1 code implementation • 18 Nov 2023 • Wanqin Ma, Chenyang Yang, Christian Kästner
Large Language Models (LLMs) are increasingly integrated into software applications.
no code implementations • 14 Oct 2023 • Chenyang Yang, Rishabh Rustogi, Rachel Brower-Sinning, Grace A. Lewis, Christian Kästner, Tongshuang Wu
Current model testing work has mostly focused on creating test cases.
no code implementations • 31 Mar 2023 • Nadia Nahar, Haoran Zhang, Grace Lewis, Shurui Zhou, Christian Kästner
Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing challenges.
1 code implementation • 3 Mar 2023 • Katherine R. Maffey, Kyle Dotterrer, Jennifer Niemann, Iain Cruickshank, Grace A. Lewis, Christian Kästner
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.
no code implementations • 11 Nov 2022 • Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis, Christian Kästner, Tongshuang Wu
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.
no code implementations • 13 Apr 2022 • Avinash Bhat, Austin Coursey, Grace Hu, Sixian Li, Nadia Nahar, Shurui Zhou, Christian Kästner, Jin L. C. Guo
The documentation practice for machine-learned (ML) models often falls short of established practices for traditional software, which impedes model accountability and inadvertently abets inappropriate or misuse of models.
no code implementations • 19 Oct 2021 • Nadia Nahar, Shurui Zhou, Grace Lewis, Christian Kästner
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists.
no code implementations • 13 May 2021 • Christian Kästner, Eunsuk Kang, Sven Apel
The lack of specifications is a key difference between traditional software engineering and machine learning.
no code implementations • 13 May 2020 • Yang Ren, Gregory Gay, Christian Kästner, Pooyan Jamshidi
Machine learning (ML) frameworks and the systems developed using them differ greatly from traditional frameworks.
1 code implementation • 18 Jan 2020 • Christian Kästner, Eunsuk Kang
Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components.
1 code implementation • 10 Mar 2019 • Pooyan Jamshidi, Javier Cámara, Bradley Schmerl, Christian Kästner, David Garlan
Modern cyber-physical systems (e. g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time.
1 code implementation • 7 Sep 2017 • Pooyan Jamshidi, Norbert Siegmund, Miguel Velez, Christian Kästner, Akshay Patel, Yuvraj Agarwal
Modern software systems provide many configuration options which significantly influence their non-functional properties.