Academic & Investigative Research Question Refiner (FINER Framework)

You are a Senior Academic Research Director and Methodologist. I have a broad, preliminary research interest that needs to be refined into a precise, defensible research question suitable for formal empirical investigation. Broad Topic or Area of Inquiry: The impact of generative AI coding assistants (e.g. GitHub Copilot) on software quality and technical debt in enterprise engineering teams. Target Discipline & Methodological Orientation: Empirical Software Engineering: Mixed-methods approach combining git repository telemetry mining and structured developer surveys. Available Data Sources or Constraints (Time, Budget, Access): 6-month research timeline; access to anonymized commit logs, PR review metadata, and sonarqube code complexity metrics across 12 mid-sized B2B SaaS engineering teams. Core Phenomenon or Anomaly You Want to Explain: While developers report feeling significantly faster when using AI tools, initial industry reports suggest a potential rise in churned code (code rewritten within 14 days) and a drop in unit test boundary coverage. Guide me through a rigorous Research Question Refinement: 1. The FINER Assessment of the Initial Topic: - Feasible (Can this realistically be answered within available sample size and time constraints?) - Interesting (Does it engage practitioners and scholars, or is it trivial?) - Novel (Does it provide new empirical findings, replicate with new data, or extend theory?) - Ethical (Are there data privacy, human subjects, or institutional risks?) - Relevant (Does it impact future research, clinical practice, or policy?) 2. 3 Calibrated Research Question Candidates: Provide 3 distinct formulations varying by scope: - Candidate A (Exploratory / Descriptive) - Candidate B (Comparative / Quasi-Experimental) - Candidate C (Mechanistic / Causal Inference) 3. Explicit Boundary & Delimitation Conditions: Explicitly define the population, timeframe, and exclusion criteria to prevent scope explosion. 4. Empirical Verification Imperative: Outline the exact empirical data points or peer-reviewed primary literature that the researcher must personally verify rather than relying on AI-generated summaries.