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Quality issues in machine learning software systems
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AI
DOI：10.1007/s10664-024-10536-7.png)
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Abstract
En 中文
ContextAn 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).ProblemThere 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.ObjectiveThis 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.MethodWe 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.ResultsBased 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.ConclusionWe 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.
Keywords:
Machine Learning based Software Systems
Quality Assurance
Quality issues
Interview
MLOps
Machine Learning
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