Automated Test Data Generation Using an Iterative Relaxation Method ?

Software development is not complete without software testing. It constitutes an inseparable part of software development process. Almost 50 percent of the total funds for development of a software are spend on software testing and this process consumes most of the time of the whole development process.

AUTOMATED TEST DATA GENERATION
Generation of test data for the given test and according to the testing criteria proves to be quite a difficult problem.
- The automated test data generation can relieve much stress of the software testers.
- Generation of test data that is used to make a program follow a given path is the most prominent and an important problem that arises while carrying out the path oriented testing.
- The obtained input is refined by various iterations and another input is obtained.
- The whole process predicates on this obtained input.
- The statements of the program relevant to the evaluation of each branch are executed in each iteration.
- Up on each iteration, a set of linear constraints is obtained which are then solved to obtain the increment values for the input data.
- The obtained increment values are added to the current input value to obtain the input data that is to be used for the next iteration.

RELAXATION METHODS
- The relaxation method provides knowledge about the amount by which the value of each input variable should be modified for the branches on the path in order to evaluate the desired result.

ITERATIVE RELAXATION METHODS
- These can be defined as techniques for solving system of equations.
- Relaxation methods are also iterative methods defined for numerical problems of mathematics.
- They are extensively used for solving system of equations which include the following types:
(A) Linear equations
Relaxation methods are used for solving the linear equations. Problems like that of linear least squares are usually addressed under this category.

(B) System of linear inequalities
Iterative or relaxation methods prove very effective in solving the system of linear inequalities which represent the problems similar to those that arise during linear programming.

(C) Non linear system of equations
These days, iterative methods or relaxation methods have been developed and prove a great help in solving system of equations which are non linear.

- Iterative relaxation methods have proven to be very effective and important methodology in providing solutions for linear system of equations.

- They prove to be effective system of equations that are used to solve partial differential equations based on the model of ellipse.

- These systems of equations are basically used to describe problems related to boundary conditions and values in which the value of the function in the solution is indicated or specified on the boundary of a specified domain.

- If the branch conditions on a path are linear in nature, the iterative relaxation technique either obtains a solution for such a path in just one iteration or it declares that the path is in-feasible.

- We can say that the existing approaches require an unacceptably large number of iterations for longer paths since they use only one branch predicate as well as input variable at a time.

- These methods also use back tracking.

- If the branch conditions on a path are non linear in nature, then it takes more than one iteration to get the desired input data.

- But, the set of constraints that has to be solved is linear in nature and can be solved using the method of gauss elimination.

These advantages make the technique of automated test data generation practical as well as suitable for automated testing.