Model-driven game development (MDGD) leverages the concept of model-driven engineering and game development. The focus of MDGD is to automate the game development process by emphasizing a higher level of abstraction, which will make game development faster and easier. In recent years, researchers in the MDGD community have developed several approaches in this domain. The goal of this paper is to survey and classify existing works in MDGD, identify the challenges in this domain, and provide promising future research directions. To achieve this, we conducted a systematic review by selecting 43 articles from a set of 849. The results show that MDE techniques are used to develop games in various genres. 42% of the investigated studies proposed a graphical concrete syntax for game specification and 56% of them used different target environment tools such as Unity Engine. Moreover, our suggestions include taking advantage of tooling environments and focusing on game components rather than a complete game.

Summary Software development is a collaborative activity that requires teams of software engineers to cooperate and work in parallel on versions of models. However, model management techniques such as model differencing, merging, and versioning have turned out to be difficult challenges, due to the complexity of operations and graph‐like nature of models. Therefore, a well‐developed support for model merging process, as well as conflict management, is highly desired. This paper presents a novel process for model merging, called the Epsilon‐based Three‐way Merging Process (E3MP) process. Model merging is a significant problem where there are different versions of a system model amongst modeler teams. E3MP includes three components implemented into the Epsilon framework. First, modelers can define domain‐specific rules that customize the merging process. Second, E3MP enables an automated method for syntactic and semantic conflict detection amongst different versions of the system model. Third, E3MP puts forward a pattern‐based approach for conflict resolution. We applied two generic benchmarks to assess conflict detection and resolution capabilities of our approach and carried out an initial scalability evaluation for the model merge with large models and large change sets. The results of our experiments revealed that the proposed process allows generating consistent and semantically correct merged models.