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ORCIDCAIN 2024: Lisbon, Portugal
- Jane Cleland-Huang, Jan Bosch, Henry Muccini, Grace A. Lewis:
Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024, Lisbon, Portugal, April 14-15, 2024. ACM 2024
Architecting, Designing, Managing, and Modeling AI-Enabled Systems
- Qinghua Lu
, Liming Zhu, Xiwei Xu, Yue Liu, Zhenchang Xing, Jon Whittle:
A Taxonomy of Foundation Model based Systems through the Lens of Software Architecture. 1-6 - Raphael Cabral, Marcos Kalinowski, Maria Teresa Baldassarre, Hugo Villamizar, Tatiana Escovedo, Hélio Lopes:
Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding. 7-17 - Francisco Durán, Silverio Martínez-Fernández, Matias Martinez, Patricia Lago:
Identifying Architectural Design Decisions for Achieving Green ML Serving. 18-23 - Diaeddin Rimawi, Antonio Liotta, Marco Todescato, Barbara Russo:
Modeling Resilience of Collaborative AI Systems. 24-29 - Jie JW Wu:
An Exploratory Study of V-Model in Building ML-Enabled Software: A Systems Engineering Perspective. 30-40 - Martin Hollender, Chaojun Xu, Ruomu Tan:
Engineering Challenges in Industrial AI. 41-42
Data Engineering and Management for AI-Enabled Systems
- Petra Heck:
What About the Data? A Mapping Study on Data Engineering for AI Systems. 43-52 - Gilberto Recupito, Raimondo Rapacciuolo, Dario Di Nucci, Fabio Palomba:
Unmasking Data Secrets: An Empirical Investigation into Data Smells and Their Impact on Data Quality. 53-63 - Tajkia Rahman Toma, Cor-Paul Bezemer:
An Exploratory Study of Dataset and Model Management in Open Source Machine Learning Applications. 64-74 - Lorena Barreto Simedo Pacheco, Musfiqur Rahman, Fazle Rabbi, Pouya Fathollahzadeh, Ahmad Abdellatif, Emad Shihab, Tse-Hsun (Peter) Chen, Jinqiu Yang, Ying Zou:
DVC in Open Source ML-development: The Action and the Reaction. 75-80
Generative AI Engineering
- Gustavo Pinto, Cleidson R. B. de Souza, Thayssa A. da Rocha, Igor Steinmacher, Alberto de Souza, Edward Monteiro:
Developer Experiences with a Contextualized AI Coding Assistant: Usability, Expectations, and Outcomes. 81-91 - Dawen Zhang, Boming Xia, Yue Liu, Xiwei Xu, Thong Hoang, Zhenchang Xing, Mark Staples, Qinghua Lu, Liming Zhu:
Privacy and Copyright Protection in Generative AI: A Lifecycle Perspective. 92-97 - Vanilson Arruda Burégio, Iverson Pereira, Henrique Cabral:
Innovating Translation: Lessons Learned from BWX Generative Language Engine. 98-99 - Boming Xia, Qinghua Lu, Liming Zhu, Sung Une Lee, Yue Liu, Zhenchang Xing:
Towards a Responsible AI Metrics Catalogue: A Collection of Metrics for AI Accountability. 100-111
Energy-Aware AI Engineering
- Jai Kannan, Scott Barnett, Anj Simmons, Taylan Selvi, Luis Cruz:
Green Runner: A Tool for Efficient Deep Learning Component Selection. 112-117 - Erik Johannes Husom, Sagar Sen, Arda Goknil:
Engineering Carbon Emission-aware Machine Learning Pipelines. 118-128 - Ye Yuan, Jiacheng Shi, Zongyao Zhang, Kaiwei Chen, Jingzhi Zhang, Vincenzo Stoico, Ivano Malavolta:
The Impact of Knowledge Distillation on the Energy Consumption and Runtime Efficiency of NLP Models. 129-133 - Negar Alizadeh, Fernando Castor:
Green AI: a Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures. 134-139
LLMs and Testing
- Krishna Khadka, Jaganmohan Chandrasekaran, Yu Lei, Raghu N. Kacker, D. Richard Kuhn:
A Combinatorial Approach to Hyperparameter Optimization. 140-149 - Ziyu Li, Donghwan Shin:
Mutation-based Consistency Testing for Evaluating the Code Understanding Capability of LLMs. 150-159 - Zafaryab Rasool, Scott Barnett, David Willie, Stefanus Kurniawan, Sherwin Balugo, Srikanth Thudumu, Mohamed Abdelrazek:
LLMs for Test Input Generation for Semantic Applications. 160-165 - Wanqin Ma, Chenyang Yang, Christian Kästner:
(Why) Is My Prompt Getting Worse? Rethinking Regression Testing for Evolving LLM APIs. 166-171 - Ali Nouri, Beatriz Cabrero Daniel, Fredrik Törner, Håkan Sivencrona, Christian Berger:
Welcome Your New AI Teammate: On Safety Analysis by Leashing Large Language Models. 172-177 - Hala Abdelkader, Mohamed Abdelrazek, Scott Barnett, Jean-Guy Schneider, Priya Rani, Rajesh Vasa:
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems - A Case Study. 178-183
System Qualities
- Gereon Weiss, Marc Zeller, Hannes Schoenhaar, Christian Drabek, Andreas Kreutz:
Approach for Argumenting Safety on Basis of an Operational Design Domain. 184-193 - Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu, Zach Brannelly, Mohamed Abdelrazek:
Seven Failure Points When Engineering a Retrieval Augmented Generation System. 194-199 - Maria Teresa Baldassarre, Domenico Gigante, Marcos Kalinowski, Azzurra Ragone:
POLARIS: A Framework to Guide the Development of Trustworthy AI Systems. 200-210 - Saeid Tizpaz-Niari, Sriram Sankaranarayanan:
Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory. 211-221 - Lorena Poenaru-Olaru, Natalia Karpova, Luis Cruz, Jan S. Rellermeyer, Arie van Deursen:
Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World. 222-233 - Minh-Tri Nguyen, Hong Linh Truong, Tram Truong Huu:
Novel Contract-based Runtime Explainability Framework for End-to-End Ensemble Machine Learning Serving. 234-244 - Zakaria Chihani:
Trustworthy AI: Industry-Guided Tooling of the Methods. 245-246
Doctoral Symposium
- Matthias Wagner:
Continuous Quality Assurance and ML Pipelines under the AI Act. 247-249 - Vladislav Indykov:
Component-based Approach to Software Engineering of Machine Learning-enabled Systems. 250-252 - Keerthiga Rajenthiram:
Optimizing Data Analytics Workflows through User-driven Experimentation. 253-255 - Santiago del Rey:
Software Design Decisions for Greener Machine Learning-based Systems. 256-258 - Rafiullah Omar:
Energy-Efficient Development of ML-Enabled Systems: A Data-Centric Approach. 259-263 - Felix Viktor Jedrzejewski:
Threat Modeling of ML-intensive Systems: Research Proposal. 264-266
POSTER SESSION: Posters
- Junji Hashimoto, Nobukazu Yoshioka:
A Domain Specific Language for Specification of Risk-oriented Object Detection Requirements. 267-268 - Hironori Washizaki, Nobukazu Yoshioka:
AI Security Continuum: Concept and Challenges. 269-270 - Mojtaba Mostafavi Ghahfarokhi, Hamed Jahantigh, Alireza Asadi, Sepehr Kianiangolafshani, Ashkan Khademian, Abbas Heydarnoori:
A Roadmap for Enriching Jupyter Notebooks Documentation with Kaggle Data. 271-272 - Md Tajmilur Rahman, Rahul Singh, Mir Yousuf Sultan:
Automating Patch Set Generation from Code Reviews Using Large Language Models. 273-274 - Gustavo Rodrigues dos Reis, Adrian Mos, Mario Cortes Cornax, Cyril Labbé:
Data Selection Driven by Item Difficulty: On Investigating Data Efficient Practice for Hyperparameter Search. 275-277 - Mojtaba Mostafavi Ghahfarokhi, Ashkan Khademian, Sepehr Kianiangolafshani, Alireza Asadi, Hamed Jahantigh, Abbas Heydarnoori:
Beyond Syntax: Unleashing the Power of Computational Notebooks Code Metrics in Documentation Generation. 278-279 - Andrei Paleyes, Han-Bo Li, Neil D. Lawrence:
Can causality accelerate experimentation in software systems? 280-281 - Lauren Olson:
Custom Developer GPT for Ethical AI Solutions. 282-283 - Jati H. Husen, Jomphon Runpakprakun, Sun Chang, Hironori Washizaki, Hnin Thandar Tun, Nobukazu Yoshioka, Yoshiaki Fukazawa:
Evaluation of The Generality of Multi-view Modeling Framework for ML Systems. 284-285 - Krishna Ronanki, Beatriz Cabrero Daniel, Christian Berger:
Prompt Smells: An Omen for Undesirable Generative AI Outputs. 286-287 - Hiroshi Tanaka, Masaru Ide, Jun Yajima, Sachiko Onodera, Kazuki Munakata, Nobukazu Yoshioka:
Taxonomy of Generative AI Applications for Risk Assessment. 288-289
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