Quality issues in Machine Learning Software Systems

TL;DR

This paper investigates real-world quality issues in Machine Learning Software Systems through practitioner interviews, aiming to develop a catalog of bad practices and improve quality assurance methods.

Contribution

It provides an empirical analysis of quality problems in MLSSs based on practitioner insights, highlighting common bad practices and challenges.

Findings

Identification of common quality issues in MLSSs

Development of a catalog of bad practices

Insights into root causes of quality problems

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 threat to human life. The quality assurance of MLSSs is considered as a challenging task and currently is a hot research topic. Moreover, it is important to cover all various aspects of the quality in MLSSs. 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 bad-practices related to poor quality in MLSSs. Method: We plan to conduct a set of interviews with practitioners/experts, believing that interviews are the best method to retrieve their experience and practices when dealing with quality issues. We expect that the catalog of issues developed at this step will also help us later to identify the severity, root causes, and possible remedy for quality issues of MLSSs, allowing us to develop efficient quality assurance tools for ML models and MLSSs.