Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases

TL;DR

This paper provides a comprehensive review and taxonomy of quality assurance challenges for machine learning software applications across different software development life cycle phases, highlighting research gaps and improvement opportunities.

Contribution

It offers the first holistic taxonomy mapping ML QA challenges to SDLC phases, guiding better QA practices and future research directions.

Findings

Identified key QA challenges in each SDLC phase

Mapped ML adoption issues across development stages

Provided recommendations for improving MLSA quality assurance

Abstract

In the past decades, the revolutionary advances of Machine Learning (ML) have shown a rapid adoption of ML models into software systems of diverse types. Such Machine Learning Software Applications (MLSAs) are gaining importance in our daily lives. As such, the Quality Assurance (QA) of MLSAs is of paramount importance. Several research efforts are dedicated to determining the specific challenges we can face while adopting ML models into software systems. However, we are aware of no research that offered a holistic view of the distribution of those ML quality assurance challenges across the various phases of software development life cycles (SDLC). This paper conducts an in-depth literature review of a large volume of research papers that focused on the quality assurance of ML models. We developed a taxonomy of MLSA quality assurance issues by mapping the various ML adoption challenges across different phases of SDLC. We provide recommendations and research opportunities to improve SDLC practices based on the taxonomy. This mapping can help prioritize quality assurance efforts of MLSAs where the adoption of ML models can be considered crucial.