Code Smell Detection Bahasa Pemrograman Python Menggunakan Pendekatan Software Metrics Pada Struktur Abstract Syntax Tree

Wadi, Hamzan (2024) Code Smell Detection Bahasa Pemrograman Python Menggunakan Pendekatan Software Metrics Pada Struktur Abstract Syntax Tree. Undergraduate thesis, Politeknik Negeri Bengkalis.

	Text (Abstract) 1.TA-6304201242-Abstract.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (2MB)
	Text (Bab I Pendahuluan) 2.TA-6304201242-Bab I Pendahuluan.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (2MB)
	Text (Daftar Pustaka) 3.TA-6304201242-Daftar Pustaka.pdf - Submitted Version Available under License Creative Commons Attribution Share Alike. Download (2MB)
	Text (Full Text) 4.TA-6304201242-Full Text.pdf - Submitted Version Restricted to Registered users only Available under License Creative Commons Attribution Share Alike. Download (4MB)

Abstract

Code smell can take the form of design flaws or bad practices in program code, leading to various deficiencies that are detrimental to software development projects. These deficiencies include a decrease in code quality that can result in difficulty understanding, increased project complexity that increases the potential for bugs, obstacles in code maintenance, decreased developer productivity, and increased risk of errors. This research aims to create a code smell detection application in the Python programming language. The development method used is the waterfall development method. The system involves converting Python programs into AST, developing code smell detection logic using the software metrics approach, and testing using blackbox testing. The results show that the system is able to detect several types of code smell in Python programs, namely long method, lazy class, feature envy and provide information about code complexity. Blackbox testing proves that the system functionality runs well.

Item Type:	Thesis (Undergraduate)
Contributors:	Contribution Contributors Email NIDN/NIDK Thesis advisor Kasmawi, Kasmawi kasmawi@polbeng.ac.id 1007067701 Thesis advisor Subandri, Muhammad Asep msubandri@polbeng.ac.id 0009129201
Uncontrolled Keywords:	Abstract Syntax Tree (AST), Code Smell, Python, Software Metrics
Subjects:	410 ILMU TEKNIK > 450 TEKNIK ELEKTRO DAN INFORMATIKA > 463 Teknik Perangkat Lunak
Divisions:	Jurusan Teknik Informatika > Sarjana Terapan Rekayasa Perangkat Lunak > TUGAS AKHIR
Depositing User:	Rekayasa Perangkat Lunak A 2024
Date Deposited:	26 Aug 2024 13:40
Last Modified:	26 Aug 2024 13:40
URI:	http://eprints.polbeng.ac.id/id/eprint/13769

Actions (login required)

View Item