Measuring the quality of generative AI systems: Mapping metrics to quality characteristics-Snowballing literature review

Generative Artificial Intelligence (GenAI) and the use of Large Language Models (LLMs) have revolutionized tasks that previously required significant human effort, which has attracted considerable interest from industry stakeholders. This growing interest has accelerated the integration of AI models into various industrial applications. However, the model integration introduces challenges to product quality, as conventional quality measuring methods may fail to assess GenAI systems. Consequently, evaluation techniques for GenAI systems need to be adapted and refined. Examining the current state and applicability of evaluation techniques for the GenAI system outputs is essential. This study aims to explore the current metrics, methods, and processes for assessing the outputs of GenAI systems and the potential of risky outputs. We performed a snowballing literature review to identify metrics, evaluation methods, and evaluation processes from 43 selected papers. We identified 28 metrics and mapped these metrics to four quality characteristics defined by the ISO/IEC 25023 standard for software systems. Additionally, we discovered three types of evaluation methods to measure the quality of system outputs and a three-step process to assess faulty system outputs. Based on these insights, we suggested a five-step framework for measuring system quality while utilizing GenAI models. Our findings present a mapping that visualizes candidate metrics to be selected for measuring quality characteristics of GenAI systems, accompanied by step-by-step processes to assist practitioners in conducting quality assessments.

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