Helping Programmers Improve the Energy Efficiency of Source Code

Pereira, Rui; Carção, Tiago; Couto, Marco; Cunha, Jácome; Fernandes, João Paulo; Saraiva, João

doi:10.1109/icse-c.2017.80

Cited by 27 publications

(31 citation statements)

References 22 publications

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“…Other studies aimed at a more extensive energy consumption analysis, by comparing the energy efficiency of similar programs in specific usage scenarios [4,17], or by providing conclusions on the energy impact of different implementation decisions [7]. Several other works have shown that several factors, such as different design patterns [22,31], coding practices [21,26,29,32], and data structures [15,23,24,27], actually have a significant influence in the software's energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

“…In fact, this is a recent and intensive area of research where several techniques to analyze and optimize the energy consumption of software systems are being developed. Such techniques already provide knowledge on the energy efficiency of data structures [15,27] and android language [25], the energy impact of different programming practices both in mobile [18,22,31] and desktop applications [26,32], the energy efficiency of applications within the same scope [2,17], or even on how to predict energy consumption in several software systems [4,14], among several other works.…”

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confidence: 99%

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Energy efficiency across programming languages: how do energy, time, and memory relate?

Pereira

Couto

Ribeiro

et al. 2017

Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering

Self Cite

131

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This paper presents a study of the runtime, memory usage and energy consumption of twenty seven well-known software languages. We monitor the performance of such languages using ten different programming problems, expressed in each of the languages. Our results show interesting findings, such as, slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. We show how to use our results to provide software engineers support to decide which language to use when energy efficiency is a concern.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

Energy efficiency across programming languages: how do energy, time, and memory relate?

Pereira

Couto

Ribeiro

et al. 2017

Proceedings of the 10th ACM SIGPLAN International Conference on Software Language Engineering

Self Cite

131

View full text Add to dashboard Cite

show abstract

“…The research which relate the closest to our study are the ones of Pereira et al [31,32]. In their studies, a tool under development named SPELL is presented.…”

Section: Related Workmentioning

confidence: 88%

“…Additionally, our approach does not rely on the coefficient of test case similarity, but instead evenly distributes the energy consumption among the items covered by the test cases. While the goal of the SPELL tool [31,32] lies in providing the means to precisely locate energy hotspots in source code, our study aims to investigate if more naive approaches can be used to effectively locate them. This enables us to lie the groundwork to accurately understand through empirical experimentation the magnitude of the tradeoff that lies between performance and precision of spectrum based energy hotspot localization.…”

Section: Related Workmentioning

confidence: 99%

Code-Level Energy Hotspot Localization via Naive Spectrum Based Testing

et al. 2018

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With the growing adoption of ICT solutions, developing energy efficient software becomes increasingly important. Current methods aimed at analyzing energy demanding portions of code, referred to as energy hotspots, often require adhoc analyses that constitute an additional process in the development life cycle. This leads to the scarce adoption of such methods in practice, leaving an open gap between source code energy optimization research and its concrete application. Thus, our underlying goal is to provide developers with a technique that enables them to efficiently gather source code energy consumption information without requiring excessive time overhead and resources. In this research we present a naive spectrumbased fault localization technique aimed to efficiently locate energy hotspots. More specifically, our research aims to understand the viability of spectrum based energy hotspot localization and the tradeoffs which can be made between performance and precision for such techniques. Our naive yet effective approach takes as input an application and its test suite, and utilizes a simple algorithm to localize portions of code which are potentially energy-greedy. This is achieved by combining test case coverage information with runtime energy consumption measurements. The viability of the approach is assessed through an empirical experiment. We conclude that the naive spectrum based energy hotspot localization approach can effectively support developers by efficiently providing insights of the energy consumption of software at source code level. Since we use processes already in place in most companies and adopt straightforward data analysis processes, naive spectrum based energy hotspot localization can reduce the effort and time required for assessing energy consumption of software and thus make including the energy consumption in the development process viable. As future work we plan to (i) further investigate the tradeoffs between performance and precision of spectrum based energy hotspot approaches (ii) compare our approach to similar ones through large-scale experiments. Our ultimate goal is to conceive ad-hoc tradeoff tuning of performance and precision according to development and organizational needs.

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“…Apigee's survey on the reasons leading to bad mobile app reviews: https://cloud.google.com/apigee/news (visited on September 10, 2019) 2. High Risk Android Apps: https://www.verizonwireless.com/ support/services-and-apps/ (visited on September 10, 2019) 3. Tap is a gesture in which a user taps the screen with his finger.…”

Section: Introductionmentioning

confidence: 99%

On the Energy Footprint of Mobile Testing Frameworks

Cruz

Abreu

2021

IIEEE Trans. Software Eng.

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High energy consumption is a challenging issue that an ever increasing number of mobile applications face today. However, energy consumption is being tested in an ad hoc way, despite being an important non-functional requirement of an application. Such limitation becomes particularly disconcerting during software testing: on the one hand, developers do not really know how to measure energy; on the other hand, there is no knowledge as to what is the energy overhead imposed by the testing framework. In this paper, as we evaluate eight popular mobile UI automation frameworks, we have discovered that there are automation frameworks that increase energy consumption up to roughly 2200%. While limited in the interactions one can do, Espresso is the most energy efficient framework. However, depending on the needs of the tester, Appium, Monkeyrunner, or UIAutomator are good alternatives. In practice, results show that deciding which is the most suitable framework is vital. We provide a decision tree to help developers make an educated decision on which framework suits best their testing needs.

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Helping Programmers Improve the Energy Efficiency of Source Code

Cited by 27 publications

References 22 publications

Energy efficiency across programming languages: how do energy, time, and memory relate?

Energy efficiency across programming languages: how do energy, time, and memory relate?

Code-Level Energy Hotspot Localization via Naive Spectrum Based Testing

On the Energy Footprint of Mobile Testing Frameworks

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