## 6 Discussion
### Interpreting Answers to RQs and PQs
#### 6.1.1 Interpreting Answers to RQs
For example, FCORE, a model-based approach, facilitates the coordination of adaptations between variability-intensive systems [70]. The allowable runtime reconfigurations of each system are specified using an FCORE model, combining the FMs used in the DSPL with the goal models. Then, the previous models are converted to constraint satisfaction problems for determining conflicts and synergies between system adaptations during execution. [PERSON] et al. [71] propose an approach based on extended feature models (EFMs) for simple FMs with feature attributes, binding times, and corresponding constraints. All model transformations have been implemented using eMoflon, a meta-CASE tool for model-based software development. Among the most used approaches based on FMs to manage software reconfigurations are the Context-aware Feature Models (CFMs). This approach contains two modeling stages, the FM and the context model, which allows for providing an autonomous (pre) planning  
Figure 17: Relationship between approaches to managing runtime variability and the evolution of challenges to manage DSPLs between the years 2010 to 2021.  
and execution of the reconfiguration of a product at runtime according to contextual requirements identified for the DSPL [72]. An extended context-aware feature modeling (eCFM) is proposed by [73], which can identify common design flaws in DSPL FMs by identifying how these features behave over time. Although the MAPE-K control loop and CFMs are two independent approaches, combined proposals address both CFMs and the MAPE-K control loop [74]. Table 13 shows a contrast between the proposed approaches with FMs and the MAPE-K control loop. In Figure 17, we can observe that the principal methodology applied to manage DSPLs is the MAPE-K control loop, the most widely used to manage dynamic environments. Interpreting Answers for RQ3  
Based on the gathered evidence for RQ3, it is possible to state that the main challenges in the management of DSPLs are based on techniques. According to [75], empirical research should be performed to obtain more realistic probability distributions of real-time constraints because most proposals are performed in a test environment, affirming what was mentioned by [13]. Also, there is a need to validate the proposals, either in an industrial environment or in different test cases, expanding the application areas of the proposed approach. In the case of challenges related to the area of open variation, most proposals seek to improve existing approaches. For example, the proposal of [PERSON] to implement the evolution model offered by MAPE-K and automating the support for learning and evolution, defining three edges associated with the definition of safe operational limits, sandboxing, and verification of the variability associated with a self-adaptive system at runtime [39]. According to [76], it is necessary to use some mechanism to recognize new context entities and insert them into the adaptation rules. From Figure 17, we can visualize an increase in the challenges associated with open variation and a decrease in static approaches, such as the definition of explicit variation points. Finally, we can state that the area of open dynamic variability is constantly increasing, and attempts are made to minimize the use of closed dynamic variability due to its limitations in reconfigurations. The works that do not mention challenges are related to applying an open dynamic variability.