Machine learning for requirements engineering (ML4RE): A systematic literature review complemented by practitioners’ voices from Stack Overflow

Context:

The research of machine learning for requirements engineering (ML4RE) has attracted more and more attention from researchers and practitioners. Although pioneering research has shown the potential of using ML techniques to improve RE practices, there lacks a systematic and comprehensive literature review in academia that integrates an industrial perspective. Specifically, none of the reviews available in ML4RE have considered the grey literature, which is primarily from practitioner origin and is more reflective of the real issues and challenges faced in practice.

Objective:

In this paper, we conduct a systematic survey of academic publications in ML4RE and complement it with the practitioners’ voices from Stack Overflow to complete a comprehensive literature review. Our research objective is to provide a comprehensive view of the current research progress in ML4RE, present the main questions and challenges faced in RE practice, understand the gap between research and practice, and provide our insights into how the RE academic domain can pragmatically develop in the future.

Method:

We systematically investigated 207 academic papers on ML4RE from 2010 to 2022, along with 375 questions related to RE practices on Stack Overflow and their corresponding answers. Our analysis encompassed their trends, focused RE activities and tasks, employed solutions, and associated data. Finally, we conducted a joint analysis, contrasting the outcomes of both parts.

Results:

Based on the statistical results from collected literature, we summarize an academic roadmap and analyse the disparities, offering research recommendations. Our suggestions include the development of intelligent question-answering assistants employing large language models, the integration of machine learning into industrial tools, and the promotion of collaboration between academia and industry.

Conclusion:

This study contributes by providing a holistic view of ML4RE, delineating disparities between research and practice, and proposing pragmatic suggestions to bridge the academia-industry gap.