Showing papers on "Java annotation published in 2020"

Annotation practices in Android apps

Abstract: Understanding the adoption and usage of any programming language feature is crucial for improving it. Existing studies indicate that Java annotations are widely used by developers. However, there is currently no empirical data on annotation usage in Android apps. Android apps are often smaller than general Java applications and typically use Android APIs or specific libraries catered to the mobile environment. Therefore, it is not clear if the results of existing Java studies hold for Android apps. In this paper, we investigate annotation practices in Android apps through an empirical study of 1,141 open-source apps. Using previously studied metrics, we first compare annotation usage in Android apps to existing results from general Java applications. Then, for the first time, we study why developers declare custom annotations. Our results show that the density of annotations and the values of various other annotation metrics are notably less in Android apps than in Java projects. Additionally, the types of annotations used in Android apps are different than those in Java, with many Android-specific annotations. These results imply that researchers may need to distinguish mobile apps while performing studies on programming language features. However, we also found examples of extreme usage of annotations with, for example, a large number of attributes, as well as a low adoption rate for most annotations. By looking at such results, annotation designers can assess adoption patterns and take various improvement measures, such as modularizing their offered annotations or cleaning up unused ones. Finally, we find that developers declare custom annotations in different apps but with the same purpose, which presents an opportunity for annotation designers to create new annotations.

Contract-based design patterns: a design by contract approach to specify security patterns

Abstract: With the ever growing digitization of activities, software systems are getting more and more complex. They must comply with new usages, varied needs, and are permanently exposed to new security vulnerabilities. Security concerns must be addressed throughout the entire development process and in particular through appropriate architectural choices. The security patterns are the founding principles to provide the architectural and design guidelines. Nevertheless, researchers have pointed out the need for further research investigations to improve quality and effectiveness of security patterns. In this paper, we focus on enhancing security patterns specification to improve the security of the systems using them. Thus, to reach this goal, we present a formal Design by Contract approach to improve the behavioral definition of the security patterns. This approach seeks to define both functional behavior and implicit parts of security design patterns. Our approach includes the contract formalization of security patterns and a comparative implementation on two Java annotation frameworks. The application of the proposal in a proof of concept case highlights the security enforcement at design time or on a legacy source code.

MCAF: Developing an Annotation-Based Offloading Framework for Mobile Cloud Computing

Abstract: Offloading computation from mobile to remote cloud servers is a promising way to reduce energy consumption and improve the performance of mobile applications. However, a great challenge arises as automatic integration of powerful computing resources in remote cloud infrastructure and the portability of mobile devices. In this paper, we develop a Java annotation-based offloading framework, called MCAF, for android mobile devices. This framework is designed and committed to simplifying the development of android applications enabled with the offload capability. All the developers need to do is to import the SDK library of our MCAF and annotate the computation-intensive methods. MCAF can automatically extract the annotated source code and generate the code that will be run in the Cloud. Moreover, the codes of making the offloading decisions are automatically inserted into the original source code. We also conducted the real experiments to show the applicability of our MCAF.