TY - GEN
T1 - Empirical evaluation of the impact of refactoring on internal quality attributes
AU - Riansyah, Muh
AU - Mursanto, Petrus
N1 - Funding Information:
This work was supported by funding from University Grant for Internationally Indexed Proceeding Publication (Hibah PUTI Presiding) Contract No: NKB-868/UN2.RST/HKP.05.00/2020, administered by the Directorate of Research and Development, Universitas Indonesia.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/17
Y1 - 2020/10/17
N2 - It is commonly believed that refactoring increases software quality. This paper presents the validation of belief about refactoring effect on internal software quality attributes, notably coupling. High coupling between classes is one of the smells considered dangerous by developers because of its impact, decreasing code maintainability. Some developers believe that refactoring as a method that can improve maintainability by reducing coupling in the source code. Evaluation of the effect of refactoring is done in the experiment way. When data collection from the experiment is done, then statistical analysis could be conducted to see whether refactoring can improve coupling or not. The results show that proper refactoring scenarios significantly reduce the coupling metric in source code, where 86% of samples have zero value of DCH, showing an apparent gain from refactoring. However, developers should pay attention to the code's size as refactoring results in increasing the code's size, especially in NOM, which has increased by 1.81%.
AB - It is commonly believed that refactoring increases software quality. This paper presents the validation of belief about refactoring effect on internal software quality attributes, notably coupling. High coupling between classes is one of the smells considered dangerous by developers because of its impact, decreasing code maintainability. Some developers believe that refactoring as a method that can improve maintainability by reducing coupling in the source code. Evaluation of the effect of refactoring is done in the experiment way. When data collection from the experiment is done, then statistical analysis could be conducted to see whether refactoring can improve coupling or not. The results show that proper refactoring scenarios significantly reduce the coupling metric in source code, where 86% of samples have zero value of DCH, showing an apparent gain from refactoring. However, developers should pay attention to the code's size as refactoring results in increasing the code's size, especially in NOM, which has increased by 1.81%.
UR - http://www.scopus.com/inward/record.url?scp=85099769397&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS51025.2020.9263167
DO - 10.1109/ICACSIS51025.2020.9263167
M3 - Conference contribution
AN - SCOPUS:85099769397
T3 - 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
SP - 463
EP - 470
BT - 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
Y2 - 17 October 2020 through 18 October 2020
ER -