The United Nations Sustainable Development Goals in Systems Engineering: Eliciting sustainability requirements

• URL: http://arxiv.org/abs/2006.10528v1
• Date: Fri, 12 Jun 2020 14:14:05 GMT
• Title: The United Nations Sustainable Development Goals in Systems Engineering: Eliciting sustainability requirements
• Authors: Ian Brooks
• Abstract summary: Using the United Nations Sustainable Development Goals as explicit inputs to drive the Software Requirements Engineering process will result in requirements with improved sustainability benefits. Three DSRM cycles are being used to test the hypothesis in safety-critical, highprecision, software-intensive systems in aerospace and healthcare.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper discusses a PhD research project testing the hypothesis that using the United Nations Sustainable Development Goals(SDG) as explicit inputs to drive the Software Requirements Engineering process will result in requirements with improved sustainability benefits. The research has adopted the Design Science Research Method (DSRM) [21] to test a process named SDG Assessment for Requirements Elicitation (SDGARE). Three DSRM cycles are being used to test the hypothesis in safety-critical, highprecision, software-intensive systems in aerospace and healthcare. Initial results from the first two DSRM cycles support the hypothesis. However, these cycles are in a plan-driven (waterfall) development context and future research agenda would be a similar application in an Agile development context.

Related papers

• Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement [62.94719119451089]
  Lingma SWE-GPT series learns from and simulating real-world code submission activities. Lingma SWE-GPT 72B resolves 30.20% of GitHub issues, marking a significant improvement in automatic issue resolution.
  arXiv  Detail & Related papers  (2024-11-01T14:27:16Z)
• Generative AI for Requirements Engineering: A Systematic Literature Review [4.056534128718579]
  Generative AI (GenAI) has emerged as a transformative tool in software engineering. This paper analyzes state-of-the-art applications and innovative proposals leveraging GenAI in requirements engineering.
  arXiv  Detail & Related papers  (2024-09-10T02:44:39Z)
• Environmentally Sustainable Software Design and Development: A Systematic Literature Review [1.6071754144962787]
  The ICT sector is under scrutiny calling for methodologies and tools to design and develop software in an environmentally sustainable-by-design manner. We conduct a systematic literature review on state-of-the-art proposals for designing and developing sustainable software.
  arXiv  Detail & Related papers  (2024-07-29T11:24:11Z)
• Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing [61.98556945939045]
  We propose a framework to learn planning-based reasoning through Direct Preference Optimization (DPO) on collected trajectories. Our results on challenging logical reasoning benchmarks demonstrate the effectiveness of our learning framework.
  arXiv  Detail & Related papers  (2024-02-01T15:18:33Z)
• Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs [10.241642683713467]
  Large-language models (LLMs) have shown significant promise in diverse domains, including natural language processing, code generation, and program understanding. This chapter explores the potential of LLMs in driving Requirements Engineering processes, aiming to improve the efficiency and accuracy of requirements-related tasks.
  arXiv  Detail & Related papers  (2023-10-21T11:29:31Z)
• Transitioning ECP Software Technology into a Foundation for Sustainable Research Software [0.586336038845426]
  The Sustainable Research Software Institute (SRSI) Model has been designed to address the concerns. This white paper provides an overview and detailed description of how ECP-ST will transition into the SRSI.
  arXiv  Detail & Related papers  (2023-08-29T01:03:12Z)
• QSDsan: An Integrated Platform for Quantitative Sustainable Design of Sanitation and Resource Recovery Systems [4.68128997208138]
  QSDsan is an open-source tool written in Python for the quantitative sustainable design of sanitation and resource recovery systems. We show the utility of QSDsan to automate design, enable flexible process modeling, achieve rapid and reproducible simulations, and to perform advanced statistical analyses with integrated visualization.
  arXiv  Detail & Related papers  (2022-03-07T18:42:15Z)
• A Research Agenda for Artificial Intelligence in the Field of Flexible Production Systems [53.47496941841855]
  Production companies face problems when it comes to quickly adapting their production control to fluctuating demands or changing requirements. Control approaches aiming to encapsulate production functions in the sense of services have shown to be promising in order to increase flexibility of Cyber-Physical Production Systems. But an existing challenge of such approaches is finding production plans based on provided functionalities for a set of requirements, especially when there is no direct (i.e., syntactic) match between demanded and provided functions.
  arXiv  Detail & Related papers  (2021-12-31T14:38:31Z)
• SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning [63.192289553021816]
  Progress toward the United Nations Sustainable Development Goals has been hindered by a lack of data on key environmental and socioeconomic indicators. Recent advances in machine learning have made it possible to utilize abundant, frequently-updated, and globally available data, such as from satellites or social media. In this paper, we introduce SustainBench, a collection of 15 benchmark tasks across 7 SDGs.
  arXiv  Detail & Related papers  (2021-11-08T18:59:04Z)
• Wizard of Search Engine: Access to Information Through Conversations with Search Engines [58.53420685514819]
  We make efforts to facilitate research on CIS from three aspects. We formulate a pipeline for CIS with six sub-tasks: intent detection (ID), keyphrase extraction (KE), action prediction (AP), query selection (QS), passage selection (PS) and response generation (RG) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.
  arXiv  Detail & Related papers  (2021-05-18T06:35:36Z)
• Using satellite imagery to understand and promote sustainable development [87.72561825617062]
  We synthesize the growing literature that uses satellite imagery to understand sustainable development outcomes. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and resolution of satellite imagery. We review recent machine learning approaches to model-building in the context of scarce and noisy training data.
  arXiv  Detail & Related papers  (2020-09-23T05:20:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.