An automatic test data generation for data flow
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This document discusses an automatic test data generation technique that uses particle swarm optimization (PSO) to generate test data that satisfies data flow coverage criteria. PSO is inspired by bird flocking behavior and simulates the movement of particles in a swarm to find the best solution. The PSO algorithm works by having a population of candidate solutions called particles that are moved around in the search space according to rules. The technique was able to automatically generate test data that successfully covered sample programs under all definition-use path criteria and required less generations than genetic algorithms to achieve coverage.
![ 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 ]](https://image.slidesharecdn.com/anautomatictestdatagenerationfordataflow-130303063054-phpapp02/85/An-automatic-test-data-generation-for-data-flow-18-320.jpg)
![◦ 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.](https://image.slidesharecdn.com/anautomatictestdatagenerationfordataflow-130303063054-phpapp02/85/An-automatic-test-data-generation-for-data-flow-19-320.jpg)
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.
- 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