Towards AI Agents Supported Research Problem Formulation

TL;DR

This paper envisions using AI agents within the Lean Research Inception framework to assist software engineering researchers in formulating research problems more effectively, reflecting industrial complexities.

Contribution

It proposes a novel approach of integrating AI agents into research problem formulation to support decision making and stakeholder alignment in software engineering.

Findings

AI support can enrich collaborative discussions

AI can enhance critical reflection on research problems

Scenario evaluation shows potential benefits of AI integration

Abstract

Poorly formulated research problems can compromise the practical relevance of Software Engineering studies by not reflecting the complexities of industrial practice. This vision paper explores the use of artificial intelligence agents to support SE researchers during the early stage of a research project, the formulation of the research problem. Based on the Lean Research Inception framework and using a published study on code maintainability in machine learning as a reference, we developed a descriptive evaluation of a scenario illustrating how AI agents, integrated into LRI, can support SE researchers by pre filling problem attributes, aligning stakeholder perspectives, refining research questions, simulating multiperspective assessments, and supporting decision making. The descriptive evaluation of the scenario suggests that AI agent support can enrich collaborative discussions and enhance critical reflection on the value, feasibility, and applicability of the research problem. Although the vision of integrating AI agents into LRI was perceived as promising to support the context aware and practice oriented formulation of research problems, empirical validation is needed to confirm and refine the integration of AI agents into problem formulation.