Modeling the power profile of mobile applications is a crucial activity to identify the causes behind energy leaks. To this aim, researchers have proposed hardware-based tools as well as model-based and software-based techniques to approximate the actual energy profile. However, all these solutions present their own advantages and disadvantages. Hardware-based tools are highly precise, but at the same time their use is bound to the acquisition of costly hardware components. Model-based tools require the calibration of parameters needed to correctly create a model on a specific hardware device. Software-based approaches do not need any hardware components, but they rely on battery measurements and, thus, they are hardware-assisted. These tools are cheaper and easier to use than hardware-based tools, but they are believed to be less precise. In this paper, we take a deeper look at the pros and cons of software-based solutions investigating to what extent their measurements depart from hardware-based solutions. To this aim, we propose a softwarebased tool named PETRA that we compare with the hardwarebased MONSOON toolkit on 54 Android apps. The results show that PETRA performs similarly to MONSOON despite not using any sophisticated hardware components. In fact, in all the apps the mean relative error with respect to MONSOON is lower than 0:05. Moreover, for 95% of the analyzed methods the estimation error is within 5% of the actual values measured using the hardware-based toolkit.@en

Energy efficiency is a vital characteristic of any mobile application, and indeed is becoming an important factor for user satisfaction. For this reason, in recent years several approaches and tools for measuring the energy consumption of mobile devices have been proposed. Hardware-based solutions are highly precise, but at the same time they require costly hardware toolkits. Model-based techniques require a possibly difficult calibration of the parameters needed to correctly create a model on a specific hardware device. Finally, software-based solutions are easier to use, but they are possibly less precise than hardware-based solution. In this demo, we present PETrA, a novel software-based tool for measuring the energy consumption of Android apps. With respect to other tools, PETrA is compatible with all the smartphones with Android 5.0 or higher, not requiring any device specific energy profile. We also provide evidence that our tool is able to perform similarly to hardware-based solutions.@en

Code smells are symptoms of poor design solutions applied by programmers during the development of software systems. While the research community devoted a lot of effort to studying and devising approaches for detecting the traditional code smells defined by Fowler, little knowledge and support isavailable for an emerging category of Mobile app code smells. Recently, Reimann et al. proposed a new catalogue of Androidspecific code smells that may be a threat for the maintainability and the efficiency of Android applications. However, current tools working in the context of Mobile apps provide limited support and, more importantly, are not available for developers interested in monitoring the quality of their apps. To overcome these limitations, we propose a fully automated tool, coined ADOCTOR, able to identify 15 Android-specific code smells from the catalogue by Reimann et al. An empirical study conductedon the source code of 18 Android applications reveals that the proposed tool reaches, on average, 98% of precision and 98% of recall. We made ADOCTOR publicly available.@en

Regression testing is performed during maintenance activities to assess whether the unchanged parts of a software behave as intended. To reduce its cost, test case prioritization techniques can be used to schedule the execution of the available test cases to increase their ability to reveal regression faults earlier. Optimal test ordering can be determined using various techniques, such as greedy algorithms and meta-heuristics, and optimizing multiple fitness functions, such as the average percentage of statement and branch coverage. These fitness functions condense the cumulative coverage scores achieved when incrementally running test cases in a given ordering using Area Under Curve (AUC) metrics. In this paper, we notice that AUC metrics represent a bi-dimensional (simplified) version of the hypervolume metric, which is widely used in many-objective optimization. Thus, we propose a Hypervolume-based Genetic Algorithm, namely HGA, to solve the Test Case Prioritization problem when using multiple test coverage criteria. An empirical study conducted with respect to five state-of-the-art techniques shows that (i) HGA is more cost-effective, (ii) HGA improves the efficiency of Test Case Prioritization, (iii) HGA has a stronger selective pressure when dealing with more than three criteria.@en

Context. The demand for green software design is steadily growing higher especially in the context of mobile devices, where the computation is often limited by battery life. Previous studies found how wrong programming solutions have a strong impact on the energy consumption. Objective. Despite the efforts spent so far, only a little knowledge on the influence of code smells, i.e.,symptoms of poor design or implementation choices, on the energy consumption of mobile applications is available. Method. To provide a wider overview on the relationship between smells and energy efficiency, in this paper we conducted a large-scale empirical study on the influence of 9 Android-specific code smells on the energy consumption of 60 Android apps. In particular, we focus our attention on the design flaws that are theoretically supposed to be related to non-functional attributes of source code, such as performance and energy consumption. Results. The results of the study highlight that methods affected by four code smell types, i.e.,Internal Setter, Leaking Thread, Member Ignoring Method, and Slow Loop, consume up to 87 times more than methods affected by other code smells. Moreover, we found that refactoring these code smells reduces energy consumption in all of the situations. Conclusions. Based on our findings, we argue that more research aimed at designing automatic refactoring approaches and tools for mobile apps is needed.@en

Obtaining a good dataset to conduct empirical studies on the engineering of Android apps is an open challenge. To start tackling this challenge, we present AndroidTimeMachine, the first, self-contained, publicly available dataset weaving spread-out data sources about real-world, open-source Android apps. Encoded as a graph-based database, AndroidTimeMachine concerns 8,431 real open-source Android apps and contains: (i) metadata about the apps' GitHub projects, (ii) Git repositories with full commit history and (iii) metadata extracted from the Google Play store, such as app ratings and permissions.@en