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