Scenarios for the evaluation of the energy efficiency of mobile applications

Ivanov, Vladimir; Kruglov, Artem; Sadovykh, Andrey; Succi, Giancarlo

doi:10.1109/iemcon.2019.8936235

Cited by 4 publications

(6 citation statements)

References 38 publications

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“…In our previous work [15], a taxonomy and some scenarios for the development of mobile applications were introduced. However, it cannot be properly extended to other relevant areas to be considered-such as cloud computing or embedded systems.…”

Section: Classification Tree Developmentmentioning

confidence: 99%

“…The principle of system function classification fits well with the given project. Previously we developed a classification for mobile applications based on the categories from Google Play Store and App Store [15]. In this research that classification will be expanded to cover all types of computer systems.…”

Section: Classification Tree Developmentmentioning

confidence: 99%

“…The next stage in the traditional SDLC process is the implementation, i.e., the coding stage. In the previous work [15] we considered development tools (IDE) as one of the aspects that have an impact on energy efficiency. However, we did not find any evidence of this hypothesis in the literature, thus we focused on another issue to be investigated at this stage-the choice of the programming language.…”

Section: Classification Tree Developmentmentioning

confidence: 99%

“…Thus, for the developing energy-efficiency taxonomy, the processing principle was selected as the criterion for the classification of programming languages. In our previous research [15] we analyzed energy efficiency as a software quality attribute. Based on this, we added the parameter of SDLC to the final taxonomy, as Inukollu et al [23] argued about the impact of the development lifecycle on the software quality.…”

Section: Classification Tree Developmentmentioning

confidence: 99%

“…Since the focus of our research is energy efficiency in mobile, cloud, and embedded systems, we performed decomposition of two categories in the "system type" branch: primary client, which includes mobile systems, and cloud/datacenter system. The former is augmented with our taxonomy for mobile applications [15], and for the latter, some of the criteria from the work by Beloglazov et al [16] were chosen. We referred to the datacenter level taxonomy, so for this topology type the parameters virtualization, workload, and target systems type are added.…”

Section: Classification Tree Developmentmentioning

confidence: 99%

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Elaborating Validation Scenarios Based on the Context Analysis and Combinatorial Method: Example of the Power-Efficiency Framework Innomterics

et al. 2020

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The preliminary task of a project consists of the definition of the scenarios that will guide further development work and validate the results. In this paper, we present an approach for the systematic generation of validation scenarios using a specifically developed taxonomy and combinatorial testing. We applied this approach to our research project for the development of the energy-efficiency evaluation framework named Innometrics. We described in detail all steps for taxonomy creation, generation of abstract validation scenarios, and identification of relevant industrial and academic case studies. We created the taxonomy of the target computer systems and then elaborated test cases using combinatorial testing. The classification criteria were the type of the system, its purpose, enabling hardware components and connectivity technologies, basic design patterns, programming language, and development lifecycle. The combinatorial testing results in 13 cases for one-way test coverage, which was considered enough to create a comprehensive test suite. We defined the case study for each particular scenario. These case studies represent the real industrial, educational, and open-source software development projects that will be used in further work on the Innometrics project.

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Section: Classification Tree Developmentmentioning

confidence: 99%

Section: Classification Tree Developmentmentioning

confidence: 99%

Section: Classification Tree Developmentmentioning

confidence: 99%

Section: Classification Tree Developmentmentioning

confidence: 99%

Section: Classification Tree Developmentmentioning

confidence: 99%

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