## 5 Results
### Answers to RQs
#### 5.1.1 RQ1: What Approach Was Used To Apply Constraints during Software Reconfigurations in DSPL? We check the constraints on runtime variability to learn about the most commonly used approaches and their context. When classifying the papers, a significant problem arose related to the initial classification of this RQ presented in the SMS protocol [35], since there were dimensions with different granularity (some very specific and others general). Therefore, to answer this RQ, the classification was based on the guidelines by [PERSON] et al. [46], in which the authors present a taxonomy of software variability approaches. * Closed Dynamic Variability: This approach aims to support the dynamic activation and deactivation of features that have been predefined in advance in the runtime variability design. A potential execution scenario can be a smart home system, where the context data collected by the installed sensors activate or deactivate some predefined features within the system, allowing it to be implemented in any smart system using sensors. In order to apply new variants to the system would require redesigning the feature model, as this approach does not support unforeseen scenarios, contexts, or features. * Open Dynamic Variability: This approach allows for unforeseen changes in the design variability at runtime, supporting the addition, removal, and modification of features dynamically, facilitating its application in systems that may need to cope with unforeseen scenarios (e.g., smart cities, robots). This variability mechanism supports unforeseen scenarios in a controlled manner. * Collaborative features: Seeks to represent features that can exchange context information at runtime for specific purposes, e.g., for real-time and critical systems or swarm systems. The main limitation of this approach is that dynamic changes are not supported. * No proposed approach. Forty-eight papers (57.1%) present an approach based on _Open Dynamic Variability_. Thirty-two papers (38.1%) present an approach based on _Closed Dynamic Variability_. Three papers (3.6%) present a _Collaborative features_ approach and one paper (1.2%) _No proposedapproach._ See details in Figure 4 and the classification of each item according to the criteria previously defined in Table 10.  
5.1.2 RQ2: What Methodologies Are Currently Used to Manage DSPL Variability during Reconfigurations? RQ2 seeks to obtain further indications on the most used methodologies in the management of DSPLs and their runtime variability, mainly visualizing their application in self-adaptive systems. This research question will be classified according to five dimensions, which were grouped once each article was analyzed. * Proprietary architecture: Methodologies proposed by the authors are mainly composed of context-aware feature models. The variability in execution time is specified during the design stage. It also contains proposals for reconfigurations based on optimization models or constraint language in variability modeling. * MAPE-K control loop: Control loop for self-adaptive systems presented by [PERSON] and [PERSON] [6], which allows managing runtime variability through machine learning. \begin{table}
\begin{tabular}{l l} \hline \hline
**Approaches for Applying Runtime** & **Selected Papers** \\
**Variability Constraints in DSPL** & \\ \hline Closed Dynamic Variability & SP1, SP4, SP5, SP6, SP7, SP10, SP11, SP12, SP13, SP14, SP15, \\  & SP17, SP18, SP19, SP20, SP21, SP24, SP27, SP29, SP32, SP37, \\  & SP44, SP46, SP49, SP55, SP59, SP62, SP68, SP69, SP70, SP73, \\  & SP78. \\ Open Dynamic Variability & SP2, SP3, SP8, SP22, SP23, SP25, SP26, SP28, SP30, SP31, SP33, \\  & SP34, SP36, SP38, SP39, SP40, SP41, SP42, SP43, SP45, SP47, \\  & SP48, SP50, SP51, SP52, SP53, SP54, SP56, SP57, SP58, SP60, \\  & SP61, SP63, SP64, SP65, SP67, SP71, SP72, SP74, SP75, SP76, \\  & SP77, SP79, SP80, SP81, SP82, SP83, SP84. \\ Collaborative features & SP9, SP16, SP35. \\ No proposed approach & SP66. \\ \hline \hline \end{tabular}
\end{table}