Quality issues in machine learning software systems

IF 3.5 2区计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Empirical Software Engineering Pub Date : 2024-09-11 DOI:10.1007/s10664-024-10536-7

Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha, Ilan Basta, Mouna Abidi, Foutse Khomh

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引用次数: 0

Abstract

Context

An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and deployed in Machine Learning Software Systems (MLSSs).

Problem

There is a strong need for ensuring the serving quality of MLSSs. False or poor decisions of such systems can lead to malfunction of other systems, significant financial losses, or even threats to human life. The quality assurance of MLSSs is considered a challenging task and currently is a hot research topic.

Objective

This paper aims to investigate the characteristics of real quality issues in MLSSs from the viewpoint of practitioners. This empirical study aims to identify a catalog of quality issues in MLSSs.

Method

We conduct a set of interviews with practitioners/experts, to gather insights about their experience and practices when dealing with quality issues. We validate the identified quality issues via a survey with ML practitioners.

Results

Based on the content of 37 interviews, we identified 18 recurring quality issues and 24 strategies to mitigate them. For each identified issue, we describe the causes and consequences according to the practitioners’ experience.

Conclusion

We believe the catalog of issues developed in this study will allow the community to develop efficient quality assurance tools for ML models and MLSSs. A replication package of our study is available on our public GitHub repository.

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机器学习软件系统的质量问题

背景各个领域对使用机器学习（ML）解决复杂问题的需求日益增长。ML 模型以软件组件的形式实现，并部署在机器学习软件系统 (MLSS) 中。此类系统的错误或错误决策可能导致其他系统失灵、重大经济损失，甚至威胁人类生命。MLSS 的质量保证被认为是一项具有挑战性的任务，目前也是一个热门研究课题。方法我们对从业人员/专家进行了一系列访谈，收集他们在处理质量问题时的经验和做法。结果根据 37 次访谈的内容，我们发现了 18 个经常出现的质量问题和 24 个缓解这些问题的策略。结论我们相信，本研究中开发的问题目录将有助于社区为 ML 模型和 MLSS 开发高效的质量保证工具。我们在 GitHub 公共仓库中提供了本研究的复制包。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Empirical Software Engineering 工程技术-计算机：软件工程

CiteScore

8.50

自引率

12.20%

发文量

169

审稿时长

>12 weeks

期刊介绍： Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories. The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings. Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.