Test data generation with a Kalman filter-based adaptive genetic algorithm
We introduce a new approach for generating test data, based on adaptive optimisation.The adaptive optimisation framework uses feedback from the optimisation process to adjust parameter values of a genetic algorithm during the search.Our approach is compared to a state of the art test data optimisation algorithm that does not adapt parameter values online, and a conspicuous adaptive optimisation algorithm, outperforming both methods in a wide range of problems. Software testing is a crucial part of software development. It enables quality assurance, such as correctness, completeness and high reliability of the software systems. Current state-of-the-art software testing techniques employ search-based optimisation methods, such as genetic algorithms to handle the difficult and laborious task of test data generation. Despite their general applicability, genetic algorithms have to be parameterised in order to produce results of high quality. Different parameter values may be optimal for different problems and even different problem instances. In this work, we introduce a new approach for generating test data, based on adaptive optimisation. The adaptive optimisation framework uses feedback from the optimisation process to adjust parameter values of a genetic algorithm during the search. Our approach is compared to a state of the art test data optimisation algorithm that does not adapt parameter values online, and a representative adaptive optimisation algorithm, outperforming both methods in a wide range of problems.