Abstract
The mobile casual game application lifespan is getting shorter. A company has to shorten the game testing procedure to avoid being squeezed out of the game market share. There is no sufficient testing indicator to objectively evaluate the operability of different game designs. Many automated testing methodologies are proposed, but they adopt rule-based approaches and cannot provide quantitative analysis to statistically evaluate gameplay experience. This study suggests applying “Learning Time” as a testing indicator and using the learning curve to identify the operability of different game designs. This study also proposes a Long/Short-Term Memory based automated testing model (called LSTM-Testing) to statistically testing game experience through end-to-end functionality (Input: game image; Output: game action) without any manual intervention. The experiment results demonstrate LSTM-Testing can provide quantitative testing data by using learning time as the control variable, game design as the independent variable, and time to complete game as the dependent variable. This study also demonstrates how LSTM-Testing evaluates the effectiveness of different gameplay learning strategies, e.g., reviewing the newest decisions, reviewing the correct decision, or reviewing the wrong decisions. The contributions of LSTM-Testing are (1) providing an objective and quantitative analytical game-testing framework, (2) reducing the labor cost of inefficient and subjective manual game testing, and (3) allowing game company boosts software development by focusing on game intellectual property and leaves game testing to artificial intelligence (AI).
Highlights
The mobile casual game with a simple gameplay feature contributes15~25% of the total sales volume of the game industry [1]
The design of the casual game targets casual players by reducing demands on time and learned gameplay skills, in contrast to complex hardcore games, such as Starcraft/Diablo-like games. This is because casual players only play games in short bursts for relaxing their brain or social purposes, and 70% of people spend only six months to play a game [2]
(1) quantitatively evaluate the difficulty of different game design, (2) to ensure reasonable leveling path design, and (3) to validate the game difficulty is proportional to level scales
Summary
The mobile casual game with a simple gameplay feature (called casual game shortly) contributes. This study, proposes a Long/Short-Term Memory based automated testing model (called LSTM-Testing) to provide quantitative testing data These data help the game designer to objectively evaluate the game design. The contributions of LSTM-Testing are (1) providing an objective and quantitative testing indicator to evaluate game experience including unreasonable traps in a game, calculate the average time to defend difference scales of enemy attacks, and keep proportional fairness of different game levels, (2) demonstrating the possibility of rapidly AI-assisted prototyping which enables game project manager with less technical domain experts to develop a suitable game design and visual interfaces, and (3) presenting an AI-driven strategic decision-making framework to assess the performance of existing applications and help both business sponsors and engineering teams identify efforts that would maximize impact and minimize risk.
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