Automatic Identification of Game Stuttering via Gameplay Videos Analysis

Abstract

Modern video games are extremely complex software systems and, as such, they might suffer from several types of post-release issues. A particularly insidious issue is constituted by drops in the frame rate ( i.e. , stuttering events), which might have a negative impact on the user experience. Stuttering events are frequently documented in the million of hours of gameplay videos shared by players on platforms such as Twitch or YouTube. From the developers’ perspective, these videos represent a free source of documented “testing activities”. However, especially for popular games, the quantity and length of these videos make impractical their manual inspection. We introduce HASTE, an approach for the automatic detection of stuttering events in gameplay videos that can be exploited to generate candidate bug reports. HASTE firstly splits a given video into visually coherent slices, with the goal of filtering-out those that not representing actual gameplay ( e.g ., navigating the game settings). Then, it identifies the subset of pixels in the video frames which actually show the game in action excluding additional elements on screen such as the logo of the YouTube channel, on-screen chats etc. In this way, HASTE can exploit state-of-the-art image similarity metrics to identify candidate stuttering events, namely subsequent frames being almost identical in the pixels depicting the game. We evaluate the different steps behind HASTE on a total of 105 videos showing that it can correctly extract video slices with a 76% precision, and can correctly identify the slices related to gameplay with a recall and precision higher than 77%. Overall, HASTE achieves 71% recall and 89% precision for the identification of stuttering events in gameplay videos.