On the Effectiveness of Machine Learning Experiment Management Tools
Paper i proceeding, 2022

Machine learning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine-learning experiments and assets when building intelligent software systems. Among others, they allow tracing back system behavior to experiment runs, for instance, when model performance drifts. Unfortunately, despite a surge of these tools, they are not well integrated with traditional software engineering tooling, and no hard empirical data exists on their effectiveness and value for users. We present a short research agenda and early results towards unified and effective software engineering and experiment management software

Författare

Samuel Idowu

Software Engineering 2

Göteborgs universitet

Osman Osman

Göteborgs universitet

Daniel Strüber

Software Engineering 2

Göteborgs universitet

Thorsten Berger

Software Engineering 2

Göteborgs universitet

Proceedings - International Conference on Software Engineering

02705257 (ISSN)

Vol. 4th ACM/IEEE International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2022 207-208
9781665495905 (ISBN)

44th ACM/IEEE International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2022
Pittsburgh, USA,

Ämneskategorier (SSIF 2025)

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.1109/ICSE-SEIP55303.2022.9794036

Mer information

Senast uppdaterat

2025-06-27