# Effort Estimation Approach through Extracting Use Cases via Informal Requirement Specifications
## 2 Materials and Methods
### Methods for Adapting Requirement Engineering with Textual Approach
#### 2.2.2 Visual Modeling as Transformation Modeling  
The proposed use-case extraction method is applied to requirements engineering with refined case grammar. Its purpose is to accurately carry the requirements to those involved in the development, which generate a use-case diagram more easily. This approach is applied to the natural language-oriented informal requirements written by the customer. Next, a use-case is extracted according to the association with the visual model transformed from requirements based on slightly connected in graph theory.  
The text-to-model identification algorithm identifies main verbs in natural language sentences according to these case grammars and extracts the arguments (nouns) related to the core predicate (main verb). Table 3 shows the basic notations for the use-case identification, which identifies arguments based on the requirement specifications like natural language sentences. It consists of predicate, argument and identifier. Predicate refers to verbs, adjectives, etc. Argument is nouns that can be taken according to its characteristics. Finally, identifier distinguishes arguments. The abbreviation 'P' is a predicate of a sentence. 'Ai' are arguments that can be taken depending on the nature of the predicate. 'I' indicates whether an identifier exists.  
Figure 2 shows the relationship between the extracted models as shown in Table 3. Using these relationships, we extract a use-case as a set of the transformed models from requirement sentences. There are two ways to express relationships. (1) Figure 2 categorizes the arguments (nouns) affected by the verbs into the categories of cases (the roles of nouns) and (2) shows where the classified arguments overlap for the same A.  
Figures 3 and 4 show the transformed model with the refined Fillmore's case grammar for use-case extraction. It represents the use-case extraction models in Table 3, and the relationships used in use-case extraction modeling in Figure 2. The argument types for use-case extraction are defined as follows:  
A\({}_{1}\)... A\({}_{n}\) = { actor, counter-actor, object, theme, result, source, instrument, experience, goal}  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Notation** & **Comment** \\ \hline Predicate & \(\mathbf{p}\) & Represent main verb in a sentence. \\ Argument & \(\mathbf{A}\) & Represent nouns that is related to subjective \\ Identification & \(\mathbf{1}\) & verb in a sentence. \\ of the same ’Ai’ Type & \(\mathbf{1}\) & Identification Line. \\ Noun (not include case mechanism) & \(\mathbf{1}\) & Nouns that is not related to case mechanism. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Notation of visual modeling [20].  
Figure 2: Relationship notation for visual modeling.  
For example, the following example illustrates the defined case mechanism on the sentence as the semantic role that different objects play when a Purchase **ACTION** is carried out. An employee acts as the **ACTOR** initiating the purchase utilizing the **INSTRUMENT** of PURCHASE order. Each purchase order identifies a set of **OBJECTS**, in this case Parts, which are to be purchased from a supplier **SOURCE** with **GOAL** of delivery to a specific Project.