• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.375-382
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• 2021

The paper introduces a model for evaluating and prioritizing mobile quality attributes and refactoring techniques through the examination of their effectiveness during the mobile application development process. The astonishing evolution of software and hardware has increased the demand for techniques and best practices to overcome the many challenges related to mobile devices, such as those concerning device storage, network bandwidth, and energy consumption. A number of studies have investigated the influence of refactoring, leading to the enhancement of mobile applications and the overcoming of code issues as well as hardware issues. Furthermore, rapid and continuous mobile developments make it necessary for teams to apply effective techniques to produce reliable mobile applications and reduce time to market. Thus, we investigated the influence of various refactoring techniques on mobile applications to understand their effectiveness in terms of quality attributes. First, we extracted the most important mobile refactoring techniques and a set of quality attributes from the literature. Then, mobile application developers from nine mobile application teams were recruited to evaluate and prioritize these quality attributes and refactoring techniques for their projects. A prioritization-based model is examined that integrates the lightweight multi-criteria decision making method, called the best-worst method, with the process of refactoring within mobile applications. The results prove the applicability and suitability of adopting the model for the mobile development process in order to expedite application production while using well-defined procedures to select the best refactoring techniques. Finally, a variety of quality attributes are shown to be influenced by the adoption of various refactoring techniques.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.915-934
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• 2020

Software refactoring is a process to restructure an existing software code while keeping its external behavior the same. Currently, various refactoring techniques are being used to develop more readable and less complex codes by improving the non-functional attributes of software. Refactoring can further improve code maintainability by applying various techniques to the source code, which in turn preserves the behavior of code. Refactoring facilitates bug removal and extends the capabilities of the program. In this paper, an exhaustive review is conducted regarding bad smells present in source code, applications of specific refactoring methods to remove that bad smell and its effect on software quality. A total of 68 studies belonging to 32 journals, 31 conferences, and 5 other sources that were published between the years 2001 and 2019 were shortlisted. The studies were analyzed based on of bad smells identified, refactoring techniques used, and their effects on software metrics. We found that "long method", "feature envy", and "data class" bad smells were identified or corrected in the majority of studies. "Feature envy" smell was detected in 36.66% of the total shortlisted studies. Extract class refactoring approach was used in 38.77% of the total studies, followed by the move method and extract method techniques that were used in 34.69% and 30.61% of the total studies, respectively. The effects of refactoring on complexity and coupling metrics of software were also analyzed in the majority of studies, i.e., 29 studies each. Interestingly, the majority of selected studies (41%) used large open source datasets written in Java language instead of proprietary software. At the end, this study provides future guidelines for conducting research in the field of code refactoring.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.3
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• pp.109-118
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• 2014

Code refactoring focuses on enhancing the maintainability of software to extend its lifetime. However as software applications were varied and the range of its usage becomes broaden, there are some efforts to improve software qualities like performance or reliability as well as maintainability using code refactoring techniques. Recently, as low-energy software has become one of critical issues in mobile environment, developing energy efficient software through code refactoring becomes an important one. Therefore this paper has its goal to investigate whether the existing refactoring techniques can support energy efficient software generation or not. That is to say, the existing code refactoring techniques can cause the minus of energy efficiency because they did not considered the energy consumption in their refactoring process. This paper experiments and analyzes to check whether the M. Fowler's code refactoring techniques can support the energy efficient software generation or not. Our research result can give to software developer some informations about energy-efficient refactoring techniques, and can support the development of software that has high maintainability and good energy efficiency.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.5
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• pp.209-220
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• 2016

While the services of mobile devices like smart phone, tablet, and smart watch have been increased and varied, the software embedded into such devices has been also increased in size and functional complexity. Therefore, increasing operation time of mobile devices for serviceability became an important issue due to the limitation of battery power. Recent studies focus on the software development having efficient behavioral patterns because the energy consumption of mobile devices is caused by software behaviors which control the hardware operations. However, it is often difficult to develop the embedded software with considering energy-efficiency and behavior optimization due to the short development cycle of the mobile services in many cases. Therefore, this paper proposes the refactoring techniques for reducing energy consumption, and enables to fulfill the energy requirements during software development and maintenance. We defined energy bad smells with the code patterns that can excessively consume the energy, and our refactoring techniques are to remove these bad smells. We performed some case studies to verify the usefulness of our refactoring techniques.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.137-150
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• 2016

Along with the rapid development of IT technology and business services, the effort to provide new services to the customers has been increasing, and also the improvement and enhancement of legacy systems are continuously occurring for rapid service delivery. In this situation, the quality assurance of the source code for the legacy system became a key technical elements that can quickly respond to the service needs. Refactoring is an engineering technique to ensure the quality for the legacy code, and essential for the improvement and extension of the legacy system in order to provide value-added services. This paper proposes some features of refactoring techniques through surveying and analyzing the existing refactoring techniques and tools to enhance source code quality. When service developers want to refactor the source code of the legacy system to enhance code quality, our proposed features may provide with the guidance on what to use any technique and tool in their work. This can improve the source code quality with correct refactoring and without trial and error, and will also enable rapid response to new services.

• Journal of Navigation and Port Research
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• v.36 no.5
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• pp.363-370
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• 2012

Recently, International Maritime Organization has been developing e-navigation implementation strategy plan, which is focused on various services for vessel safety navigation. Then, different kinds of software will be developed in maritime area and with this, the quality issues are to be expected becoming more important. In this paper, we adopt software refactoring techniques to reduce the complexity of structure on source code level. It makes software program more effective to understand and modify, without any change of outward behavior. The existing AIS broadcast server program is used as an example for our trial, and calculating coupling and cohesion metric are introduced to analyze the refactoring effect, taking account of the maintainability of IEC/ISO9126 software quality standards.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.66-73
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• 2023

The construction industry suffers losses due to failures in demand forecasting due to price fluctuations in construction raw materials, increased user costs due to project cost changes, and lack of forecasting system. Accordingly, it is necessary to improve the accuracy of construction raw material price forecasting. This study aims to predict the price of construction raw materials and verify applicability through the improvement of the Data Refactor technique. In order to improve the accuracy of price prediction of construction raw materials, the existing data refactor classification of low and high frequency and ARIMAX utilization method was improved to frequency-oriented and ARIMA method utilization, so that short-term (3 months in the future) six items such as construction raw materials lumber and cement were improved. ), mid-term (6 months in the future), and long-term (12 months in the future) price forecasts. As a result of the analysis, the predicted value based on the improved Data Refactor technique reduced the error and expanded the variability. Therefore, it is expected that the budget can be managed effectively by predicting the price of construction raw materials more accurately through the Data Refactor technique proposed in this study.