An Online Agent-Based Search Approach in Automated Computer Game Testing with Model Construction

TL;DR

This paper presents an online agent-based search method for automated game testing that dynamically constructs a system model during testing, improving efficiency in exploring complex game interaction spaces.

Contribution

It introduces a novel online agent-based approach that builds a system model on-the-fly, aiding automated testing of complex computer games.

Findings

Effective in reducing testing time

Successfully applied to Lab Recruits game

Model construction improves test coverage

Abstract

The complexity of computer games is ever increasing. In this setup, guiding an automated test algorithm to find a solution to solve a testing task in a game's huge interaction space is very challenging. Having a model of a system to automatically generate test cases would have a strong impact on the effectiveness and efficiency of the algorithm. However, manually constructing a model turns out to be expensive and time-consuming. In this study, we propose an online agent-based search approach to solve common testing tasks when testing computer games that also constructs a model of the system on-the-fly based on the given task, which is then exploited to solve the task. To demonstrate the efficiency of our approach, a case study is conducted using a game called Lab Recruits.