2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI)最新文献

Robustness Testing of a Machine Learning-based Road Object Detection System: An Industrial Case 基于机器学习的道路物体检测系统鲁棒性测试:一个工业案例

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) Pub Date : 2022-05-01 DOI: 10.1145/3526073.3527592

Anne-Laure Wozniak, S. Segura, Raúl Mazo, Sarah Leroy

引用次数: 1

Operationalizing Machine Learning Models - A Systematic Literature Review 操作机器学习模型-系统的文献综述

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) Pub Date : 2022-05-01 DOI: 10.1145/3526073.3527584

Ask Berstad Kolltveit, Jingyue Li

{"title":"Operationalizing Machine Learning Models - A Systematic Literature Review","authors":"Ask Berstad Kolltveit, Jingyue Li","doi":"10.1145/3526073.3527584","DOIUrl":"https://doi.org/10.1145/3526073.3527584","url":null,"abstract":"Deploying machine learning (ML) models to production with the same level of rigor and automation as traditional software systems has shown itself to be a non-trivial task, requiring extra care and infrastructure to deal with the additional challenges. Although many studies focus on adapting ML software engineering (SE) approaches and techniques, few studies have summarized the status and challenges of operationalizing ML models. Model operationalization encompasses all steps after model training and evaluation, including packaging the model in a format appropriate for deployment, publishing to a model registry or storage, integrating the model into a broader software system, serving, and monitoring. This study is the first systematic literature review investigating the techniques, tools, and infrastructures to operationalize ML models. After reviewing 24 primary studies, the results show that there are a number of tools for most use cases to operationalize ML models and cloud deployment in particular. The review also revealed several research opportunities, such as dynamic model-switching, continuous model-monitoring, and efficient edge ML deployments. CCS CONCEPTS • General and reference → Surveys and overviews; • Computing methodologies → Machine learning; • Software and its engineering → Software development techniques.","PeriodicalId":129536,"journal":{"name":"2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117347920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

The Concept of Ethical Digital Identities 伦理数字身份的概念

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) Pub Date : 2022-05-01 DOI: 10.1145/3526073.3527586

Emilia Cioroaica, Barbora Buhnova, Frans Jacobi, Daniel Schneider

引用次数: 2

Towards Trusting the Ethical Evolution of Autonomous Dynamic Ecosystems 走向信任自主动态生态系统的伦理进化

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) Pub Date : 2022-05-01 DOI: 10.1145/3526073.3527585

Emilia Cioroaica, Barbora Buhnova, E. Tomur

引用次数: 0

Challenges in Machine Learning Application Development: An Industrial Experience Report 机器学习应用开发中的挑战:一份工业经验报告

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) Pub Date : 2022-05-01 DOI: 10.1145/3526073.3527593

Md Saidur Rahman, Foutse Khomh, Emilio Rivera, Yann-Gaël Guéhéneuc, Bernd Lehnert

{"title":"Challenges in Machine Learning Application Development: An Industrial Experience Report","authors":"Md Saidur Rahman, Foutse Khomh, Emilio Rivera, Yann-Gaël Guéhéneuc, Bernd Lehnert","doi":"10.1145/3526073.3527593","DOIUrl":"https://doi.org/10.1145/3526073.3527593","url":null,"abstract":"SAP is the market leader in enterprise application software offering an end-to-end suite of applications and services to enable their customers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales transactions for their day-to-day business. Transactions are created during retail sales at the point of sale (POS) terminals and those transactions are then sent to some central servers for validations and other business operations. A considerable proportion of the retail transactions may have inconsistencies or anomalies due to many technical and human errors. SAP provides an automated process for error detection but still requires a manual process by dedicated employees using workbench software for correction. However, manual corrections of these errors are time-consuming, labor-intensive, and might be prone to further errors due to incorrect modifications. Thus, automated detection and correction of transaction errors are very important regarding their potential business values and the improvement in the business workflow. In this paper, we report on our experience from a project where we develop an AI-based system to automatically detect transaction errors and propose corrections. We identify and discuss the challenges that we faced during this collaborative research and development project, from two distinct perspectives: Software Engineering and Machine Learning. We report on our experience and insights from the project with guidelines for the identified challenges. We collect developers’ feedback for qualitative analysis of our findings. We believe that our findings and recommendations can help other researchers and practitioners embarking into similar endeavours. CCS CONCEPTS • Software and its engineering → Programming teams.","PeriodicalId":129536,"journal":{"name":"2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125480850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

MLOps: A Guide to its Adoption in the Context of Responsible AI MLOps:在负责任的人工智能背景下采用MLOps的指南

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) Pub Date : 2022-05-01 DOI: 10.1145/3526073.3527591

B. M. A. Matsui, D. Goya

引用次数: 4

Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest 机器学习的非功能需求:对系统范围和兴趣的探索

2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI) Pub Date : 2022-03-21 DOI: 10.1145/3526073.3527589

K. M. Habibullah, Gregory Gay, Jennifer Horkoff

{"title":"Non-Functional Requirements for Machine Learning: An Exploration of System Scope and Interest","authors":"K. M. Habibullah, Gregory Gay, Jennifer Horkoff","doi":"10.1145/3526073.3527589","DOIUrl":"https://doi.org/10.1145/3526073.3527589","url":null,"abstract":"Systems that rely on Machine Learning (ML systems) have differing demands on quality—non-functional requirements (NFRs)— compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope—and of the extent of existing research—is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results— as well as inter-coder reliability on a sample of NFRs—to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain.CCS CONCEPTS • Software and its engineering → Extra-functional properties; Requirements analysis; • Computing methodologies → Machine learning.","PeriodicalId":129536,"journal":{"name":"2022 IEEE/ACM 1st International Workshop on Software Engineering for Responsible Artificial Intelligence (SE4RAI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126656159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7