Bruno Góis Mateus

Bruno Góis Mateus, Matias Martinez, Christophe Kolski

Context: A Legacy system can be defined as a system that significantly resists modification and evolution. According to the literature, there are two main strategies to migrate a legacy system: (a) to replace the legacy system by a new one, (b) to incrementally migrate parts from the legacy system to the new one. Incremental migration allows developers to better control the risks that may occur during the migration process. However, this strategy is more complex because it requires decomposition of the legacy system into different parts, e.g. a set of files, and to define the order of migration of them along the migration process. To our knowledge, there is no approach to support developers on those activities. Objective: This paper presents an approach, named MigrationExp, to support incremental language migrations of applications from one source language to another target language. MigrationExp recommends the files that should be migrated first in a particular migration iteration. As a novelty, our approach relies on a ranking model learned, using a learning-to-rank algorithm, from migrations made by developers. Method: We validate our approach in the context of the migrations of Android apps, from Java to Kotlin, a new official language for Android. We train our model using migrations of Java code to Kotlin written by developers on open-source applications. Results: The results show that, on the task of proposing files to migrate, our approach outperforms a previous migration strategy proposed by Google, in terms of its ability to accurately predict empirically observed migration orders. Conclusion: Since most Android applications are written in Java, we conclude that approaches to support developers such as MigrationExp may significantly impact the development of Android applications.

Bruno Góis Mateus, Matias Martinez

Background: Google announced Kotlin as an Android official programming language in 2017, giving developers an option of writing applications using a language that combines object-oriented and functional features. Aims: The goal of this work is to understand the usage of Kotlin features considering four aspects: i) which features are adopted, ii) what is the degree of adoption, iii)when are these features added into Android applications for the first time, and iv) how the usage of features evolves along with applications' evolution. Method: Exploring the source code of 387 Android applications, we identify the usage of Kotlin features on each version application's version and compute the moment that each feature is used for the first time. Finally, we identify the evolution trend that better describes the usage of these features. Results: 15 out of 26 features are used on at least 50% of applications. Moreover, we found that type inference, lambda and safe call are the most used features. Also, we observed that the most used Kotlin features are those first included on Android applications. Finally, we report that the majority of applications tend to add more instances of 24 out of 26 features along with their evolution. {\bf Conclusions:} Our study generates 7 main findings. We present their implications, which are addressed to developers, researchers and tool builders in order to foster the use of Kotlin features to develop Android applications.