This paper presents ArSGam, a modular architecture designed to integrate gamification schemes into serious games aimed at cognitive-affective training. ArSGam focuses on the user's emotional, cognitive, motivational profile and learning style. The proposal seeks to move beyond traditional approaches in which game elements are added without clear justification while offering a structured framework that aligns pedagogical, motivational, and emotional objectives. We designed four modules, guiding the process from user profiling to the logical selection of game mechanics: 1) Contextual Analysis, 2) Recommendation, 3) Gamification Scheme Design, and 4) Gamification Integration. Particularly, the inclusion of the cognitive-affective model in the first module facilitated the identification of the emotional and cognitive aspects of the user. We used a case study conducted in the mining sector to validate the architecture's applicability, flexibility, and coherence. In addition, we developed a formal validation using an instrument adapted from the ISO/IEC 25010 standard, evaluating five key characteristics: functionality, usability, maintainability, compatibility, and portability. The evaluation resulted in a global quality score of Q = 47 with an average standard deviation of σ = 0.3, indicating high consistency and overall quality across the assessed characteristics. The results suggest that ArSGam enables the integration of gamification into serious games, thus enhancing educational processes across diverse contexts.

Model-Driven Engineering can play a crucial role in developing serious training games. Model-Driven Engineering benefits the creation of serious games by offering a structured software modeling approach, resulting in enhanced productivity, quality, and maintainability of the game software. These models can then be used to automatically generate code and other artifacts, significantly reducing development time and improving efficiency. We propose a metamodel that describes users' cognitive and affective dimensions during a serious game, utilizing a set of classes that define the relationships between them based on a theoretical framework of cognitive functions and emotions. We employed the Eclipse Modeling Framework to transform a primary model of the problem domain into a metamodel. We then combine the cognitive and affective dimensions of the model and formalize it into a generic metamodel for cognitive-affective training of users using serious games. Finally, we illustrate our approach with an example of instantiation of the model metamodel. We concluded with the importance of Model-Driven Engineering in serious game development.

The legacy code of a tool handling domain specific data gathers valuable expertise. However in many cases, it must be rewritten to make it apply to structurally incompatible data. We investigate a co-evolution approach to avoid this update by making the call context meet the a legacy tool definition domain. The data conforming to the call context co-evolve into data conforming to the definition domain. Once processed by the tool, they can be put back into their original context thanks to a specific reverse transformation which enables the recovery of elements that had been initially removed. This approach is applied to Orcc, a compiler for dataflow applications. Orcc requires many common functions that are expected to be adapted to its own context. Our approach is an effective way to reuse them instead of rewriting them.

This paper puts the focus on adaptable model migrations. A dedicated formalism is introduced to combine automatically-generated migrations with custom-made migrations. To illustrate this issue and the approach we suggest to address it, a prototype engine is presented. Then, the prototype is applied on a case study. The prototype processes the migration specifications that have been automatically generated and then customized. The case study consists of the reuse of a mapping tool, in order to change highlighted places. During the reuse process the migration specification is customized in order to produce different migrated models.