# Reasoning Algorithms on Feature Modeling--A Systematic Mapping Study
## 6 Discussion
### Interpreting Answers to RQs and PQs
#### 6.1.1 Interpreting Answers to RQs  
According to the evidence gathered to answer RQ1, we can state that around 70% of the algorithms were used in the domain engineering stage. The remaining 30% were used jointly between the _application engineering_ stage and _both_ stages. The significant presence of the _domain engineering_ stage can be justified because FMs help to manage product family variability in the early stages of SPL development.  
According to the evidence gathered to answer RQ2, we can state that our initial classification scheme was incorrect. In part, we believe this may be due to our previous work, which may have added a biasing factor [45; 46; 47; 48; 49]. Second, considering the new  
Figure 19: Number of papers published by year.  
Figure 20: Papers published by year.  
classification scheme, the most used technologies were _Transformations (app. 18%)_, _Modeling language (app. 14%)_, and Algorithms and Frameworks (app. 11% for each)_. The _Other_ category also reached 14%. We believe that due to this high percentage, and due to the diversity of technologies included, these results should be further reviewed to understand the impact and scope of these diverse technologies, such as NLP and Pietri networks.  
Based on the evidence gathered to answer RQ3, we can state that around 76% of the proposals originated in academia, whereas less than 20% originated in industry. Only 6% of the proposals emerged from a joint effort between academia and industry. This could be because the industry is unaware of the benefits and advantages of using reasoning algorithms for their FMs. On the other hand, the industry may not have experienced the supposed gains when testing or may not be interested in including such algorithms as part of their processes. All of these judgments can be considered hypotheses of which the verification is beyond the scope of this study, and they represent an open question that requires further research.  
Based on the evidence gathered to answer RQ4, we can state that there is a high level of validation for selected papers (_Evaluation Research, Validation Research, and Solution Proposal categories_), which is very interesting to contrast with the response of RQ3, which indicates that the proposals mainly originated from _Academia_ (app. 76%). This percentage could be a signal that proposals at the academic level are becoming much more concerned with validations and not simply leaving their proposals as a theoretical exercise.  
Based on the evidence gathered to answer RQ5, we can state that the FODA model was still the most widely used model to represent the variability of SPLs. The _Other_ category was more prevalent than the three other categories combined (_extended FM, multiplicity, and orthogonal model_). This result could indicate that in addition to the use of FMs, researchers are developing ad hoc solutions to their problem domains.  
Based on the evidence gathered to answer RQ6, we can state that the types of problems most frequently solved include _valid partial configuration_, _anomaly detection_, _void FMs_, and synthesizing FMs_. Even though we reviewed the _Other_ category in detail, the number of papers is still relevant. The criteria we finally used considered all types of solved problems that were mentioned only once, including twenty-eight solved problems such as the _automatic analysis of performance_, _feature traceability_, _conformance faults_, _depth of tree_, _variability safety_, and _unique features_, among others. A case in point for the _Other_ category is the paper SP40. This paper claims to solve twelve types of problems--(_maintainability_, _index of the feature model_, _depth of tree_, _number of features_, _number of leaf features_, _FM cognitive complexity_, _graph density_, _configuration flexibility_, _number of mandatory features_, _feature extensibility_, _number of valid configurations_, _unique cyclic-dependant features_,_and multiple cyclic-dependant features_).