How Does Code Obfuscation Impact Energy Usage?

Sahin, Cagri; Tornquist, Philip; McKenna, Ryan; Pearson, Zachary; Clause, James

doi:10.1109/icsme.2014.35

Cited by 40 publications

(11 citation statements)

References 19 publications

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“…Other researchers focused their attention on the relationship between the development practices adopted by programmers and the energy consumption. Sahin et al [33] showed that code obfuscation can negatively affect energy consumption, but the observed difference with non-obfuscated code unlikely impacts end-users. The same authors have also reported on an analysis of the role of design patterns [5].…”

Section: B Empirical Studies In Green Software Engineeringmentioning

confidence: 99%

Software-based energy profiling of Android apps: Simple, efficient and reliable?

Nucci

Palomba

Prota

et al. 2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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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 software-based tool named PETRA that we compare with the hardware-based 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

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Section: B Empirical Studies In Green Software Engineeringmentioning

confidence: 99%

Software-based energy profiling of Android apps: Simple, efficient and reliable?

Nucci

Palomba

Prota

et al. 2017

2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…Doing so allows software producing organizations to consider sustainability aspects during the design of the software, i.e. with its software architecture, and make trade-offs with other quality aspects (e.g [16]). However, analogous to the other quality attributes [17], working on software quality could require significant investments in terms of time, specialized knowledge to address specific issues and analysis across architectural views [18].…”

Section: A Addressing Software Sustainabilitymentioning

confidence: 99%

A Resource Utilization Score for Software Energy Consumption

Jagroep¹,

Werf²,

Broekman³

et al. 2016

Proceedings of ICT for Sustainability 2016

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Abstract-Software as the true consumer of power and its potential contribution to reach sustainability goals is increasingly being acknowledged. Studies so far have presented successful results and methods to address the energy consumption of the software, indicating that different stakeholders striving for green software have different information needs with respect to their goals. However, currently there is no uniform manner to communicate measurements to the different stakeholders such that key findings are clearly identifiable and easy to understand, which is likely to hamper green software practices. In this paper we propose a metric that expresses a score for the resource utilization, such as power consumption, of a software product. The metric is designed to be a single score and is flexible to encompass those aspects that a stakeholder considers relevant in the context of software energy consumption. The metric was applied on two applications and allowed for objective comparison of application configurations and versions. Also the behavior of these applications across different hardware configurations could be analyzed. In addition to the metric we investigate means to visualize measurements which enhances communication and helped with highlighting the key findings.

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“…Studies have shown that the energy consumption of a software system can be significantly influenced by a lot of factors. Such factors can be software development related, such as code obfuscation [20], different design patterns or coding practices [1,8,11,18], refactorings [19], and even the usage of different data structures [12,16]. Even in software testing the decisions made can influence the overall energy consumption at the testing phase [10].…”

Section: Related Workmentioning

confidence: 99%