Causal Software Engineering: A Vision and Roadmap
For software engineers and researchers, this work provides a vision for moving beyond correlation to causal reasoning in high-stakes decisions, though it remains a conceptual proposal without empirical validation.
The paper proposes Causal Software Engineering (CSE) as a new paradigm that uses causal models and reasoning to answer interventional and counterfactual questions in software engineering, addressing limitations of current correlational AI approaches. It outlines a workflow, roadmap, and evaluation agenda for adoption.
Software engineering increasingly involves making high-stakes decisions under uncertainty, using signals from code, field data, and socio-technical processes. Recent AI-driven support (e.g., anomaly detection, predictive analytics, AIOps, as well as LLM-based agents) has amplified engineers' ability to detect patterns and synthesize content and recommendations, but many critical questions are interventional or counterfactual: What is the expected impact of changing a load-balancing strategy? Would an outage have been avoided under a different release plan? Correlational models answer "what tends to co-occur"; they struggle to answer "what would happen if we act." We propose Causal Software Engineering (CSE) as a future paradigm in which causal models and causal reasoning systematically inform activities across the software lifecycle, augmenting existing practices with explicit assumptions, uncertainty-aware effect estimates, and counterfactual diagnosis. We outline (i) a causal-first workflow view spanning development and operations, (ii) a staged roadmap for tools and organizational adoption, and (iii) an evaluation and benchmark agenda for measuring progress.