Does maintainability relate to the energy consumption of software? A case study

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…Mancebo et al [22] developed a methodology where they tested a full software to evaluate and identify the areas of high energy consumption. They based their evaluation on maintainability of a software, which included number of lines used, comments and repetitive code, among other criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Applied code transformations and compared the energy consumptions between two versions of several programs before and after the modified SDLC models. Mancebo et al [22] Developed a framework that evaluates a software application on certain aspects in terms of energy efficiency.…”

Section: Consideredmentioning

confidence: 99%

“…In recent years, there have been numerous studies [11][12][13][14][15][16][17][18][19][20][21][22][23] that focused on energy efficiency in software development. These studies have investigated various aspects of programming languages, tools, and…”

Section: Introductionmentioning

confidence: 99%

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GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

Reya,

Ahmed,

Zaman

et al. 2023

AETiC

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The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Consideredmentioning

confidence: 99%

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GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

Reya,

Ahmed,

Zaman

et al. 2023

AETiC

View full text Add to dashboard Cite

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“…Mancebo et al [12] analyzed the relation between maintainability and energy consumption of different versions of Redmine. They computed the maintainability using Sonar-Qube and measured the energy consumption using an Energy Efficiency Tester (EET) appliance.…”

Section: Related Workmentioning

confidence: 99%

Can We Spot Energy Regressions using Developers Tests?

Danglot,

Falleri,

Rouvoy

2021

Preprint

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Background: Software Energy Consumption (SEC) is gaining more and more attention. In this paper, we tackle the problem of hinting developers about the SEC of their programs in the context of software developments based on Continuous Integration (CI).Objective: In this study, we investigate if the CI can leverage developers' tests to perform a new class of test: the energy regression testing. Energy regression is similar to performance regression, but focused on the energy consumption of the program instead of standard performance indicators, like execution time or memory consumption. Method: We propose to perform an exploratory study of the usage of developers' tests for energy regression testing. We propose to first investigate if developers' tests can be used to obtain stable SEC indicators. Then, to consider if comparing the SEC of developers' tests between two versions can accurately spot energy regressions introduced by automated program mutations. Finally, to assess if it can successfully pinpoint the source code lines guilty of energy regressions.Impact: Our study will pave the way for automated SEC regression tools that can be readily deployed inside an existing CI infrastructure to raise awareness of SEC issues among practitioners.

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“…These metrics are the oldest and most widely used size measurements [36,37]. LOC and CC can affect energy consumption and software quality [38]. As the dimension of CC increases, it becomes extremely difficult to test a software program [39].…”

mentioning

confidence: 99%

Energy Efficiency Analysis of Code Refactoring Techniques for Green and Sustainable Software in Portable Devices

2022

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Code refactoring is a time-consuming and effort-intensive process that is applied for making improvements to source codes. There exist several refactoring techniques to improve software quality. Some of them aim to reduce the energy consumption of the software. However, the combination of applied refactoring techniques is crucial to the success rate. In addition, to provide sustainable services on portable devices such as mobile phones and laptops, which rely on batteries, improving and optimizing the energy efficiency is important. This study focuses on examining the effect of code refactoring techniques on energy consumption. A total of 25 different source codes of applications programmed in the C# and Java languages are selected for the study, and combinations obtained from refactoring techniques are applied to these source codes. The combinations applied are analyzed using the maintainability index. Power consumption estimation tools are used to measure the energy consumption of the original and refactored codes. The results show that the combinations significantly improve the software’s energy efficiency. The results will provide a better understanding of the relationship between the energy efficiency of software and refactoring techniques. Moreover, they will help developers to improve their object-oriented code in terms of both energy efficiency and sustainability.

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Does maintainability relate to the energy consumption of software? A case study

Cited by 14 publications

References 29 publications

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

Can We Spot Energy Regressions using Developers Tests?

Energy Efficiency Analysis of Code Refactoring Techniques for Green and Sustainable Software in Portable Devices

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