Analysis of Energy Consumption of Software Development Process Entities

Ciancarini, Paolo; Ergasheva, Shokhista; Kholmatova, Zamira; Kruglov, Artem; Succi, Giancarlo; Vasquez, Xavier; Zuev, Evgeniy

doi:10.3390/electronics9101678

Cited by 18 publications

(8 citation statements)

References 28 publications

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“…Finally, improved code understanding and the visual representation of the source code structure enhance developers' comprehension, enabling them to make informed design decisions and reducing the energy spent on rework and refactoring. [22] In conclusion, this study proposes a novel method for automatically producing class diagrams from source code using Graph Neural Networks. The suggested method effectively substitutes time-energy-consuming and error-prone manual class diagram construction work by utilizing AI and reverse engineering approaches.…”

Section: Discussionmentioning

confidence: 98%

A methodology of automatic class diagrams generation from source code using Model-Driven Architecture and Machine Learning to achieve Energy Efficiency

Sajji,

Rhazali,

Hadi

2023

E3S Web Conf.

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The automated generation of class diagrams is a crucial task in software engineering, facilitating the understanding, analysis, and documentation of complex software systems. Traditional manual approaches are time and energy consuming, error-prone, and lack consistency. To address these challenges, this research presents an automated proposed approach that utilizes Graph Neural Networks (GNNs), a machine learning algorithm, to generate class diagrams from source code within the context of Model Driven Architecture (MDA) and reverse engineering. A comprehensive case study is conducted to compare the results obtained from the automated approach with manually created class diagrams. The GNN model demonstrates high accuracy in capturing the system’s structure, associations, and relationships. Notably, the automated approach significantly reduces the time required for class diagram generation, leading to substantial time and energy savings. By advancing automated software documentation, this research contributes to more efficient software engineering practices. It promotes consistency, eliminates human errors, and enables software engineers to focus on higher-value tasks. Overall, the proposed approach showcases the potential of GNNs in automating class diagram generation and its practical benefits for software development and documentation.

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Section: Discussionmentioning

confidence: 98%

A methodology of automatic class diagrams generation from source code using Model-Driven Architecture and Machine Learning to achieve Energy Efficiency

Sajji,

Rhazali,

Hadi

2023

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Initially, we have conducted three trials from the same source to destination using Google Fit, and the averaged distance of 3.06 km has been considered for the comparison. We used Haversine formula 11 for calculating the distance by collecting the coordinates of the previous and current locations and cumulatively adding the distance between every two locations for the entire trail.…”

Section: Answering Rq3: To What Extent Code Generated By Egen Degrade...mentioning

confidence: 99%

“…In recent years, software energy consumption is becoming a critical non-functional requirement for software developers [11,32,44]. The investigations by Pinto and Castor [49], and Manotas et al [35] highlight the significance of reducing software energy consumption.…”

Section: Introductionmentioning

confidence: 99%

eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps

Boyalakuntla¹,

C²,

Chimalakonda³

et al. 2022

Preprint

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The demand for reducing the energy consumption of location-based applications has increased in recent years. The abnormal battery-draining behavior of GPS makes it difficult for the developers to decide on battery optimization during the development phase directly. It will reduce the burden on developers if battery-saving strategies are considered early, and relevant battery-aware code is generated from the design phase artifacts. Therefore, we aim to develop tool support, eGEN, to specify and create native location-based mobile apps. eGEN consists of Domain-specific Modeling Language (DSML) and a code generator for location-sensing. It is developed using Xtext and Xtend as an Eclipse plug-in, and currently, it supports native Android apps. eGEN is evaluated through controlled experiments by instrumenting the generated code in five location-based open-source Android applications. The experimental results show 4.35 minutes of average GPS reduction per hour and 188 mA of average reduction in battery consumption while showing only 97 meters degrade in location accuracy over 3 kilometers of a cycling path. Hence, we believe that code generated by eGEN would help developers to balance between energy and accuracy requirements of location-based applications. The source code, documentation, tool demo video 1 , and tool installation video 2 are available at https://github.com/Kowndinya2000/egen.

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“…The use of a coherent system of metrics to evaluate energy consumption has been studied extensively in the literature [4,5]. The question of definition and application of metrics to evaluate the energy consumption is of great practical interest, and we have studied this issue in a companion paper recently published [6]. The system described in that paper-that we called InnoMetrics-aims at building and validating a quantitative framework to guide the development and the evolution of sustainable computer systems using a variety of metrics collected throughout the lifecycle of software products on different computer systems, from the initial concept to the deployment, execution, and maintenance, optimizing the performances of the products under a variety of nowadays relevant factors, including the efficient use of energy resources.…”

Section: Rationalementioning

confidence: 99%

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

et al. 2020

Self Cite

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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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Analysis of Energy Consumption of Software Development Process Entities

Cited by 18 publications

References 28 publications

A methodology of automatic class diagrams generation from source code using Model-Driven Architecture and Machine Learning to achieve Energy Efficiency

A methodology of automatic class diagrams generation from source code using Model-Driven Architecture and Machine Learning to achieve Energy Efficiency

eGEN: An Energy-saving Modeling Language and Code Generator for Location-sensing of Mobile Apps

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

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