Abstract:
Increasing number of input sizes are caused by the exponential growth of test input interaction
and create a large input space. The problem examine is needed to do so fast that even the fasted
computers require an insufferable amount of time. It limits the ability of computers to solve
large input space problems. Only less amount of test case can solve the problem. Since twenty
years many useful t-way strategies have been developed to reduce test case size. Deterministic
and non-deterministic search strategies are used to design T-way (sequence-less) strategy such
as, High level Hyper Heuristic (HHH), Harmony Search Strategy (HSS),Cuckoo Search
Strategy (CSS),Particle Swarm Test Generator (PSTG), Simulated Annealing (SA), Genetic
Algorithm (GA), Ant Colony Algorithm (ACA), Bat-Inspired t-way Strategy (BTS), Late
Acceptance Hill Climbing (LAHC), Nie Implementation of GA (GA-N), Automatic Efficient
Test Generator (AETG), Modified Automatic Efficient Test Generator (mAETG), In Parameter
Order General (IPOG), Test Vector Generator (TVG), Generalized T-Way Test Suite
Generator (GTWay), Density, para order etc. Sequence-less strategy indicates the inputs are
taken as parameterized. From the literature it is found that the T-way strategy for sequenceless input interaction is an NP-hard problem. So no one can get optimum solution for every
combination of system configuration. In this research an algorithm is proposed and
implemented to enhance the T-way input interaction test strategy (sequence-less). To check the
effectiveness, the proposed algorithm is compared with the other renown deterministic and
non-deterministic search based T-way strategies. The result help to show that the strategy (for
sequence-less input interaction) able to generate feasible results and minimize the number of
test cases compared with other strategies.
