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An effective Seed Selection Method for Maximizing the Performance of Symbolic Execution
http://doi.org/10.5626/JOK.2025.52.7.578
Symbolic execution is a promising software testing technique that aims to maximize the coverage of executed code areas of program under test by effectively generating test cases. A well-known challenge in symbolic execution is the high cost associated with solving path conditions. One solution to this challenge is to utilize generated test cases as seed inputs for a symbolic execution tool, thereby reducing the number of required solver calls. The effectiveness of this solution depends on the selection of appropriate test cases as seed inputs. This paper proposes a method to enhance the performance of symbolic execution by grouping the generated test cases into clusters, identifying the most promising cluster and selecting the most potential seed input from within it. Experimentally, the proposed method achieved 42.0% more branch coverage on average than traditional symbolic execution tools without seed inputs.
A Binary Decision Diagram-based Modeling Rule for Object-Relational Transformation Methodology
Sooyoung Cha, Sukhoon Lee, Doo-Kwon Baik
In order to design a system, software developers use an object model such as the UML class diagram. Object-Relational Transformation Methodology (ORTM) is a methodology to transform the relationships that are expressed in the object model into relational database tables, and it is applied for the implementation of the designed system. Previous ORTM studies have suggested a number of transformation methods to represent one relationship. However, there is an implementation problem that is difficult to apply because the usage criteria for each transformation method do not exist. Therefore, this paper proposes a binary decision diagram-based modeling rule for each relationship. Hence, we define the conditions for distinguishing the transformation methods. By measuring the query execution time, we also evaluate the modeling rules that are required for the verification. After evaluation, we re-define the final modeling rules which are represented by propositional logic, and show that our proposed modeling rules are useful for the implementation of the designed system through a case study.
A Design of Metadata Registry Database based on Object-Relational Transformation Methodology
Sooyoung Cha, Sukhoon Lee, Dongwon Jeong, Doo-Kwon Baik
The ISO/IEC 11179 Metadata registry (MDR) is an international standard that was developed to register and share metadata. ISO/IEC 11179 represents an MDR as a metamodel that is an object model. However, it is difficult to develop an MDR based on ISO/IEC 11179 because the standard has no clear criteria to transform the metamodel into a database. In this paper, we suggest the design of an MDR data model that is based on object-relational transformation methodology (ORTM) for the MDR implementation. Hence, we classify the transformation methods of ORTM according to the corresponding relationships. After classification, we propose modeling rules by defining the standard use of the transformation. This paper builds the relational database tables as an implementation result of an MDR data model. Through experiments and evaluation, we verify the proposed modeling rules and evaluate the suitability of the created table structures. As the result, the proposed method shows that the table structures preserve classes and relationships of the standard metamodel well.
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