Earticle

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.

The focus of this paper is secure code development and maintenance. When it comes to safe code, it is most important to consider code readability and maintainability. This is because complex code has a code smell, that is, a structural problem that complicates code understanding and modification. In this paper, the goal is to improve code quality by detecting and removing smells existing in code. We target the encryption and decryption code SEED.c and evaluate the quality level of the code using several metrics such as lines of code (LOC), number of methods (NOM), number of attributes (NOA), cyclo, and maximum nesting level. We improved the quality of SEED.c through systematic detection and refactoring of code smells. Studies have shown that refactoring processes such as splitting long methods, modularizing large classes, reducing redundant code, and simplifying long parameter lists improve code quality. Through this study, we found that encryption code requires refactoring measures to maintain code security.

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