An automatic test data generation for data flow.

1. 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

2. PRESENTED BY
WAFA QAISER KHAN
MS(SE)-2

3. 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.

4.  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.

5. INTRODUCTION
 Almost 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.

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

7. 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.

8. EXAMPLE PROGRAM

10.  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.

13. ALGORITHMS
GENETIC ALGORITHMS
 Inspired 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

14. A basic pseudo algorithm for a GA:

15.  The fitness function used is
mathematically expressed as:

17. PARTICLE SWARM
OPTIMIZATION
 Developed 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

18.  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 ]

19. ◦ 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.

21. CONCLUSION
A set of 12 small FORTRAN programs is used in the
experiments.

22.  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.

23. REFERENCES
1.http://www.sciacademypublisher.com/jo
urnals/index.php/IJRRCS/article/view/4
57
2.http://en.wikipedia.org/wiki/Particle_swa
rm_optimization

24. THANKYOU