How Can Game Developers Leverage Data from Online Distribution Platforms? A Case Study of the Steam Platform

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Authors
Lin, Dayi
Keyword
Computer games , Steam , Online distribution
Abstract
Developing a successful game is challenging. Prior work shows that gamers are extremely difficult to satisfy, making the quality of games an important issue. Prior work has yielded important results from mining data that is available on the online distribution platforms for software applications, helping practitioners save valuable resources, and improving the user-perceived quality of software that is distributed through these platforms. However, much of the work on mining online distribution platforms focuses on mining mobile app stores (e.g., Google Play Store, Apple App Store). Video game development differs from the development of other types of software. Hence, knowledge derived from mining mobile app stores may not be directly applicable to game development. In this Ph.D. thesis, we focused on mining online distribution platforms for games. In particular, we mined data from the Steam platform, the largest digital distribution platforms for PC gaming, with over 23,000 games available and over 184 million active users. More specifically, we analyzed the following four aspects of online distribution for games: urgent updates; the early access model (which enables game developers to sell unfinished versions of their games); user reviews; and user-recorded gameplay videos on the Steam platform. We observed that the choice of update strategy is associated with the proportion of urgent updates that developers have to release. Early access game developers can use the early access model as a method for eliciting early feedback and more positive reviews to attract additional new players. In addition, although negative reviews contain more valuable information for developers, the portion of useful information in positive reviews should not be ignored by developers and researchers. Finally, we proposed an approach for determining the likelihood that a gameplay video demonstrates a game bug, with both a mean average precision at 10 and a mean average precision at 100 of 0.91. Our approach can help game developers leverage readily available gameplay videos to automatically collect otherwise hard-to-gather bug reports for games. The results of our empirical studies highlight the value of mining online distribution platforms for games in offering practical suggestions for game developers.
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