AN AUTOMATIC TEST DATA GENERATION FOR DATA FLOW

COVERAGE USING SOFT COMPUTING APPROACH

SANJAY SINGLA, PRITI SINGLA AND H M RAI

Vol. 2, No. 2, April 2011

PRESENTED BY

WAFA QAISER KHAN

MS(SE)-2

ABSTRACT

Testing is the most important quality assurance measure for software.

Testing is time consuming and laborious process.

Automatic test data generation would be useful to reduce the cost and time.

Paper presents an automatic test data generation technique that uses a particle swarm optimization (PSO) to generate test data that satisfy data flow coverage criteria.

INTRODUCTIONAlmost every service/system we

use today has an element of software in it.

To make the system reliable, predictable and to run all the time and every time, software has to tested before delivery.

Generally the goal of software testing is to design a set of minimal number of test cases such that it reveals as many faults as possible.

An automated software testing can significantly reduce time and cost of developing software.

DATA FLOW ANALYSIS TECHNIQUE

This section uses the all-uses criterion and the data flow analysis technique.

The example program determines the middle value of three given integers I, J, K.

EXAMPLE PROGRAM

Defs and c-uses are associated with nodes.

p-uses are associated with edges.Two sets dcu(i) and dpu(i,j) are

determined. The def-clear paths are

constructed from the dcu and dpu sets.

ALGORITHMSGENETIC ALGORITHMSInspired by Darwin’s theory of

evolution.It generates useful solutions.Operates on strings called

chromosomes.Each digit that makes up a

chromosome is called a gene.Each chromosome has a fitness

value associated with it, which is the probability of survival of an individual chromosome in the next generation.

A basic pseudo algorithm for a GA:

The fitness function used is mathematically expressed as:

PARTICLE SWARM OPTIMIZATIONDeveloped by Kennedy and

Eberhart.Simulates the behaviour of bird

flocking by which they find food sources.

PSO algorithm works by having a population (called a swarm) of candidate solutions (called particles).

These particles are moved around in the search-space according to a few simple formulae.

For each particle i = 1, ..., S do: Initialize the particle's position with a uniformly distributed random vector: 

xi =[xi1 , xi2, … xid ] Initialize the particle's best known

position to its initial position: pi ← xi

If (f(pi) < f(g)) update the swarm's best known position: g ← pi

Initialize the particle's velocity:  vi =[vi1 , vi2, … vid ]

◦ For each particle i = 1, ..., S do: For each dimension d = 1, ..., n do:

Update the particle's velocity: vi,d ← ω vi,d + cp rp (pi,d-xi,d) + cg rg (gd-xi,d)

ω is the inertia weight and cp cg are the acceleration constants and rp rg are two random values in range [0,1]

Update the particle's position: xi ← xi + vi

If (f(xi) < f(pi)) do: Update the particle's best known position: pi ← xi

If (f(pi) < f(g)) update the swarm's best known position: g ← pi

Now g holds the best found solution.

Stopping criterion: If the number of iterations exceeds the maximum number of iteration or accumulated coverage is 100% then stop.

CONCLUSIONA set of 12 small FORTRAN programs is used in the experiments.

PSO is able to generate test data automatically that cover successfully the sample program under all du path criteria.

It requires less number of generation to achieve def- use percentage.

REFERENCES

1.http://www.sciacademypublisher.com/journals/index.php/IJRRCS/article/view/457

2.http://en.wikipedia.org/wiki/Particle_swarm_optimization

THANKYOU