Many-Objective Neuroevolution for Testing Games
Generating tests for games is challenging due to the high degree of randomisation inherent to games and hard-to-reach program states that require sophisticated gameplay. The test generator NEATEST tackles these challenges by combining search-based software testing principles with neuroevolution to optimise neural networks that serve as test cases. However, since NEATEST is designed as a single-objective algorithm, it may require a long time to cover fairly simple program states or may even get stuck trying to reach unreachable program states. In order to resolve these shortcomings of NEATEST, this work aims to transform the algorithm into a many-objective search algorithm that targets several program states simultaneously. To this end, we combine the neuroevolution algorithm NEATEST with the two established search-based software testing algorithms, MIO and MOSA. Moreover, we adapt the existing many-objective neuroevolution algorithm NEWS/D to serve as a test generator. Our experiments on a dataset of 20 SCRATCH programs show that extending NEATEST to target several objectives simultaneously increases the average branch coverage from 75.88% to 81.33% while reducing the required search time by 93.28%.
Code (0)
등록된 구현이 없습니다.
Tasks
software testingSimilar Papers 제목 키워드 기반
Neuroevolution in Games: State of the Art and Open Challenges
This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution, artificial neural networks are trained through evolutionary algorithms, taking inspiration from the way biological brains evolved. …
Evolutionary AlgorithmsNeuroevolution for RTS Micro
This paper uses neuroevolution of augmenting topologies to evolve control tactics for groups of units in real-time strategy games. In such games, players build economies to generate armies composed of multiple types of u…
Real-Time Strategy GamesStarcraftCombining Neuroevolution with the Search for Novelty to Improve the Generation of Test Inputs for Games
As games challenge traditional automated white-box test generators, the Neatest approach generates test suites consisting of neural networks that exercise the source code by playing the games. Neatest generates these neu…
An electronic-game framework for evaluating coevolutionary algorithms
One of the common artificial intelligence applications in electronic games consists of making an artificial agent learn how to execute some determined task successfully in a game environment. One way to perform this task…
Evolutionary AlgorithmsNeuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles
The incentive for using Evolutionary Algorithms (EAs) for the automated optimization and training of deep neural networks (DNNs), a process referred to as neuroevolution, has gained momentum in recent years. The configur…
Autonomous VehiclesEvolutionary AlgorithmsTrajectory Prediction