
Research Article


A Greedy Particle Swarm Optimization Strategy for Tway Software Testing


Bestoun S. Ahmed
and
Kamal Z. Zamli


ABSTRACT

Combinatorial strategies are used as methods or mechanisms for selecting test cases using combinations of test input parameters. We normally want that all tway combinations of parameter values occur in the test suit at least once. Artificial intelligence base search algorithms have been used within strategies for constructing near optimal test suites. In this paper, we propose a new test generation strategy, for combinatorial testing based on greedy Particle Swarm Optimization. The basic design concepts of the strategy are demonstrated through the paper. The experimental results and comparisons of our strategy showed impressive results as far as the test suite size is considered.





Received:
December 28, 2011; Accepted: January 20, 2012;
Published: June 05, 2012 

INTRODUCTION
In the complex software systems, such as those used in industrial applications,
there is a large number of possible test cases due to the different input parameters
the system may accept. These parameters and their interactions with each other
must be considered to ensure the accurate detection of different software bugs
(Qu et al., 2007). However, exhaustive testing
is often impossible due to time and resource limitations (Lei
et al., 2008). As a result, there still exists a need for a more
intelligent mechanism to ensure that such interaction coverage is approached
systematically with a minimum number of test cases. For solving such a problem,
combinatorial strategy used which is a method or mechanism for selecting test
cases using a combination of test input parameters (Beizer,
1990).
Due to the complexity of search space in the combinatorial interaction problems,
different techniques have been used to deal with this search process. Artificial
intelligent techniques have been regarded as being especially adequate search
strategies, since they are able to deal with search for optimization (Afzal
et al., 2009). Two of the well known algorithms are Genetic Algorithm
(GA) and Ant Colony Algorithm (ACA). However, other heuristic search techniques
have started to compete with GA and ACA such as Particle Swarm Optimization
(PSO) in the context of algorithm simplicity and performance (Wang
et al., 2011; Marinakis and Marinaki, 2010a;
Marinakis and Marinaki, 2010b; Pant
et al., 2008). Recent literature showed that PSO outperforms GA and
ACA in different cases of optimization problems (Windisch
et al., 2007).
Motivating by those researches, this paper a new test suite generation strategy
for tway combinatorial testing (whereby, t indicates the interaction strength)
based on Particle Swarm Optimization, namely PSTG. PSTG complements our earlier
work on pairwise testing (Ahmed and Zamli, 2011) to
investigate the use of PSO for testing when t = 2. In addition to its impressive
results against other strategies in case of test case size, PSTG generates one
test case at a time.
Particle swarm optimization: Particle swarm optimization is a mechanism
that tries to manipulate a certain number of candidate solutions at once (Chettih
et al., 2011; Qasem and Shamsuddin, 2010).
The whole population is called swarm and the solutions are called particles
(Jie et al., 2008; Poli, 2008).
Each solution represented by a particle that works in the search space to find
a better position or solution of the problem. As a popular optimization method,
PSO has been used during the last years since it showed a number of advantages
in comparison to other optimization methods. As compared to other artificial
intelligent optimization methods, PSO has few parameters to regulate and can
be easily merged with the environment that needs optimization. In additions,
PSO does not need the calculation of derivatives that the knowledge of good
solutions is kept by all particles and that particle share the information with
others in the swarm (Ganjali, 2008; Yap
et al., 2011; Padhy, 2009; Sutha
and Kamaraj, 2008).
With the starting of the optimization process in PSO, each particle has a random
position and updates its position iteratively in the hope of finding better
solutions. This is done with each particle by holding the essential information
about its movement (Marinakis and Marinaki, 2010a; Marinaki
et al., 2010). These information including its position currently
(x_{i}), its velocity currently (v_{i}), personal best or the
position that it has achieved so far which is denoted by (pBest_{i})
of particle i, local best or the position that it has achieved in its neighborhood
which is denoted by (lBest_{i}) and the global best or the position
it has achieved in the whole swarm which is denoted by (gBest_{i}).
The manipulation of the particles around the search space is restricted by a
certain update and positions rule as follows (Windisch et
al., 2007; Ganjali, 2008):
where, t is iteration number or time, d is the dimension, j the particle index, w is the inertia weight, r and r’ are two random factors which are two random real numbers between 0 and 1 and c, c’ are acceleration coefficients that are adjusting the weight between components. Pursuant to such updated rule, each particle update its velocity for better movement around the search space and the new velocity used to find the new position of the particles depending on a cost factor that controls this movement. A strategy for tway test suite generation: In test suite generation, we are mainly dealing with parameters and values and we want to find optimal test cases that cover most of the interaction elements. We introduce each particle as a vector. Since each test case has (D) parameters, as a result, the particle or the vector is (D) dimension also. We can illustrate this vector by the notation: X_{j} = (X_{j,1}, X_{j,2},X_{j,d}………, X_{j,D}). Referring to Fig. 1, our test suite generation strategy will start by receiving the parameters and values. The strategy immediately manipulates all parameters’ values. Then, the algorithm will generate all tway combinations named Ps that contains all tinteraction element combinations of parameters’ values that are not been covered yet (step 4).  Fig. 1: 
A test case generation procedure for PSO 
When a test case is found for Ts that can cover more tinteraction elements, the strategy removes the tcombinations which are covered by this test case, from Ps list. The strategy continues its running until Ps list get empty (step 6). The strategy randomly initializes each particle in the swarm search space with its associated parameter values (step 7). It compares each particle which represents a test case, with the list of tinteraction element PS (steps 9 and 20). EXPERIMENTAL RESULTS
To justify and evaluate the efficiency of our strategy in term of the generated
test suite size, we made comparison with some existing strategies and tools
based on wellknown benchmarks. These strategies are IPOG with its tool FireEye
(Lei et al., 2007), WHITCH, Jenny, TConfig and
TVG. The comparison aims to study the growth in the generated test suite size
in terms of strength of coverage (t). We adopt two different set of experiment
conducted by Lei et al. (2007) and Bryce
et al. (2005).
All strategies are employed within our environment which consisted of a desktop PC with Windows XP, 2.8 GHz Core 2 Due CPU, 2 GB of RAM and JDK 1.5 installed. Table 1 and 2 showed the results obtained for the two set of experiments. Each table represents the smallest test suit size obtained. The cells were marked NS (not supported) indicate that the tool cannot generate the test case for a specific configuration and the cells were marked NA (not available) indicate that the results were unavailable. Referring to the above tables, we note that our PSTG strategy scales well against other strategies in most cases. Referring to Table 1, PSTG produces optimal sizes in case of t equals to 2 and 4 while in case of t equals to 3 and 5 it can compete the other strategies except the optimal one. Table 1: 
Comparison with existing algorithms 

NS: Not supported, NA: Not available 
Table 2: 
P and V constants (10, 2) but t varied up to 6 

NS: Not supported, NA: Not available 
WHITCH appeared to produce satisfactory results in case of small value of t (maximum of 4) and not producing results beyond that. Similarly, TConfig and TVG do not produce any specific results for more than one day of running in case of t>4. Jenny produce a reasonable result in case of t equals to 5 while it cannot produce results in case of t equals to 6. With the multiple domain (variable) configurations in Table 2, also PSTG scales well against others in most cases. In most of the tests PSTG can produce the most optimum; however, when it is not the most optimum in some cases, it still can compete with most of the other none optimum strategies.
CONCLUSION
We have proposed and illustrated our efficient strategy, namely, PSTG for tway
combinatorial test case generation using a novel approach by combining the greedy
fashion of particle swarm optimization technique with the software test case
generation to gain near optimal solution. The main concern of our algorithm
is optimization in term of size of the resulting test sets. From the experiment
results, we can state that no single strategy can claim absolute dominance over
other strategies for all configuration since it is an NPcomplete problem (Lei
et al., 2007; Lei and Tai, 1998). PSTG performs
better than other strategies in term of size in most cases but it cannot be
the most optimal strategy for all configuration sets. We are currently developing
our PSTG strategy to release the beta version of the PSTG tool.
ACKNOWLEDGMENT
This research is partially funded by the generous fundamental grants“Investigating
tway Test Data Reduction Strategy Using Particle Swarm Optimization Technique”
from Ministry of Higher Education (MOHE) and the USM Research University grants“Development
of variablestrength interaction testing strategy for tway Test Data generation ”
and the short term grant“Development of interaction testing tool for pairwise
coverage with seeding and constraints”. The first author, Bestoun S. Ahmed,
is a recipient of the USM fellowship.

REFERENCES 
Afzal, W., R. Torkar and R. Feldt, 2009. A systematic review of searchbased testing for nonfunctional system properties. Inform. Software Technol., 51: 957976. CrossRef 
Ahmed, B.S. and K.Z. Zamli, 2011. The development of a particle swarm based optimization strategy for pairwise testing. J. Artif. Intell., 4: 156165. CrossRef  Direct Link 
Beizer, B., 1990. Software Testing Techniques. 2nd Edn., Van Nostrand Reinhold, New York, ISBN: 0442206720, Pages: 550.
Bryce, R., C.J. Colbourn and M.B. Cohen, 2005. A framework of greedy methods for constructing interaction tests. Proceeding of the 27th International Conference on Software Engineering, May1521, 2005, ACM Press, St. Louis, MO., USA., pp: 146155.
Chettih, S., M. Khiat and A. Chaker, 2011. Voltage control and reactive power optimisation using the meta heuristics method: Application in the Western algerian transmission system. J. Artif. Intell., 4: 1220. CrossRef  Direct Link 
Ganjali, A., 2008. A requirementsbased partition testing framework using particle swarm optimization technique. Master Thesis, Electrical and Computer Engineering, University of Waterloo, Ontario, Canada.
Jie, J., J. Zeng, C. Han and Q. Wang, 2008. Knowledgebased cooperative particle swarm optimization. Applied Math. Comput., 205: 861873. CrossRef  Direct Link 
Lei, Y. and K.C. Tai, 1998. Inparameterorder: A test generation strategy for pairwise testing. Proceedings of the 3rd IEEE International Symposium on HighAssurance Systems Engineering, November 1314, 1998, Washington DC. USA., pp: 254261.
Lei, Y., R. Kacker, D.R. Kuhn, V. Okun and J. Lawrence, 2007. IPOG: A general strategy for Tway software testing. Proceedings of the 14th Annual IEEE International Conference and Workshops on the Engineering of ComputerBased Systems, March 2629, 2007, IEEE Computer Society, Tucson, AZ., USA., pp: 549556.
Lei, Y., R. Kacker, D.R. Kuhn, V. Okun and J. Lawrence, 2008. IPOG/IPOGD: Efficient test generation for multiway combinatorial testing. Softw. Test. Verification Reliab., 18: 125148. Direct Link 
Marinaki, M., Y. Marinakis and G.E. Stavroulakis, 2010. Fuzzy control optimized by PSO for vibration suppression of beams. Control Eng. Pract., 18: 618629. CrossRef 
Marinakis, Y. and M. Marinaki, 2010. A hybrid multiswarm particle swarm optimization algorithm for the probabilistic traveling salesman problem. Comput. Operations Res., 37: 432442. CrossRef  Direct Link 
Marinakis, Y. and M. Marinaki, 2010. A hybrid genetic: Particle swarm optimization algorithm for the vehicle routing problem. Exp. Syst. Appl., 37: 14461455. CrossRef 
Padhy, N.P., 2009. Artificial Intelligence and Intellegent Systems. Oxford University Press, Oxford.
Pant, M., P. Sharma, T. Radha, R.S. Sangwan and U. Roy, 2008. Nonlinear optimization of enzyme kinetic parameters. J. Boil. Sci., 8: 13221327. CrossRef  Direct Link 
Poli, R., 2008. Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl. 10.1155/2008/685175
Qasem, S.N. and S.M. Shamsuddin, 2010. Generalization improvement of radial basis function network based on multiobjective particle swarm optimization. J. Artif. Intell., 3: 116. CrossRef  Direct Link 
Qu, X., M.B. Chohen and K.M. Woolf, 2007. Combinatorial interaction regression testing: A study of test case generation and prioritization. Proceedings of the IEEE International Conference on Software Maintenance, October 25, 2007, University of NebraskaLincoln, Lincoln, pp: 255264.
Sutha, S. and N. Kamaraj, 2008. Particle swarm optimization applications to static security enhancement using multi type facts devices. J. Artif. Intell., 1: 3443. CrossRef  Direct Link 
Wang, J., Y. Cai, Y. Zhou, R. Wang and C. Li, 2011. Discrete particle swarm optimization based on estimation of distribution for terminal assignment problems. Comput. Ind. Eng., 60: 566575. CrossRef 
Windisch, A., S. Wappler and J. Wegener, 2007. Applying particle swarm optimization to software testing. Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, July 711, 2007, ACM Press, London, England, pp: 11211128.
Yap, D.F.W., S.P. Koh, S.K. Tiong and S.K. Prajindra, 2011. Particle swarm based artificial immune system for multimodal function optimization and engineering application problem. Trends Applied Sci. Res., 6: 282293. CrossRef  Direct Link 



