ESEIW 2024
Sun 20 - Fri 25 October 2024 Barcelona, Spain

Background Video games are complex projects that involve a seamless integration of art and software during the development process to compose the final product. In the creation of a video game, software is fundamental as it governs the behavior and attributes that shape the player’s experience within the game. When assessing the quality of a video game, one needs to consider specific quality aspects, namely design',difficulty’, fun', andimmersiveness’, which are not considered for traditional software. On the other hand, there are not well-established best practices for the empirical assessment of video games as there are for the empirical evaluation of more traditional software. Aims Our goal is to carry out a rigorous empirical evaluation of the latest proposals to automatically generate content for video games following best practices established in software engineering research. Specifically, we compare Procedural Content Generation (PCG) and Reuse-based Content Generation (RCG). Our study also considers the perception of players and professional developers on the generated content. Method We conducted a controlled experiment where human subjects had to play with content that was automatically generated for a commercial video game by the two techniques (PCG and RCG), and evaluate it according to specific quality aspects of video games. A total of 44 subjects including professional developers and players participated in our experiment. Results The results suggest that participants perceive that RCG generates content is of higher quality than PCG. Conclusions The results can turn the tide for content generation. So far, RCG has been neglected as a viable option: typically, reuse is frowned upon by the developers, who aim to avoid repetition in their video games as much as possible. However, our study uncovered that RCG unlocks latent content that is actually favoured by players and developers alike. This revelation poses an opportunity towards opening new horizons for content generation research.