J2EE applications tend to be multi-tier and multi-language applications. They rely on the J2EE platform and containers that offer infrastructure and architectural services to ensure distributed, secure, safe, and scalable executions. These mechanisms hide many program dependencies, which helps development but hinders maintenance, evolution, and re-engineering of J2EE applications. In this paper, we study (i) the J2EE specifications to extract a declarative specification of the dependencies that are inherent in the services offered and that are not visible in the user code that uses them. Then, we introduce (ii) a codification of the dependencies into rules, and (iii) a tool that supports the specification of those dependencies and their detection in J2EE applications. We validate our approach and tool on a sample of 10 J2EE applications. We also compare our tool against JRipples, a state-of-the-art tool for change-impact analysis tasks. Results show that our tool adds, on average, 15% more call dependencies, which would have been missed otherwise. On change impact analysis tasks, our tool outperforms JRipples in all 10 applications, especially for the early iterations of change propagation exploration.

Android code smells are bad implementation practices within Android applications (or apps) that may lead to poor software quality. These code smells are known to degrade the performance of apps and to have an impact on energy consumption. However, few studies have assessed the positive impact on energy consumption when correcting code smells. In this paper, we therefore propose a tooled and reproducible approach, called HOT-PEPPER, to automatically correct code smells and evaluate their impact on energy consumption. Currently, HOT-PEPPER is able to automatically correct three types of Android-specific code smells: Internal Getter/Setter, Member Ignoring Method, and HashMap Usage. HOT-PEPPER derives four versions of the apps by correcting each detected smell independently, and all of them at once. HOT-PEPPER is able to report on the energy consumption of each app version with a single user scenario test. Our empirical study on five open-source Android apps shows that correcting the three aforementioned Android code smells effectively and significantly reduces the energy consumption of apps. In particular, we observed a global reduction in energy consumption by 4,83% in one app when the three code smells are corrected. We also take advantage of the flexibility of HOT-PEPPER to investigate the impact of three picture smells (bad picture format, compression, and bitmap format) in sample apps. We observed that the usage of optimised JPG pictures with the Android default bitmap format is the most energy efficient combination in Android apps. We believe that developers can benefit from our approach and results to guide their refactoring, and thus improve the energy consumption of their mobile apps.

Mobile applications are becoming complex software systems that must be developed quickly and evolve regularly to fit new user requirements and execution contexts. However, addressing these constraints may result in poor design choices, known as antipatterns, which may degrade software quality and performance. Thus, the automatic detection of antipatterns is an important activity that eases the future maintenance and evolution tasks. Moreover, it helps developers to refactor their applications and thus, to improve their quality. While antipatterns are well-known in object-oriented applications, their study in mobile applications is still in their infancy. In this paper, we presents a tooled approach, called Paprika, to analyze Android applications and to detect object-oriented and Android-specific antipatterns from binaries of applications.

Mobile apps are becoming complex software systems that must be developed quickly and evolve continuously to fit new user requirements and execution contexts. However, addressing these requirements may result in poor design choices, also known as antipatterns, which may incidentally degrade software quality and performance. Thus, the automatic detection and tracking of antipatterns in this apps are important activities in order to ease both maintenance and evolution. Moreover, they guide developers to refactor their applications and thus, to improve their quality. While antipatterns are well-known in object-oriented applications, their study in mobile applications is still in its infancy. In this paper, we analyze the evolution of mobile apps quality on 3, 568 versions of 106 popular Android applications downloaded from the Google Play Store. For this purpose, we use a tooled approach, called PAPRIKA, to identify 3 object-oriented and 4 Android-specific antipatterns from binaries of mobile apps, and to analyze their quality along evolutions.

Cloud patterns are described as good solutions to recurring design problems in a cloud context. These patterns are often inherited from Service Oriented Architectures or Object Oriented Architectures where they are considered good practices. However, there is a lack of studies that assess the benefits of these patterns for cloud applications. In this paper, we conduct an empirical study on a Restful application deployed in the cloud, to investigate the individual and the combined impact of three cloud patterns (i.e., Local Database proxy, Local Sharding-Based Router and Priority Queue Patterns) on Quality of Service (QoS). We measure the QoS using the application's response time, average, and maximum number of requests processed per seconds. Results show that cloud patterns doesn't always improve the response time of an application. In the case of the Local Database proxy pattern, the choice of algorithm used to route requests has an impact on response time, as well as the average and maximum number of requests processed per second. Combinations of patterns can significantly affect the QoS of applications. Developers and software architects can make use of these results to guide their design decisions.