Video-game development, despite being a multi-billion-dollar industry, has not attracted sustained attention from software engineering researchers and remains understudied from a software engineering perspective. We aim to uncover, from game developers’ perspectives, which video game development topics are the most asked about and which are the most supported, in order to provide insights about technological and conceptual challenges game developers and managers may face on their projects. To do so, we turned to the Game Development Stack Exchange (GDSE), a prominent Question and Answer forum dedicated to game development. On that forum, users ask questions and tag them with keywords recognized as important categories by the community. Our study relies on those tags, which we classify either as technology or concept topics. We then analysed these topics for their levels of community attention (number of questions, views, upvotes, etc.) and community support (whether their questions are answered and how long it takes). Related to community attention, we found that topics with the most questions include concepts such as 2D and collision detection and technologies such as Unity and C#, whereas questions touching on concepts such as video and augmented reality and technologies such as iOS, Unreal-4 and Three.js generally lack satisfactory answers. Moreover, by pairing topics, we uncovered early clues that, from a community support perspective, (i) the pairing of some technologies appear more challenging (e.g., questions mixing HLSL and MonoGame receive a relatively lower level of support); (ii) some concepts may be more difficult to handle conjointly (e.g., rotation and movement); and some technologies may prove more challenging to use to address a given concept (e.g., Java for 3D). Our findings provide insights to video game developers on the topics and challenges they might encounter and highlight tool selection and integration for video game development as a promising research direction.
Working on technologies that have community support is one of the most important factors in software development. Software developers often face difficulties during software development, and community support from other software developers help them significantly. This paper presents an approach based on K-mean clustering technique to identify the level of community support for software technologies and development concepts using Stack Overflow discussion forums. To test the approach, a case study was performed by gathering data from SO and preparing a dataset that contains over a million of Java developers' questions. Then, K-mean clustering was applied to identify the community support levels. The goal is to find the best features that group community-supported software technologies and development concepts and identify the number of groups to determine the community support levels. Statistical error, clustering and classification evaluation metrics were applied. The results indicate that the best features to formulate community supported technologies and development concept levels are Failure Rate and Wait Time. The results show that the approach identifies two groups of community supported and development concept levels based on the best silhouette index value of 97%. According to the results the majority of Java technologies and development concepts are labeled with less community supported technologies and development concepts (Cluster 2). Random Forest classifier was applied to indirectly evaluate the approach to detect the identified community support class. The result shows that RF classifier presents a good performance and shows high accuracy value of 99.49% which indicates that the identified groups improve the performance of the classifier. The approach can be utilized to assist software developers and researchers in utilizing the SO platform in developing SO-based recommendation systems.
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