Earticle

Software refactoring is a technique that transforms the various types of software artifacts to improve the software internal structure without affecting the external behavior. Refactoring is commonly applied to improve the software quality after a significant amount of features are added. Researchers in the area have studied the different angles of refactoring and developed the right evidence, knowledge and skill. And they published their research findings through journals and conference papers to provide an easy access to everyone. Eventually, the knowledge accumulated in these literatures is huge, so that it needs structuring and organizing. The main purpose of this study is to extend a previously conducted study by covering more literatures and applying a systematic literature review method to increase the accuracy and validity of the study. We study a collection of literature from different electronic databases, published since 1999 to understand and extract the software refactoring knowledge through classification and summarization. The classification and summarization can reveal the research pattern, common concerns and statistics of the published papers in the last fifteen years. The extracted information should help the researchers to formulate better research topics that can solve the crucial problems in software refactoring and save the researchers effort and time.

Refactoring is a technique to make a computer program more readable and maintainable. A bad smell is an indication of some setback in the code, which requires refactoring to deal with. Many tools are available for detection and removal of these code smells. These tools vary greatly in detection methodologies and acquire different competencies. In this work, we studied different code smell detection tools minutely and try to comprehend our analysis by forming a comparative of their features and working scenario. We also extracted some suggestions on the bases of variations found in the results of both detection tools.

코드 스멜은 Kent Beck에 의해 사용된 개념으로, 잠재적인 품질 문제를 나타내며 리팩토링의 필요성을 제시한 다. 본 논문은 LEA 코드베이스에서 코드 스멜을 평가하며, 분류와 관련된 메트릭에 중점을 둔다. 연구에서는 LEA_core.c와 LEA.cpp를 분석하여 코드 품질과 복잡성의 차이를 강조한다. 또한 연구에서는 LOC, NOM, NOA, CYCLO, MAXNESTING, FANOUT와 같은 메트릭을 사용하여 크기, 복잡성, 결합도, 캡슐화, 상속, 응집도를 평가한 다. 연구 결과에서는 LEA_core.c가 LEA.cpp에 비해 더 복잡하고 유지보수가 어려운 것으로 나타났다. 우리는 향후 연구에서 실시간 코드 스멜 탐지 및 리팩토링 제안을 위한 자동화 도구를 개발할 것이다.

Code smells, used by Kent Beck, indicate potential quality issues and suggest the need for refactoring. This paper evaluates code smells in the LEA codebase, focusing on categorization and associated metrics. The research analyze LEA_core.c and LEA.cpp, highlighting differences in code quality and complexity. And metrics such as LOC, NOM, NOA, CYCLO, MAXNESTING, and FANOUT are used to assess size, complexity, coupling, encapsulation, inheritance, and cohesion. In the result of research, LEA_core.c is found to be more complex and challenging to maintain compared to LEA.cpp. In future work, we will develop automated tools for real-time code smell detection and refactoring suggestions