Search Results for author:
Found 11 papers, 1 papers with code
How to Sustainably Monitor ML-Enabled Systems? Accuracy and Energy Efficiency Tradeoffs in Concept Drift Detection
Our results indicate that there are three types of detectors: a) detectors that sacrifice energy efficiency for detection accuracy (KSWIN), b) balanced detectors that consume low to medium energy with good accuracy (HDDM_W, ADWIN), and c) detectors that consume very little energy but are unusable in practice due to very poor accuracy (HDDM_A, PageHinkley, DDM, EDDM).
Balancing Progress and Responsibility: A Synthesis of Sustainability Trade-Offs of AI-Based Systems
11 themes were identified in the interviews, with the top mentioned theme being "employee wellbeing".
A Synthesis of Green Architectural Tactics for ML-Enabled Systems
The rapid adoption of artificial intelligence (AI) and machine learning (ML) has generated growing interest in understanding their environmental impact and the challenges associated with designing environmentally friendly ML-enabled systems.
AI Techniques in the Microservices Life-Cycle: A Survey
Microservices is a popular architectural style for the development of distributed software, with an emphasis on modularity, scalability, and flexibility.
Exploring the Carbon Footprint of Hugging Face's ML Models: A Repository Mining Study
This study seeks to answer two research questions: (1) how do ML model creators measure and report carbon emissions on Hugging Face Hub?, and (2) what aspects impact the carbon emissions of training ML models?
A Case Study on AI Engineering Practices: Developing an Autonomous Stock Trading System
We selected 10 AI engineering practices from the literature and systematically applied them during development, with the goal to collect evidence about their applicability and effectiveness.
Design Patterns for AI-based Systems: A Multivocal Literature Review and Pattern Repository
While new patterns for AI-based systems are emerging, existing patterns have also been adapted to this new context.
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
With the recent proliferation of artificial intelligence systems, there has been a surge in the demand for explainability of these systems.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Software Engineering for AI-Based Systems: A Survey
Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study
Results: Our results show that (i) established TD types, variations of them, and four new TD types (data, model, configuration, and ethics debt) are present in AI-based systems, (ii) 72 antipatterns are discussed in the literature, the majority related to data and model deficiencies, and (iii) 46 solutions have been proposed, either to address specific TD types, antipatterns, or TD in general.
Résumé-Driven Development: A Definition and Empirical Characterization
We therefore empirically investigated this phenomenon by surveying 591 software professionals in both hiring (130) and technical (558) roles and identified RDD facets in substantial parts of our sample: 60% of our hiring professionals agreed that trends influence their job offerings, while 82% of our software professionals believed that using trending technologies in their daily work makes them more attractive for prospective employers.
Software Engineering Computers and Society
