Comparative Study of Manual and Generated Data Transfer Object Implementation Performance

The Data Transfer Object (DTO) is a fundamental component in Flutter application development, particularly in managing data serialization and deserialization. This study compares two DTO implementation methods—manual and generated—focusing on execution speed and memory efficiency. Testing was conduc...

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Bibliographic Details
Published in:Journal of Applied Informatics and Computing Vol. 9; no. 5; pp. 2912 - 2919
Main Authors: Pardede, Chandro, Sihombing, Wilson, Nainggolan, Winfrey
Format: Journal Article
Language:English
Published: Politeknik Negeri Batam 21.10.2025
Subjects:
data transfer object
flutter
json serializable
parsing
ISSN:2548-6861, 2548-6861
Online Access:Get full text
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Summary:The Data Transfer Object (DTO) is a fundamental component in Flutter application development, particularly in managing data serialization and deserialization. This study compares two DTO implementation methods—manual and generated—focusing on execution speed and memory efficiency. Testing was conducted across three levels of data complexity (Small, Medium, and Large) over 100 iterations using Flutter DevTools. The findings reveal that the generated approach (utilizing libraries such as json_serializable) consistently outperforms the manual approach. Specifically, it achieves a 1:1.147 ratio in parsing speed and a 1:1.42 ratio in memory efficiency compared to manual DTOs. Although the manual method provides greater flexibility for implementing conditional parsing logic, it tends to be more error-prone and less efficient when handling large datasets. In contrast, the generated approach offers faster performance, better scalability, and reduced human error potential, making it the preferred option for projects demanding technical efficiency and rapid development cycles. Consequently, this study recommends adopting generated DTOs for applications dealing with large-scale and complex data, while reserving manual DTOs for cases requiring highly dynamic or conditional data parsing.
ISSN:2548-6861
2548-6861
DOI:10.30871/jaic.v9i5.10818