Background: The increasing environmental impact of Information Technologies, particularly in Machine Learning (ML), highlights the need for sustainable practices in software engineering. The escalating complexity and energy consumption of ML models need tools for assessing and improving their energy efficiency. Goal: This paper introduces GAISSALabel, a web-based tool designed to evaluate and label the energy efficiency of ML models. Method: GAISSALabel is a technology transfer development from a former research on energy efficiency classification of ML, consisting of a holistic tool for assessing both the training and inference phases of ML models, considering various metrics such as power draw, model size efficiency, CO2e emissions and more. Results: GAISSALabel offers a labeling system for energy efficiency, akin to labels on consumer appliances, making it accessible to ML stakeholders of varying backgrounds. The tool’s adaptability allows for customization in the proposed labeling system, ensuring its relevance in the rapidly evolving ML field. Conclusions: GAISSALabel represents a significant step forward in sustainable software engineering, offering a solution for balancing high-performance ML models with environmental impacts. The tool’s effectiveness and market relevance will be further assessed through planned evaluations using the Technology Acceptance Model.
Thu 18 JulDisplayed time zone: Brasilia, Distrito Federal, Brazil change
14:00 - 15:30 | SE4AI 1Journal First / Ideas, Visions and Reflections / Research Papers / Demonstrations at Mandacaru Chair(s): Qinghua Lu Data61, CSIRO | ||
14:00 18mTalk | Harnessing Neuron Stability to Improve DNN Verification Research Papers Hai Duong George Mason University, Dong Xu University of Virginia, ThanhVu Nguyen George Mason University, Matthew B Dwyer University of Virginia | ||
14:18 18mTalk | MirrorFair: Fixing Fairness Bugs in Machine Learning Software via Counterfactual Predictions Research Papers Ying Xiao King's College London / Southern University of Science and Technology, Jie M. Zhang King's College London, Yepang Liu Southern University of Science and Technology, Mohammad Reza Mousavi King's College London, Sicen Liu Southern University of Science and Technology, Dingyuan Xue Southern University of Science and Technology | ||
14:36 9mTalk | Using Run-time Information to Enhance Static Analysis of Machine Learning Code in Notebooks Ideas, Visions and Reflections Yiran Wang Linköping University, José Antonio Hernández López Linkoping University, Ulf Nilsson Linköping University, Daniel Varro Linköping University / McGill University Link to publication DOI | ||
14:45 9mTalk | Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications Ideas, Visions and Reflections Quan Zhang Tsinghua University, Binqi Zeng Central South University, Chijin Zhou Tsinghua University, Gwihwan Go Tsinghua University, Heyuan Shi Central South University, Yu Jiang Tsinghua University | ||
14:54 18mTalk | DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural Networks Journal First Zohreh Aghababaeyan University of Ottawa, Canada, Manel Abdellatif Software and Information Technology Engineering Department, École de Technologie Supérieure, Mahboubeh Dadkhah The School of EECS, University of Ottawa, Lionel Briand University of Ottawa, Canada; Lero centre, University of Limerick, Ireland | ||
15:12 9mTalk | Testing Learning-Enabled Cyber-Physical Systems with Large-Language Models: A Formal Approach Ideas, Visions and Reflections Xi Zheng Macquarie University, Aloysius K. Mok University of Texas at Austin, Ruzica Piskac Yale University, Yong Jae Lee University of Wisconsin Madison, Bhaskar Krishnamachari University of Southern California, Dakai Zhu The University of Texas at San Antonio, Oleg Sokolsky University of Pennsylvania, USA, Insup Lee University of Pennsylvania | ||
15:21 9mTalk | GAISSALabel: A tool for energy labeling of ML models Demonstrations Pau Duran Universitat Politècnica de Catalunya (UPC), Joel Castaño Fernández Universitat Politècnica de Catalunya (UPC), Cristina Gómez Universitat Politècnica de Catalunya, Silverio Martínez-Fernández UPC-BarcelonaTech Link to publication Pre-print | ||