Search Results for author: Christian Kästner

Found 12 papers, 5 papers with code

A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners

no code implementations31 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.

MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities

1 code implementation3 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.

Capabilities for Better ML Engineering

no code implementations11 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.

Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability

no code implementations13 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.

BIG-bench Machine Learning Ethics

Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process

no code implementations19 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.


Feature Interactions on Steroids: On the Composition of ML Models

no code implementations13 May 2021 Christian Kästner, Eunsuk Kang, Sven Apel

The lack of specifications is a key difference between traditional software engineering and machine learning.

BIG-bench Machine Learning

Teaching Software Engineering for AI-Enabled Systems

1 code implementation18 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.

Ethics Fairness

Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots

1 code implementation10 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.

BIG-bench Machine Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.