

For the reduction of mannul test engineer, the algorithm testing is done. A coding is written to execute to automate the test cases for the softwares. Hence the efficiency of test cases is generated using Graph theory based on a Genetic algorithm.

Introduction for Graph Theory:-

According to graph theory, there will be the implementation of total system state as a directed graph where, G= (V,E).

V= Set of Vertices,

E= Set of Edges

Edges are lines, which is connected with the respective Vertices  as shown in the figure.

In Generic Algorithm 3 operation are performed:-

1. Selection:-  A selection process is applied to determine a way in which possible solutions are chosen for making the  set of all individuals based on their fitness or text fields. Fitness is define the capability of an individual to survive in an environment of text or Boolean type fields according to its characteristic i.e it select all the types of fields where it is text type or boolean type. Selection generated the new population from older one, i.e started a new generation. The fitness value of each an every fields for an individual in present generation is determined by an appropriate evaluation. Thus, the fitness value is used to select a set of better Text fields for an individual from a set of all individuals fields used for the next generation.[5,6].

2. Crossover:- The crossover operation is applied to the fields for an individual selected from the set of text fields individual also involves swapping of sequence of bits in the string between the two different individuals. This process of swapping repeated each time with different parent individuals until the next field has optimum text.

3. Mutation:- The mutation operation is applied for the random selected subset of the all individuals or for the text fields. Mutation leads to an alteration blueprint for an individual in small new ways to introduce good type of testing purpose. The main aim of mutation is to bring diversity in set of all individuals

Working of Genetic Algorithms:-

Genetic Algorithms were used for single objective search and optimization algorithms. Most of the Genetic algorithms is the used for chromosome, genetic operators, selection of mechanism and also for an evaluation mechanism.

Genetic algorithms involve for creating an initial set of random solutions (population) and evaluate them [1, 4, 8, 12]. Followed by a process of selection, the better solutions are identified (parents) and then used to generate new solutions (children). These values can be used to replace other less members of the population.