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arXiv 2109.00872cs.SE
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机器学习的非功能性需求:了解当前工业中的使用和挑战

Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industry

Khan Mohammad Habibullah, Jennifer Horkoff

摘要:

机器学习(ML)是人工智能(AI)的一种应用,它使用大数据来生成复杂的预测和决策系统,否则将具有挑战性。为了确保支持ML的系统的成功,必须了解ML解决方案的某些质量(性能,透明度,公平性),从需求工程(RE)的角度称为非功能性需求(NFR)。然而,当系统涉及ML时,传统软件的NFR可能不会以相同的方式应用;一些NFR可能变得更加突出或不那么重要; NFR可能在ML模型,数据或整个系统上定义; ML的NFR可能会以不同的方式测量。在这项工作中,我们的目标是了解在工业中处理ML的NFR的最新技术和挑战。我们采访了10位使用NFR和ML的工程实践者。我们发现了以下示例:(1)识别和测量ML的NFR,(2)识别ML的更多和更少重要的NFR,以及(3)与NFR和ML相关的挑战。这些知识描绘了在实践中如何处理ML相关的NFR,并有助于指导未来的RE for ML工作。

英文摘要:

Machine Learning (ML) is an application of Artificial Intelligence (AI) that uses big data to produce complex predictions and decision-making systems, which would be challenging to obtain otherwise. To ensure the success of ML-enabled systems, it is essential to be aware of certain qualities of ML solutions (performance, transparency, fairness), known from a Requirement Engineering (RE) perspective as non-functional requirements (NFRs). However, when systems involve ML, NFRs for traditional software may not apply in the same ways; some NFRs may become more prominent or less important; NFRs may be defined over the ML model, data, or the entire system; and NFRs for ML may be measured differently. In this work, we aim to understand the state-of-the-art and challenges of dealing with NFRs for ML in industry. We interviewed ten engineering practitioners working with NFRs and ML. We find examples of (1) the identification and measurement of NFRs for ML, (2) identification of more and less important NFRs for ML, and (3) the challenges associated with NFRs and ML in the industry. This knowledge paints a picture of how ML-related NFRs are treated in practice and helps to guide future RE for ML efforts.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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