Aims: Software Test Suite Optimization (TSO) is a common approach for generating efficient
test data in lesser time. This paper presents an efficient methodology for automatic generation of
independent paths and TSO with the help of Artificial Bee Colony (ABC) investigating technique.
Method: The proposed work combines both global search methods (by scout bees) and local search
methods (performed by employee bees and onlooker bees). The parallel behaviour of these three bees
makes the generation of independent paths and software TSO faster.
Observation: The proposed novel approach is compared with other population-based approaches such
as Genetic Algorithm (GA) and Ant Colony Optimization (ACO). It is analysed and validated using 30
Java programs and the results show that it outperforms the other approaches on the basis of execution
time and percentage of optimization.
Results: The study presents sophisticated concept in a simplified form that should be beneficial to both
researchers and practitioners involved in solving TSO problems.
Keywords: Investigating technique, software under test, ant colony, software testing, artificial bee colony,
optimization, genetic algorithm, test data generation.
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