Code Smells and Development Efficiency in Native and Cross-Platform Mobile Applications: A Study of Java, Kotlin, and Flutter
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While many methods have been proposed for creating mobile applications, developers have struggled to decide which is best. This study contrasts native and cross-platform application development methodologies, paying special attention to the growing popularity of Flutter and the trend away from Java in favor of Kotlin. Using Java, Kotlin, and Dart (Flutter) to create identical applications, this research provides useful insights into factors influencing developers’ choice of programming languages and frameworks in mobile application development. In addition, this research investigates development best practices by analyzing the quality of the code in 45 public GitHub repositories. The study measures the impact of choosing a particular language or framework on code smells and development efficiency by evaluating LOC and code smells using semi-automated SonarQube assessments, which include the measurement of severity levels. Preliminary findings show differences in the code quality produced by the two approaches, offering developers useful information on reducing code smells and improving project quality.
Abstract
While many methods have been proposed for creating mobile applications, developers have struggled to decide which is best. This study contrasts native and cross-platform application development methodologies, paying special attention to the growing popularity of Flutter and the trend away from Java in favor of Kotlin. Using Java, Kotlin, and Dart (Flutter) to create identical applications, this research provides useful insights into factors influencing developers’ choice of programming languages and frameworks in mobile application development. In addition, this research investigates development best practices by analyzing the quality of the code in 45 public GitHub repositories. The study measures the impact of choosing a particular language or framework on code smells and development efficiency by evaluating LOC and code smells using semi-automated SonarQube assessments, which include the measurement of severity levels. Preliminary findings show differences in the code quality produced by the two approaches, offering developers useful information on reducing code smells and improving project quality.