# Reasoning about Confidence in Goal Satisfaction
## 8 Conclusions  
Data availability has always been a big challenge in industry. Yet, reasoning about system goals and alternatives in the presence of uncertainty related to data quality is important. In this paper, we propose a Data Quality Tagging and Confidence Propagation Mechanism that maps data quality to initial confidence levels and propagates this information to compute the confidence in the satisfaction values of other elements of a goal model. The approach, implemented in a modeling tool, improves on related work thanks to its simplicity and broader applicability, as illustrated in the example and informally reported by IT workers who perceived value in this approach. Not only does it provide confidence levels for all intentional elements of a (GRL) goal model, which can influence decision making, but it also helps identify locations where higher-quality data would help increase confidence in the goal-oriented analysis.  
This work can also enable further research on the impact of weak/strong confidence of high/low satisfaction values on decision making. In particular, high-quality data may not be needed everywhere in a model to support proper decision-making; for example, the structure of a goal model could compensate weak confidence from one source with strong confidence from another source. In other words, there might be some built-in tolerance to some level of uncertainty in different goal model structures. On the other hand, the lack of sufficient confidence in a conclusion might require decision-makers to seek additional evidence or better sources of data to increase the resulting confidence above a certain threshold to actually make an informed decision.  
For future work, different and more rigorous data quality dimensions, as well as the generalization of our mechanism to languages other than GRL, could both be explored. Different functions for aggregating confidence levels (e.g., average, maximum, etc.) based on GRL link types could also be more formally and empirically evaluated; their suitability might vary along different contexts, and we could even enable analysts to select them explicitly for a given model. Further industrial validation is also required, especially outside the healthcare sector, and ideally done by people other than the developers of the approach. Furthermore, we see an opportunity to combine our approach with other existing methods presented in related work to provide more concrete evidence to support decisions by considering the source, volume, and quality of data being used.  
**Author Contributions:** Conceptualization, M.B. and D.A.; methodology, M.B., D.A. and J.M.; software, M.B.; validation, M.B.; formal analysis, M.B. and D.A.; investigation, M.B., D.A. and J.M.; resources, M.B. and D.A.; data curation, M.B.; writing--original draft preparation, M.B.; writing--review and editing, D.A. and J.M.; visualization, M.B.; supervision, D.A.; project administration, D.A.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Education, Kingdom of Saudi Arabia (Ph.D. scholarship).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank [PERSON] and managers at Al-Rass Hospital for their collaboration in the case study. We also thank the anonymous reviewers for their useful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.