A greenability evaluation sheet for AI-based systems

El auge de los sistemas de machine learning (ML), la mejora de sus capacidades y el mayor tamaño de los sistemas, ha incrementado el impacto medioambiental de los modelos ML. Sin embargo, la información sobre cómo se mide, comunica y evalúa la huella de carbono de los modelos de ML es escasa. Este proyecto, basado en un análisis de 1.417 modelos de ML y conjuntos de datos asociados en Hugging Face, el repositorio más popular para modelos de ML preentrenados, tiene como objetivo proporcionar una solución integrada para comprender, informar y optimizar la eficiencia de carbono de los modelos de ML. Además, implementamos una aplicación web que genera etiquetas de eficiencia energética para modelos de ML y permite visualizar sus emisiones de carbono. Con menos del 1% de los modelos en Hugging Face proporcionando información sobre las emisiones de carbono, el proyecto subraya la necesidad de mejorar las prácticas de reporte energético y la promoción del desarrollo de modelos eficientes en carbono dentro de la comunidad Hugging Face. Para abordar esta cuestión, ofrecemos una herramienta web que produce etiquetas de eficiencia energética para modelos de ML, una contribución que fomenta la transparencia y el desarrollo de modelos sostenibles dentro de la comunidad de ML. Permite la creación de etiquetas energéticas, al tiempo que proporciona valiosas visualizaciones de los datos de emisiones de carbono. Esta solución integrada constituye un paso importante hacia prácticas de IA más sostenibles medioambientalmente.The rise of machine learning (ML) systems has increased their environmental impact due to the enhanced capabilities and larger model sizes. However, information about how the carbon footprint of ML models is measured, reported, and evaluated remains scarce and scattered. Aims: This project, based on an analysis of 1,417 ML models and associated datasets on Hugging Face, the most popular repository for pretrained ML models, aims to provide an integrated solution for understanding, reporting, and optimizing the carbon efficiency of ML models. Moreover, we implement a web-based application that generates energy efficiency labels for ML models and visualizes their carbon emissions. With less than 1% of models on Hugging Face currently reporting carbon emissions, the project underscores the need for improved energy reporting practices and the promotion of carbon-efficient model development within the Hugging Face community. To address this, we offer a web-based tool that produces energy efficiency labels for ML models, a contribution that encourages transparency and sustainable model development within the ML community. It enables the creation of the energy labels, while also providing valuable visualizations of carbon emissions data. This integrated solution serves as an important step towards more environmentally sustainable AI practices

Sustainability Debt: A Metaphor to Support Sustainability Design Decisions

Sustainability, the capacity to endure, is fundamental for the societies on our planet. Despite its increasing recognition in software engineering, it remains difficult to assess the delayed systemic effects of decisions taken in requirements engineering and systems design. To support this difficult task, this paper introduces the concept of sustainability debt. The metaphor helps in the discovery, documentation, and communication of sustainability issues in requirements engineering. We build on the existing metaphor of technical debt and extend it to four other dimensions of sustainability to help think about sustainability-aware software systems engineering. We highlight the meaning of debt in each dimension and the relationships between those dimensions. Finally, we discuss the use of the metaphor and explore how it can help us to design sustainability-aware software intensive systems

On the Presence of Green and Sustainable Software Engineering in Higher Education Curricula

Nowadays, software is pervasive in our everyday lives. Its sustainability and environmental impact have become major factors to be considered in the development of software systems. Millennials-the newer generation of university students-are particularly keen to learn about and contribute to a more sustainable and green society. The need for training on green and sustainable topics in software engineering has been reflected in a number of recent studies. The goal of this paper is to get a first understanding of what is the current state of teaching sustainability in the software engineering community, what are the motivations behind the current state of teaching, and what can be done to improve it. To this end, we report the findings from a targeted survey of 33 academics on the presence of green and sustainable software engineering in higher education. The major findings from the collected data suggest that sustainability is under-represented in the curricula, while the current focus of teaching is on energy efficiency delivered through a fact-based approach. The reasons vary from lack of awareness, teaching material and suitable technologies, to the high effort required to teach sustainability. Finally, we provide recommendations for educators willing to teach sustainability in software engineering that can help to suit millennial students needs.Comment: The paper will be presented at the 1st International Workshop on Software Engineering Curricula for Millennials (SECM2017

Sustainable software engineering education curricula development

Climate change risk and environmental degradation are the most critical issues of our society. Our technology influenced daily life style involves many software and apps which are used by large society and their use is increasing than ever before. Sustainability is a significant topic for future professionals and more so for Information Technology (IT) professionals and software engineers due to its impact on the society. It is significant to motivate and raise concern among students and faculty members regarding sustainability by including it into Software Engineering curriculum. Key words: Sustainability, Sustainable Software Engineering, Curricula, Software Engineering.publishedVersio

Software sustainability from a user perspective: A case study of a developing country (Kingdom of Saudi Arabia)

Interest in sustainable development is increasing. Understanding the user’s perspective toward software sustainability helps to enhance understanding of the concept. The need for developing countries to enhance their ICT infrastructure to align with United Nation (UN) sustainable development goals increases the necessity to understand the current perception of software users, industry and sustainability experts, to improve the level of software sustainability. Software sustainability has a number of challenges with regard to adoption by software users. This study investigates software sustainability from the point of view of users in the Kingdom of Saudi Arabia (KSA) by addressing four scales, namely beliefs, intention, attitude and perceptions toward using sustainable software. It also addresses key barriers to sustainable software, such as lack of awareness and difficulty of recognising sustainable software

Modeling the impact of UAVs in sustainability

This work has been supported by Junta de Extremadura (according to the Order 129/2015 of the 2nd of June) and NOVA LINCS Research Laboratory (Ref. UID/CEC/04516/2013).In the last few years, sustainability has become one of the priority lines for many companies and organizations, especially public administrations. This trend has been even more evident in some regions where the preservation of natural resources is of utmost importance, not only from an environmental perspective, but also from an economic one. In this context, technology has become one of the key factors to achieve sustainability goals. An example of these technologies are Unmanned Aerial Vehicles (UAVs) which are being used more and more with sustainability purposes. However, although some efforts have been made to propose software approaches to model sustainability, some examples that model the impact of technology on sustainability are still needed. This paper presents an instance of a sustainability metamodel for the UAVs domain. This model allows to specify the impact of UAV-based processes on sustainability, and also to identify potential limitations that may hinder its applicability. Finally, the paper provides some suggestions to complete the metamodel based on the instantiation process

Effect Of Stamping Parameters Of The Spring-Back Behaviour Of Oil Palm Fibre Composite

Composite materials have been vastly known worldwide for its uses in various sectors such as aerospace, infrastructures and automotive industries. Natural fibres are gaining recognition as a substitute to synthetic fibres due to their recyclability and abundance. In Malaysia, research on palm fibre composite had mainly focused on tensile and flexural properties but not on its stamp forming behaviour. Oil palm fibre reinforced polypropylene composite panel has the potential to be stamp formed in order to build complex geometries. In stamp forming the most sensitive feature is elastic recovery during unloading. This phenomenon will affect the net dimension of the final product. This research studies the effects of tool radius, feed rate, temperature and weight ratio of fibre to polypropylene on the spring-back of oil palm fibre composite and to formulate an empirical equation to predict the spring-back angle. The composite material are mixed together with different fibre composition of 10wt%, 20wt%, 30wt%, 40wt% and pure polypropylene using an internal mixer and hot pressing. The samples are cut into rectangular shape samples with a dimension of 180mm x 20mm x 2mm for V-bend testing and are 160mm x 20mm x 2mm accooding to ASTM D3039 for tensile testing. A V-bending die is used to characterise the spring-back angle of the oil palm fibre composite. The results are computed using a statistical software (Minitab). Statistical analysis conducted shows all the studied parameters gave significant effect towards spring-back. Based on the analysed result, an empirical model was formulated to predict the spring-back angle. A stereo microscope was used as a visual aid to show the surface on the deformed area. It can be concluded that the higher the temperature, feed rate and fibre composition (up to 30wt %) the smaller the spring-back but the smaller the tool radius, the smaller the spring-back angle