An LLM-Based Approach for Automatic ML Prototype Review
IEEE International Conference on Software Analysis, Evolution and Reengineering - Companion (SANER-C), pp. 277–284
Abstract
When developing machine learning (ML) solutions, it is crucial to build prototypes that demonstrate the solution's technical feasibility and potential value. These ML prototypes are typically Jupyter notebooks. However, manually reviewing ML prototypes is time-consuming and can lead to relevant qualities being overlooked from diverse stakeholders' perspectives. This paper introduces an innovative approach that uses LLMs to automate the ML prototype review process, thereby improving quality and stakeholder awareness. Through a systematic literature review, we identified key quality characteristics and information needs. The result is an ML prototype review catalog containing a quality model, a list of information needs, and stakeholder personas. We present Proto-Check, a JupyterLab extension that implements our LLM-based review process. Evaluation results demonstrate high usefulness and usability, as well as heightened developer awareness of stakeholder qualities and needs.
Authors 5
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Affiliation as printed
RWTH Aachen University,Research Group Software Construction,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Research Group Software Construction,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Research Group Software Construction,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Research Group Software Construction,Aachen,Germany
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Affiliation as printed
RWTH Aachen University,Research Group Software Construction,Aachen,Germany
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