## 6 Discussion and Conclusions  
We have proposed an expert-based methodology for software effort estimation, suitable for use in early phase estimation of Agile software development projects. Unlike traditional software effort estimation methods, our methodology can be applied in a time-limited context of early software requirements elicitation. It also supports the elicitation of requirements from various media: Story maps, requirements lists, UML Use case models, UML Activity models, UML Class models, and SysML Requirement models. The use of various forms for the requirements elicitation allows the involvement of stakeholders with various competencies. For the project scope estimation, a single list of requirements is composed by extracting requirements from the various media used by the participants of the early requirements workshop. To avoid duplicates and contradictions, requirements are then deduplicated and reconciled with each other. The final list of requirements is used to estimate the project scope by assigning story points to the requirements via an expert-based approach. The case study has demonstrated that using several different models for requirements elicitation allowed different aspects of solution to be discussed. While Story map was used as a central model, other models have allowed additional requirements to be contributed, which might have been missed if only Story map had been used. The analysis of synonyms allowed noun and verb synonyms to be identified and eliminated to unify the domain concepts. It also enabled the identification of overlapping contexts of requirements and reducing the number of distinct contexts by aggregating the overlapping ones. A smaller number of distinct contexts made the final requirements list more manageable. After identifying overlaps, the final count of requirements was reduced to 91 from 133 initially. This indicates the usefulness of automated overlap analysis, since otherwise there would have been a large amount of wasted effort produced by overlapping requirements. The scope estimation phase has resulted in most requirements having up to 8 story points and an average level of clarity. Several requirements were assigned 13 and 21 story points, which indicates that they should be further broken down into smaller parts. However, in the early scope assessment phase, having a few \"heavy\" requirements is generally acceptable, because subsequent splitting could require additional time that is not available in the early requirements workshop. The case study using the proposed methodology demonstrated that using UML Use case, Activity, and Class diagrams as additional sources of requirements in addition to the Story map can increase the number of discovered requirements to be used in project scope estimation. The possibility of recording requirements in various forms prepared by different workshop participants and then reconciling them allowed us to calculate a more detailed estimate for the demonstrated project. In the future, we are planning to continue our research in two main directions. First, we aim to apply the methodology to a larger set of software development projects covering a wider variety of domains and project sizes, thus exploring the scalability of the methodology. Second, we also plan to improve the proposed methodology and tool by employing machine learning techniques in the requirements reconciliation step to reduce reliance on individual expertise and better support decision-making processes. Machine learning models can be helpful in supporting team members in identifying synonyms and/or requirement overlaps and aggregating similar contexts, thus further streamlining the reconciliation step. Conceptualization, L.B., L.C. and M.J.; methodology, L.B., L.C. and E.G. ; software, E.G. and L.B. ; validation, L.C., M.J. and L.A.; formal analysis, L.B. and L.C. ; investigation, L.B. ; resources, L.B. and L.C. ; writing--original draft preparation, L.B., L.C. and M.J.; writing--review and editing, L.B., L.C., M.J. and L.A.; visualization, L.B. and L.C.