Automated Configuration Synthesis for Machine Learning Models: A git-Based Requirement and Architecture Management System
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914771797803008 |
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| author | AlShriaf, Abdullatif Heyn, Hans-Martin Knauss, Eric |
| author_facet | AlShriaf, Abdullatif Heyn, Hans-Martin Knauss, Eric |
| contents | This work introduces a tool for generating runtime configurations automatically from textual requirements stored as artifacts in git repositories (a.k.a. T-Reqs) alongside the software code. The tool leverages T-Reqs-modelled architectural description to identify relevant configuration properties for the deployment of artificial intelligence (AI)-enabled software systems. This enables traceable configuration generation, taking into account both functional and non-functional requirements. The resulting configuration specification also includes the dynamic properties that need to be adjusted and the rationale behind their adjustment. We show that this intermediary format can be directly used by the system or adapted for specific targets, for example in order to achieve runtime optimisations in term of ML model size before deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_17244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Configuration Synthesis for Machine Learning Models: A git-Based Requirement and Architecture Management System AlShriaf, Abdullatif Heyn, Hans-Martin Knauss, Eric Software Engineering This work introduces a tool for generating runtime configurations automatically from textual requirements stored as artifacts in git repositories (a.k.a. T-Reqs) alongside the software code. The tool leverages T-Reqs-modelled architectural description to identify relevant configuration properties for the deployment of artificial intelligence (AI)-enabled software systems. This enables traceable configuration generation, taking into account both functional and non-functional requirements. The resulting configuration specification also includes the dynamic properties that need to be adjusted and the rationale behind their adjustment. We show that this intermediary format can be directly used by the system or adapted for specific targets, for example in order to achieve runtime optimisations in term of ML model size before deployment. |
| title | Automated Configuration Synthesis for Machine Learning Models: A git-Based Requirement and Architecture Management System |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2404.17244 |