Assessing the Use of AutoML for Data-Driven Software Engineering

TL;DR

This study evaluates the current state of AutoML tools in software engineering, demonstrating their potential to outperform manual models but highlighting limitations in automation and team support.

Contribution

It provides a comprehensive benchmark of 12 AutoML tools on SE datasets and explores practitioner perceptions through surveys and interviews.

Findings

AutoML can outperform manually trained models in SE classification tasks.

Current AutoML tools lack full automation across all ML development stages.

Practitioners see AutoML as useful but limited in supporting team workflows.

Abstract

Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.