Evaluasi Deteksi Smell Code dan Anti Pattern pada Aplikasi Berbasis Java
Main Article Content
This paper presents an evaluation result of smell code and anti pattern detection in java based application development. The main objective to be achieved in this research is to determine the proper way in the detection of smell code and anti pattern in the development of java based software, and to evaluate the impact of using code inspection tools and software metrics to refactoring code in java based software development. Smell code to be detected in this research is Long Parameter List, Large Class, Lazy Class, Feature Envy, Long Method, and Dead Code. Anti pattern that will be detected is The Blob / God Class and Lava Flow. The selection of smell code and anti pattern is based on the definition, characteristics, detection factor, and software metrics. To support the research process is done through the evaluation stage of a case study java based application as a sample for inspection of code for the detection of smell code and anti pattern and calculation software metrics. Case studies of selected applications as sample applications are E-Commerce applications with functional master data management of goods and customers as well as management of sales and payment transactions. The detection of the smell code and anti-pattern on the case study is done in stages so it can be determined whether or not to refact. As well as ensuring the technique of making the program better fit the characteristics and rules of object-oriented programming.
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How to Cite
S. F. Sujadi, “Evaluasi Deteksi Smell Code dan Anti Pattern pada Aplikasi Berbasis Java”, JuTISI, vol. 5, no. 3, Jan. 2020.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial used, distribution and reproduction in any medium.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.