Zobrazeno 1 - 10
of 385
pro vyhledávání: '"Lewis, Grace"'
Machine learning models make mistakes, yet sometimes it is difficult to identify the systematic problems behind the mistakes. Practitioners engage in various activities, including error analysis, testing, auditing, and red-teaming, to form hypotheses
Externí odkaz:
http://arxiv.org/abs/2409.09261
Increasing rate of progress in hardware and artificial intelligence (AI) solutions is enabling a range of software systems to be deployed closer to their users, increasing application of edge software system paradigms. Edge systems support scenarios
Externí odkaz:
http://arxiv.org/abs/2406.08583
Testing of machine learning (ML) models is a known challenge identified by researchers and practitioners alike. Unfortunately, current practice for ML model testing prioritizes testing for model performance, while often neglecting the requirements an
Externí odkaz:
http://arxiv.org/abs/2406.08575
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. While Gr
Externí odkaz:
http://arxiv.org/abs/2312.09610
Autor:
Yang, Chenyang, Rustogi, Rishabh, Brower-Sinning, Rachel, Lewis, Grace A., Kästner, Christian, Wu, Tongshuang
Current model testing work has mostly focused on creating test cases. Identifying what to test is a step that is largely ignored and poorly supported. We propose Weaver, an interactive tool that supports requirements elicitation for guiding model tes
Externí odkaz:
http://arxiv.org/abs/2310.09668
Publikováno v:
2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE)
Machine learning (ML) components are increasingly incorporated into software products for end-users, but developers face challenges in transitioning from ML prototypes to products. Academics have limited access to the source of commercial ML products
Externí odkaz:
http://arxiv.org/abs/2308.04328
Publikováno v:
2023 IEEE/ACM 2nd International Conference on AI Engineering -- Software Engineering for AI (CAIN)
Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing challenges. Many researchers have invested significant effort in understanding the challenges of industry practi
Externí odkaz:
http://arxiv.org/abs/2304.00078
Autor:
Maffey, Katherine R., Dotterrer, Kyle, Niemann, Jennifer, Cruickshank, Iain, Lewis, Grace A., Kästner, Christian
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodology in place to do so. To fill this gap, we propose MLTE (Machine Learni
Externí odkaz:
http://arxiv.org/abs/2303.01998
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-graine
Externí odkaz:
http://arxiv.org/abs/2211.06409