GEMS: Generative Expert Metric System through Iterative Prompt Priming
<|reference_start|>GEMS: Generative Expert Metric System through Iterative Prompt Priming: Across domains, metrics and measurements are fundamental to identifying challenges, informing decisions, and resolving conflicts. Despite the abundance of data available in this information age, not only can it be challenging for a single expert to work across multi-disciplinary data, but non-experts can also find it unintuitive to create effective measures or transform theories into context-specific metrics that are chosen appropriately. This technical report addresses this challenge by examining software communities within large software corporations, where different measures are used as proxies to locate counterparts within the organization to transfer tacit knowledge. We propose a prompt-engineering framework inspired by neural activities, demonstrating that generative models can extract and summarize theories and perform basic reasoning, thereby transforming concepts into context-aware metrics to support software communities given software repository data. While this research zoomed in on software communities, we believe the framework's applicability extends across various fields, showcasing expert-theory-inspired metrics that aid in triaging complex challenges.<|reference_end|>
@article{cheng2024gems:,
    title={GEMS: Generative Expert Metric System through Iterative Prompt Priming},
    author={Ti-Chung Cheng, Carmen Badea, Christian Bird, Thomas Zimmermann,
  Robert DeLine, Nicole Forsgren, Denae Ford},
    journal={arXiv preprint arXiv:2410.00880},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00880},
    primaryClass={cs.SE cs.AI}
}