Quality Issues in Machine Learning Software Systems

TL;DR

This paper empirically investigates common quality issues in Machine Learning Software Systems through interviews and surveys, providing a catalog of issues, their causes, and mitigation strategies to improve system reliability.

Contribution

It presents the first comprehensive catalog of real-world quality issues in MLSSs based on practitioner insights, aiding future quality assurance efforts.

Findings

Identified 18 recurring quality issues in MLSSs.

Developed 21 strategies for mitigating these issues.

Provided detailed causes and consequences for each issue.

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 21 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