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On Automating Video Game Regression Testing by Planning and Learning

Tomáš Balyo, G. Michael Youngblood, Filip Dvořák, Lukáš Chrpa, Roman Barták

TL;DR

The paper addresses automating regression testing in video game development by leveraging automated planning and action-model learning (AML) to synthesize PDDL domain models from game logs. It presents a practical workflow that separates roles between developers and PDDL modellers, enabling non-experts to contribute to planning-driven test generation. A proof-of-concept on a Unity Creator Kit RPG demonstrates automatic domain synthesis, planning, and test execution, while discussing challenges of fully automating logging and domain refinement. The work aims to broaden adoption of automated planning in game testing by reducing reliance on formal-modeling expertise and by integrating logs into the test-generation loop.

Abstract

In this paper, we propose a method and workflow for automating regression testing of certain video game aspects using automated planning and incremental action model learning techniques. The basic idea is to use detailed game logs and incremental action model learning techniques to maintain a formal model in the planning domain description language (PDDL) of the gameplay mechanics. The workflow enables efficient cooperation of game developers without any experience with PDDL or other formal systems and a person experienced with PDDL modeling but no game development skills. We describe the method and workflow in general and then demonstrate it on a concrete proof-of-concept example -- a simple role-playing game provided as one of the tutorial projects in the popular game development engine Unity. This paper presents the first step towards minimizing or even eliminating the need for a modeling expert in the workflow, thus making automated planning accessible to a broader audience.

On Automating Video Game Regression Testing by Planning and Learning

TL;DR

The paper addresses automating regression testing in video game development by leveraging automated planning and action-model learning (AML) to synthesize PDDL domain models from game logs. It presents a practical workflow that separates roles between developers and PDDL modellers, enabling non-experts to contribute to planning-driven test generation. A proof-of-concept on a Unity Creator Kit RPG demonstrates automatic domain synthesis, planning, and test execution, while discussing challenges of fully automating logging and domain refinement. The work aims to broaden adoption of automated planning in game testing by reducing reliance on formal-modeling expertise and by integrating logs into the test-generation loop.

Abstract

In this paper, we propose a method and workflow for automating regression testing of certain video game aspects using automated planning and incremental action model learning techniques. The basic idea is to use detailed game logs and incremental action model learning techniques to maintain a formal model in the planning domain description language (PDDL) of the gameplay mechanics. The workflow enables efficient cooperation of game developers without any experience with PDDL or other formal systems and a person experienced with PDDL modeling but no game development skills. We describe the method and workflow in general and then demonstrate it on a concrete proof-of-concept example -- a simple role-playing game provided as one of the tutorial projects in the popular game development engine Unity. This paper presents the first step towards minimizing or even eliminating the need for a modeling expert in the workflow, thus making automated planning accessible to a broader audience.
Paper Structure (12 sections, 6 figures, 1 table)

This paper contains 12 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Motivation
  3. What can be tested with Planning
  4. Applicability
  5. Preliminaries
  6. Related Work
  7. The General Workflow
  8. Proof of concept: A Simple RPG Game
  9. Usage During Development
  10. Challenges with Fully Automated Logging for Learning
  11. Conclusion
  12. Future Work

Figures (6)

  • Figure 1: The overview of the workflow for acquiring PDDL domain models based on logs from the game execution. The objects with white background represent the tasks best done by game developer(s). The tasks and decisions with a blue background should be done by the PDDL modeller. The light green items can be done automatically.
  • Figure 2: A fragment of the log file created by playing the game and used for action model acquisition.
  • Figure 3: First half of the RPG domain PDDL description.
  • Figure 4: Second half of the RPG domain PDDL description.
  • Figure 5: The problem file for the RPG demo. Some of the objects and initial state predicates are redacted to shorten the listing.
  • ...and 1 more figures