Using Sustainability Impact Scores for Software Architecture Evaluation
- URL: http://arxiv.org/abs/2501.17004v1
- Date: Tue, 28 Jan 2025 15:00:45 GMT
- Title: Using Sustainability Impact Scores for Software Architecture Evaluation
- Authors: Iffat Fatima, Patricia Lago, Vasilios Andrikopoulos, Bram van der Waaij,
- Abstract summary: We present an improved version of the Sustainability Impact Score (SIS)
The SIS facilitates the identification and quantification of trade-offs in terms of their sustainability impact.
Our study reveals that technical quality concerns have significant, often unrecognized impacts across sustainability dimensions.
- Score: 5.33605239628904
- License:
- Abstract: For future regulatory compliance, organizations must assess and report on the state of sustainability in terms of its impacts over time. Sustainability, being a multidimensional concern, is complex to quantify. This complexity further increases with the interdependencies of the quality concerns across different sustainability dimensions. The research literature lacks a holistic way to evaluate sustainability at the software architecture level. With this study, our aim is to identify quality attribute (QA) trade-offs at the software architecture level and quantify the related sustainability impact. To this aim we present an improved version of the Sustainability Impact Score (SIS), building on our previous work. The SIS facilitates the identification and quantification of trade-offs in terms of their sustainability impact, leveraging a risk- and importance-based prioritization mechanism. To evaluate our approach, we apply it to an industrial case study involving a multi-model framework for integrated decision-making in the energy sector. Our study reveals that technical quality concerns have significant, often unrecognized impacts across sustainability dimensions. The SIS coupled with QA trade-offs can help practitioners make informed decisions that align with their sustainability goals. Early evaluations can help organizations mitigate sustainability risks by taking preventive actions.
Related papers
- Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives [52.863024096759816]
Misaligned research objectives have hindered progress in adversarial robustness research over the past decade.
We argue that realigned objectives are necessary for meaningful progress in adversarial alignment.
arXiv Detail & Related papers (2025-02-17T15:28:40Z) - Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC)
RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks.
Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
arXiv Detail & Related papers (2025-02-08T06:50:47Z) - Twin Transition or Competing Interests? Validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI) [0.0]
This paper presents the development and validation of the Artificial Intelligence and Sustainability Perceptions Inventory (AISPI)
The 13-item instrument measures how individuals view the relationship between AI advancement and environmental sustainability.
Our findings suggest that individuals can simultaneously recognize both synergies and tensions in the AI-sustainability relationship.
arXiv Detail & Related papers (2025-01-26T16:21:27Z) - Trustworthiness in Retrieval-Augmented Generation Systems: A Survey [59.26328612791924]
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs)
We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
arXiv Detail & Related papers (2024-09-16T09:06:44Z) - Explainable Natural Language Processing for Corporate Sustainability Analysis [26.267508407180465]
The concept of corporate sustainability is complex due to the diverse and intricate nature of firm operations.
Corporate sustainability assessments are plagued by subjectivity both within data that reflect corporate sustainability efforts and the analysts evaluating them.
We argue that Explainable Natural Language Processing (XNLP) can significantly enhance corporate sustainability analysis.
arXiv Detail & Related papers (2024-07-03T08:27:51Z) - Literature Review of Current Sustainability Assessment Frameworks and
Approaches for Organizations [10.045497511868172]
This systematic literature review explores sustainability assessment frameworks (SAFs) across diverse industries.
The review focuses on SAF design approaches including the methods used for Sustainability Indicator (SI) selection, relative importance assessment, and interdependency analysis.
arXiv Detail & Related papers (2024-03-07T18:14:52Z) - Evaluating and Improving Continual Learning in Spoken Language
Understanding [58.723320551761525]
We propose an evaluation methodology that provides a unified evaluation on stability, plasticity, and generalizability in continual learning.
By employing the proposed metric, we demonstrate how introducing various knowledge distillations can improve different aspects of these three properties of the SLU model.
arXiv Detail & Related papers (2024-02-16T03:30:27Z) - Service Level Agreements and Security SLA: A Comprehensive Survey [51.000851088730684]
This survey paper identifies state of the art covering concepts, approaches, and open problems of SLA management.
It contributes by carrying out a comprehensive review and covering the gap between the analyses proposed in existing surveys and the most recent literature on this topic.
It proposes a novel classification criterium to organize the analysis based on SLA life cycle phases.
arXiv Detail & Related papers (2024-01-31T12:33:41Z) - Assessing the Sustainability and Trustworthiness of Federated Learning Models [6.821579077084753]
The European Commission's AI-HLEG group has highlighted the importance of sustainable AI for trustworthy AI.
This work introduces the sustainability pillar to the trustworthy FL taxonomy, making this work the first to address all AI-HLEG requirements.
An algorithm is developed to evaluate the trustworthiness of FL models, incorporating sustainability considerations.
arXiv Detail & Related papers (2023-10-31T13:14:43Z) - Broadening the perspective for sustainable AI: Comprehensive
sustainability criteria and indicators for AI systems [0.0]
This paper takes steps towards substantiating the call for an overarching perspective on "sustainable AI"
It presents the SCAIS Framework which contains a set 19 sustainability criteria for sustainable AI and 67 indicators.
arXiv Detail & Related papers (2023-06-22T18:00:55Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z)
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.