# Automatic Cause-Effect Graph Tool with Informal Korean Requirement Specifications
## 2 Related Studies  
In requirement-based testing, most researchers considered a formal language, UML, or natural language for requirement specification. However, we have difficulty dealing with natural language-based requirements. [PERSON] [4] also mentioned natural language requirements with ambiguity, lack of consistency, inaccuracy, and incomplete information. [PERSON] [5] focused on a semi-formal mechanism for specifying the English language description of the function. [PERSON] suggests the cause-effect graph in specifying the functional behavior of a program and formalization based on mathematical logic. We found it unwise to use this cause-effect graph to represent the large English language descriptions.  
The existing tools should manually draw the cause-effect graph without analyzing requirements. It provides various functions for entering and managing detailed information. We compared current representative cause-effect graph tools as follows:  
Hongik MDA-based embedded software component development methodology (HiMEM) [6] is a tool that supports the generation of test cases from use case descriptions. We suggest the process of generating test cases from the use case specifications without requirement specifications, which generates a cause-effect graph from each use case diagram/sequence diagram. This tool also uses various UML diagrams. Bender-RBT [7] supports manually drawing the cause-effect graph, which automatically generates test cases via decision tables. Without requirement specifications, this tool needs to be manually input. That is, this tool does not analyze its requirements. [PERSON]'s cause-effect graph testing tool [8] also automatically generates a test case via a decision table with a manual cause-effect graph. It provides input and management functions for node logic, type, test process observation, and true/false state description. This tool does not analyze its requirements.  
[PERSON] noted that 100% coverage could be satisfied with the minimum number of test cases [9] if a test case is generated with his cause-effect graph at the top of Figure 1. In the middle of Figure 1, our previous approach generates a cause-effect graph from each use case and sequence diagram [10; 11], which does not use requirement specifications. At the bottom of Figure 1, we mentioned how to generate a cause-effect graph with natural language. This method generates a cause-effect graph via a C3 Tree model from informal Korean requirements [3].  
Our proposed Korean requirement analyzer for cause-effect graph (KRA-CE) automatically generates cause-effect graph from informal requirements specifications. By providing web-based UIs, we will potentially work with other web-based tools. Table 1 shows the difference between KRA-CE and the existing tools. O is Yes. X is no.  
Most previous researchers do not work automatically to generate test cases from requirement specifications written in diverse natural languages. Our research focuses on automatically generating test cases with Korean requirement specifications. IBM researchers focus on recognizing and splitting conditional sentences for the automation of business process management, which identifies the causes and effects of English sentences based on a deep learning approach [12]. However, they have difficulty analyzing complex sentences.