GreenBundle: An Empirical Study on the Energy Impact of Bundled Processing

Chowdhury, Shaiful Alam; Hindle, Abram; Kazman, Rick; Shuto, T.; Matsui, Kazuma; Kamei, Yasutaka

doi:10.1109/icse.2019.00114

Cited by 12 publications

(14 citation statements)

References 50 publications

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“…To estimate the energy consumption we use two different softwarebased tools, GreenScaler and Trepn Profiler. We have chosen these tools because they allow the energy consumption of mobile applications to be estimated, with an upper error bound of less than 10% [34,37] and they have been used in other studies on energy consumption [38][39][40] . Although hardware-based tools provide more precise results, it is not easy to identify the part of the software responsible for this consumption using them.…”

Section: Energy Profilementioning

confidence: 99%

“…Recently, current approaches [40,[50][51][52]54] have tried to optimize the energy consumption of the mobile applications following a refactoring approach, without an explicit dynamic reconfiguration of the application. In contrast to most of the classical reconfiguration approaches that adapt the application at runtime, these approaches refactor the code of the application at compile time.For example, Banerjee et al [50] they use a refactoring technique that relies on a set of energy-efficiency guidelines to encode the optimal usage of energy-intensive hardware resources in an Android application, reducing the energy consumption of the applications by between 3% to 29%.…”

Section: Related Workmentioning

confidence: 99%

“…Refactoring can be applied to different level of abstraction: from low level implementation details [51] , to the design level [52,54] or to the architectural level [40] . For instance, Sahin et al [51] explore the energy impacts of performance tips, and they demonstrate that those changes in the code are unlikely to impact the energy usage in a statistically significant manner.…”

Section: Related Workmentioning

confidence: 99%

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Energy efficient adaptation engines for android applications

Cañete

Horcas

Ayala

et al. 2020

Information and Software Technology

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ContextThe energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application.Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained.Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption. ResultsThe proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%. ConclusionThe adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.

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Section: Energy Profilementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Energy efficient adaptation engines for android applications

Cañete

Horcas

Ayala

et al. 2020

Information and Software Technology

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show abstract

“…Several attempts have been made to adopt software refactoring techniques to change the energy‐hungry code 13 . The recent evidence shows the importance of considering energy‐related issues as antipatterns at the source code level 8,14 . Recently Cruz et al 9,15 and Palomba et al 16 investigated the impact of design patterns and traditional object‐oriented code smells respectively on the energy consumption of Android applications.…”

Section: Introductionmentioning

confidence: 99%

“…13 The recent evidence shows the importance of considering energy-related issues as antipatterns at the source code level. 8,14 Recently Cruz et al 9,15 and Palomba et al 16 investigated the impact of design patterns and traditional object-oriented code smells respectively on the energy consumption of Android applications. Notably, Morales et al 8 highlighted that the Android-specific antipatterns result in abnormal battery-drain.…”

mentioning

confidence: 99%

How do open source app developers perceive API changes related to Android battery optimization? An empirical study

2020

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There is an increasing interest shown by researchers and developers in reducing the battery consumption of Android applications. Recently, the battery optimization features such as doze mode, app standby, background execution limits, and background location limits were introduced in the form of API changes. According to the API changes, application developers have to change their source code to manage the behavioral changes caused by operating system limitations. These battery optimization features are evolving rapidly, and the apps show unexpected behaviors until updating the source code. Also, developers find it difficult to cope with the changes. Therefore, there is a need to understand the behavioral changes, application developer's perceptions, and response patterns on the API changes to plan upcoming battery optimization features. In this article, we have collected the relevant GitHub issues from 225 open‐source Android repositories and performed a thematic analysis of collected data. This study analyzes the 391 related issues to answer three research questions. This study's important finding is that developers often post issues related to delayed app notifications, inconsistent background location updates, and suspended background tasks, and so on. We found that library developers are showing a quick response to API changes compared with application developers.

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Tales from the Code #2: A Detailed Assessment of Code Refactoring’s Impact on Energy Consumption

Ournani

Rouvoy

Rust

et al. 2022

Communications in Computer and Information Science

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Energy consumption has been a prominent question in the last decade that concerns both hardware and software dimensions. Source code refactoring is a widespread activity among developers that includes a set of well-known changes to improve the code quality without impacting the functional aspects. Hence, the concern of the impact that may those changes induce on the software energy consumption is legitimate, in order to identify whether and which refactorings have a significant impact on the evolution of the energy consumption. In particular, while the state of the art investigated the impact of some specific code refactorings on dedicated benchmarks, we miss an assessment that those apply to more comprehensive and complex software. To address this threat, this paper studies the evolution of the energy consumption of 7 open-source software developed for more than 5 years. Then, by focusing on the impact on energy consumption of changes involving code refactorings, we intend to assess the effects induced by computational code refactorings. For all these software systems we studied, our empirical results report that the code refactorings we mined do not substantially impact energy consumption. Interestingly, these results highlight that i) structural code refactorings bring energy-preserving changes to the code, and ii) major energy variations seem to be related to computational code refactorings and/or functional changes.

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GreenBundle: An Empirical Study on the Energy Impact of Bundled Processing

Cited by 12 publications

References 50 publications

Energy efficient adaptation engines for android applications

Energy efficient adaptation engines for android applications

How do open source app developers perceive API changes related to Android battery optimization? An empirical study

Tales from the Code #2: A Detailed Assessment of Code Refactoring’s Impact on Energy Consumption

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