Quality Visualization of Quality Metric Indicators based on Table Normalization of Static Code Building Information


KIPS Transactions on Software and Data Engineering, Vol. 12, No. 5, pp. 199-206, May. 2023
https://doi.org/10.3745/KTSDE.2023.12.5.199,   PDF Download:
Keywords: software engineering, Database Normalization, Code Metrics, Static Analysis, Software Visualization
Abstract

The current software becomes the huge size of source codes. Therefore it is increasing the importance and necessity of static analysis for high-quality product. With static analysis of the code, it needs to identify the defect and complexity of the code. Through visualizing these problems, we make it guild for developers and stakeholders to understand these problems in the source codes. Our previous visualization research focused only on the process of storing information of the results of static analysis into the Database tables, querying the calculations for quality indicators (CK Metrics, Coupling, Number of function calls, Bad-smell), and then finally visualizing the extracted information. This approach has some limitations in that it takes a lot of time and space to analyze a code using information extracted from it through static analysis. That is since the tables are not normalized, it may occur to spend space and time when the tables(classes, functions, attributes, Etc.) are joined to extract information inside the code. To solve these problems, we propose a regularized design of the database tables, an extraction mechanism for quality metric indicators inside the code, and then a visualization with the extracted quality indicators on the code. Through this mechanism, we expect that the code visualization process will be optimized and that developers will be able to guide the modules that need refactoring. In the future, we will conduct learning of some parts of this process.


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Cite this article
[IEEE Style]
C. Park, S. Y. Moon, R. Y. C. Kim, "Quality Visualization of Quality Metric Indicators based on Table Normalization of Static Code Building Information," KIPS Transactions on Software and Data Engineering, vol. 12, no. 5, pp. 199-206, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.5.199.

[ACM Style]
Chansol Park, So Young Moon, and R Young Chul Kim. 2023. Quality Visualization of Quality Metric Indicators based on Table Normalization of Static Code Building Information. KIPS Transactions on Software and Data Engineering, 12, 5, (2023), 199-206. DOI: https://doi.org/10.3745/KTSDE.2023.12.5.199.