# Reasoning about Confidence in Goal Satisfaction
## 7 Discussion  
Although the decision-making process of the hospital from the case study is still in progress, the case study provided an instance where the data quality approach presented here adds a piece of information to the goal model that can estimate confidence in a simple and practical way, while identifying locations where data quality needs to be higher. Many studies have been proposed to support decision making in the presence of uncertainty. However, they are often not practical in industry. For example, in the healthcare sector, clinical data about patients, diagnoses, and treatments, in most cases, are available. In this context, advanced methods, such as those based on probability distributions, can be used. However, management data about performance or costs (beyond acquisition costs) related to a new technology or system design are rarely available [32]. The challenge increases when data is unavailable about the current technological solution being used in a hospital. In this case, the mechanism proposed in this paper would be beneficial to highlight data insufficiency and the need to collect more evidence. As we are interested in healthcare requirements engineering research [32], the approach has been discussed with healthcare IT workers with encouraging feedback. In addition to the potential benefits in practice (decision-making support, and coverage of goal contributions), this data quality approach could likely be applied to other goal modeling languages beyond GRL (e.g., \(i^{*}\), KAOS, or the Goal Structuring Notation).  
There are also several limitations to the method and threats to the validity of our work that deserve attention:  
1. One major limitation is that data quality types (Section 4.1) could be improved based on other dimensions. The quality types presented in this paper are generic and function well, at least in the context of this paper and its case study. However, more precise types may be needed in other contexts. For example, the classification of data gathered through sensors in real time is not yet supported in the proposed approach.
2. It is also worth mentioning that assigning quality types to data is not trivial. Currently, this is done based on an assessment of analysts and stakeholders involved in the context where, probably, many disagreements and conflicts arise. Therefore, it is important to systematize and more formally describe the process of assigning quality types to data.  
3. The functions used to propagate confidence levels (Section 4.2), although they did not generate complaints from the case study participants, are currently only justified through arguments. They could be further validated empirically, especially against alternative propagation functions.
4. The illustrative example may not reflect the complexity of other real-world cases and contexts, especially outside the healthcare domain. Additional cases studies in other domains would help raise our confidence in the suitability and generalizability of the mechanism proposed here. More formal experiments could also provide more reliable empirical evidence, for instance by comparing the outputs of the uncertainty reasoning proposal with those of domain experts, or by studying the scalability and usability of the reasoning as goal models get larger.
5. The approach was currently implemented for GRL models. Although we do not see major issues in porting it to other goal-oriented modeling languages, whether specific semantics of these languages will require major adaptations to some of the confidence propagation functions remains a research topic.
6. As the creators of the Data Quality Tagging and Propagation Mechanism also led the development and analysis of the case study, real or perceived biases represent another potential threat to the internal validity of our work. One potential mitigation would be to have people other than us lead experiments and case studies about the usefulness of the approach.