The development of applications for mobile devices is a constantly growing market which and more and more enterprises support the development of applications for this kind of devices. In that sense, videogames for mobile devices have become very popular worldwide and are now part of highly profitable and competitive industry. Due to the diversity of platforms and mobile devices and the complexity of this kind of applications, the development time and the number of errors within that development process have increased. The productivity of the developers has also decreased due to the necessity of using many programming languages in the development process. One of the most popular strategies is to employ specialized people to perform the development tasks more efficiently, but this involves an increase of the costs, which makes some applications economically unviable. In this article we present the Gade4all Project, consisting in a new platform that aims to facilitate the development of videogames and entertainment software through the use of Domain Specific Languages and Model Driven Engineering. This tool makes possible for users without previous knowledge in the field of software development to create 2D videogames for multiplatform mobile devices in a simple and innovative way
Nowadays, business models are in permanent evolution since the requirements belongs to a rapidly evolving world. In a context where communications all around the world travel so fast the business models need to be adapted permanently to the information the managers receive. In such world, traditional software development, needed for adapting software to changes, do not work properly since business changes need to be in exploitation in shorter times. In that situation, it is needed to go quicker from the business idea to the exploitation environment. This issue can be solved accelerating the development speed: from the expert to the customer, with no –or few, technical intervention. This paper proposes an approach to empower domain experts in developing adaptability solutions by using automated sets of production rules in a friendly way. Furthermore, a use case that implements this kind of development was used in a real problem prototype
Web recommendation systems usually brings a content list to users based on previous ratings made by them to other similar contents through some social voting mean. This paper aims to present a comparison of the main explicit rating methods used by web recommendation systems. The goal of this survey is to determine which of the studied methods fits better to user preferences when they rate a content on the web; based on the obtained results, a recommendation system can be implemented using an explicit feedback method to achieve this goal.
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