Automated Trainability Evaluation for Smart Software Functions

Gerostathopoulos, Ilias; Kugele, Stefan; Segler, Christoph; Bureš, Tomáš; Knoll, Alois

doi:10.1109/ase.2019.00096

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Finally we concluded that the understanding of how Software Engineering Model Development practices and the adoption of a Machine Learning Workflow in accordance with those practices, more specifically, with the Software Engineering life-cycle is a subject of vital importance for the evolution of Machine Learning/Artificial Intelligence and continuing development of its applications (especially on a large scale), even if further research on the topic is needed. -Provenance tag for data models [1], [2], [13] Documentation and Versioning -Extract metadata from repositories is difficult -Catalog of ML models to support design and maintenance [15] Non-functional Requirements -security -unassured reliability and lacking transparency -Identify parts of the ISO 26262 to be adapted to ML -An approach based on dependability assurances [30], [31] Design and Implementation -APIs look and feel like conventional APIs, but abstract away data-driven behavior -catalog of design patterns for ML development -information to support documentation and design of APIs [6], [32] Evaluation -Testing interpretability, privacy, or efficiency of ML -Proposal of new test semantic -Tests based on quality score [3], [33], [34] Deployment and Maintenance -Lack of support to adapt based on feedback -An approach to support adaptation based on quality gates [34] Software Capability Maturity Model (CMM)…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Lorenzoni¹,

Alencar²,

Nascimento³

et al. 2021

Preprint

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Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.

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