• KIPS Transactions on Software and Data Engineering
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• v.8 no.12
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• pp.465-472
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• 2019

Today, in many area the rise of software necessity there has been increasing the issue of the impotance of Good Software. Our reality in software industrial world has been happening to frequently change requirements at any stage of software life cycle. Furthermore this frequent changing will be increasing the design complexity, which will result in being the lower quality of software against our purpose the original design goals. To solve this problem, we suggest how to improve software design through refactoring based on reverse engineering. This is our way of diverse approaches to visually identify bad smell patterns in source code. We expect to improve software quality through refactoring on even frequently changing requirements.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.79-86
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• 2022

This paper focuses on realizing design improvement and high quality through visualization of reverse engineering-based software. As new technologies and complex software emerge in various areas of the fourth industry in the future, software verification with both stability and reliability is becoming an issue. We propose a reverse engineering-based UML design extraction and visualization for high-quality software ranging from simple computational software to machine learning-based data-oriented software. Through this study, it is expected to improve software quality through design improvement by checking the accuracy of the target design and identifying the code complexity.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.148-153
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• 2015

In our domestic software industries, it is focused on such a high quality development/ testing process, maturity measurement, and so on. But the real industrial fields are still working on a code-centric development. Most of the existing legacy systems did not keep the design and highly increased the code complexity with more patching of the original codes. To solve this problem, we adopt a code visualization technique which is important to reduce the code complexity among modules. To do this, we suggest a tool chaining method based on the existing open source software tools, which extends NIPA's Software Visualization techniques applied to procedural languages. In addition, it should be refactored to fix bad couplings of the quality measurement indicators within the code visualization. As a result, we can apply reverse engineering to the legacy code, that is, from programming via model to architecture, and then make high quality software with this approach.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.199-206
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• 2023

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.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.213-220
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• 2020

The power consumption and performance of hardware-based mobile and IoT embedded systems that require high specifications are one of the important issues of these systems. In particular, the problem of excessive power consumption is because it causes a problem of increasing heat generation and shortening the life of the device. In addition, in the same environment, software also needs to perform stable operation in limited power and memory, thereby increasing power consumption of the device. In order to solve these issues, we propose a Low level power improvement via identifying performance dissipation. The proposed method identifies complex modules (especially Cyclomatic complexity, Coupling & Cohesion) through code visualization, and helps to simplify low power code patterning and performance code. Therefore, through this method, it is possible to optimize the quality of the code by reducing power consumption and improving performance.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.431-436
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• 2023

Recently the incorporation of artificial intelligence approaches in the field of software engineering has been one of the big topics. In the world, there are actively studying in two directions: 1) software engineering for artificial intelligence and 2) artificial intelligence for software engineering. We attempt to apply artificial intelligence to software engineering to identify and refactor bad code module areas. To learn the patterns of bad code elements well, we must have many datasets with bad code elements labeled correctly for artificial intelligence in this task. The current problems have insufficient datasets for learning and can not guarantee the accuracy of the datasets that we collected. To solve this problem, when collecting code data, bad code data is collected only for code module areas with high-complexity, not the entire code. We propose a method for exploring common weakness enumeration by learning the collected dataset based on transfer learning of the CodeBERT model. The CodeBERT model learns the corresponding dataset more about common weakness patterns in code. With this approach, we expect to identify common weakness patterns more accurately better than one in traditional software engineering.