Causal Software Engineering: A Vision and Roadmap

TL;DR

This paper advocates for Causal Software Engineering, emphasizing the integration of causal models and reasoning to improve decision-making and diagnostics throughout the software lifecycle.

Contribution

It introduces the concept of Causal Software Engineering, proposing a causal-first workflow, a staged adoption roadmap, and an evaluation agenda for future development.

Findings

Highlights the limitations of correlational models in software decision-making.

Proposes causal models to improve impact prediction and diagnostics.

Outlines a roadmap for integrating causal reasoning into software engineering.

Abstract

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.