# Effort Estimation Approach through Extracting Use Cases via Informal Requirement Specifications
## 3 Results  
The use-case extraction process consists of five steps. Step 1: Requirement analysis:  
Figure 6 shows goal identification and classification of natural language-oriented requirement analysis. Each requirement is identified according to its goals (G) and classified for its goal of each use-case. As shown in Figure 6, these requirements are numbered and listed by sentences. Step 2: Sentence Analysis:  
We use a parser to analyze sentences like the requirements partitioned in step 1. For our research, the Stanford Parser [23] is used to study linguistic analysis and provides a \"Stanford Natural Language Processing Group.\" Based on neural networks, this tool determines the grammatical structure of each sentence. By analyzing the characteristics of dependency, the parser detects the textual relationship between sentences. The dependency represents it in the graph. According to the analysis, nouns are defined as NN, and VBD is the past form of a verb. Finally, DT is a determinant, and JJ is an adjective. Figure 7 shows the sentence analysis for the structure of the language. Figure 6: Goal identification and classification of natural language oriented informal requirement analysis. Step 3: Token (Verb, Noun) List:  
The data analyzed in Steps 1 and 2 are divided into verbs and nouns. Among them, verbs are stored and listed in a file list to apply [PERSON]'s method. This method analyzes requirements and extracts class diagrams. In Table 4, we analyze nouns, verbs and adjectives in the requirements and map them to model components (object, class, method, etc.). The purpose of our method is to analyze the main verb from the requirements and extract a use-case diagram. Therefore, only the verb part is applying in [PERSON]'s method in detail. The types of verbs include doing verb, being verb, having verb, etc., which are mapped as method, inheritance, aggregation, etc. We model use-case diagrams by analyzing subjects and objects associated with the main verb as a center. Here, the use-case name combines the main verb with the theme that was defined earlier. The use-case name is recommended to have a form of 'Verb + Noun' to specify it concretely. We define it as 'Main Verb + Theme.' Theme indicates 'Subject that is changed as an action or a subject (noun),' which is the core of a paragraph. Therefore, intransitive verbs are excluded among those of [PERSON]. Being verb stands for an inheritance, and having verb shows aggregation. Modal verbs represent a constraint, and they are excluded from the main verb because they indicate components of \begin{table}
\begin{tabular}{l c c} \hline \hline
**Part of Speech** & **Model Component** & **Definition** \\ \hline Proper Noun & Object & Jim, [PERSON] \\ Doing Verb & Method & Buy, Recommend \\ Being Verb & Inheritance & Is-a (kind-of) \\ Having Verb & Aggregation & Has an \\ Transitive Verb & Method & Enter \\ Intransitive Verb & Method (Event) & Depends on \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mapping parts of speech to object model components [18]. Figure 7: Sentence analysis. a class diagram. The main verb is selected from a doing verb (method or operation) or a transitive verb (method or operation). Step 4: Visual Modeling (transformed model):  
Visual modeling is divided into six steps. First, Step 4-1 identifies key predicates of requirements analysis. This step identifies the main verb while using data from Steps 1, 2 and 3. In the example of Figure 8, the main verb is \"mails.\"  
Step 4-2 examines the properties of key verbs to detect their major roles. Steps 2 and 3 identify semantically related arguments based on the identified verbs. However, they cannot distinguish every noun from a sentence. Instead, they only find arguments related to the predicate. In Figure 9, the arguments related to the main verb \"mails\" are Customer and Postal Mail. Postal Mail is analyzed as a theme while a customer is an actor. Step 4-3 extracts the identified arguments and related verbs.