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Based on the aforementioned design choices, the PSTG test suite generator is sum- marized in Figure 3. This process is iteratively repeated for the other five interactions, i. In this case, PSTG appears to be busy resetting the particle to bring it back to the search space, rendering inability or taking a long time to reach the desired solution. We are also currently improving PSTG to support both uniform and variable strength interactions. Each particle works in the search space to find a better position or solution of the problem by recording the essential information about its movement. In SA, the large random search space and the update rule make the algorithm complex, and the search process is computationally intensive especially when the interaction strength grows up i. Based on the solution, a set of new solutions containing all neighbors reachable with one transaction is generated.

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In the case where the number of parameter values is non-uniform, they can be expressed by using the Mixed Covering Array MCA N ; t, k, v1v2. Based on certain update rules, each particle explores the search space by changing its position. The algorithm continues until P s becomes empty see Figure 5. In this case, f 2f 11 and f 12 are still not able to be detected, and the total fault coverage is the same to that of the 4-way test suite i.

The algorithm starts by receiving two arguments, the strength of coverage t, and the parameter values Step 1. The PSO algorithm starts by generating a random number of particles with random positions, and then moves the particles towards pBestilBesti and gBesti by adjust- ing their velocity rates [40,44].

As will be seen later, artificial intelligence-based strategies give mixed results. Cohen [29] implemented SA to support up to 3-way interaction. After these values of iteration and swarm size, we observe that there are no significant improvements in the overall test size. The best and the average test sizes are shown, and the darkened cells indicate the most optimal size.

The interaction strength t and values V are set to 4 and 5 respectively with the variation of the parameter number P from 5 to The parameter generation adopts binary digits whereby 0 indicates the exclusion of a referred parameter and 1 indicates the inclusion of the parameter.

Cheng, Orthogonal arrays with variable numbers of symbols, The Annals of Statistics, vol. Best and average test size obtained with the variation of swarm size and repetition for CA N ; 2, 57 B. Incremental generation of combinatorial interaction test data based on symmetries of covering arrays, Proc.

Tcojfig example, if we have a parameter with a range of values from 0 to 3, when the position is greater than 3, the position is reset to 0.

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Peck, Test-data generation using genetic algorithms, Software Testing, Verification and Reliability, vol. An International Jour- nal, vol.

Here, the interaction coverage for each parameter extension is checked before the complete test case is generated. A total of As an example, consider a 2-way interaction for a system with four parameters. As can be seen from the tables, the acceleration coefficients and tconcig weight have a direct impact on the test sizes.

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In accordance with the abovementioned update rule, tconfi particle updates its velocity for a better movement within the search space. When the seeded program gives tconfog different output from the unseeded program, a particular seeded fault is considered to be detected.

We also generate an exhaustive test suite of i. Here, we conclude that the generated 4-way test suite is able to detect faults in the same manner as an exhaustive test suite, but but with fewer number of tests.

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In order to determine the suitable choices for the swarm search space size and repetition, the values of c and w are fixed to the values deduced earlier i.

Different topologies have been proposed to take pBest from the neighbor.

At about the same time, Williams developed a t-way test tool called TConfig Test Configuration [33] and Hartman et al. The final part deals with comparison of PSTG with other computational-based strategies. Based on its specifications in the Test Specification Language TSL file, flex contains seven essential input parameters four 2-valued parameters, one 3-valued parameter, one valued parameter, and one 6-valued parameterwhich can ctonfig represented in tconfkg Cov- ering Array notation as CA N ; t, 24 31 If a better lBest value is found, it is set tconfiv the new lBest value instead of the old lBest value Steps 21 and The rest of the paper is as follows.

Cells marked NA not available indicate that the results are unavailable from the literature.

On the other hand, in the case of CA N ; 2, 57the best solution is obtained when the swarm size equals to