Causal Software Engineering: A Vision and Roadmap

2026-05-04

AI Summary

This work addresses the challenge of high-stakes decision-making in software engineering, where answering interventional or counterfactual questions—such as “what would happen if a specific action were taken?”—is critical yet poorly supported by existing correlation-based AI methods. To bridge this gap, we propose Causal Software Engineering (CSE), a novel paradigm that systematically integrates causal inference, counterfactual reasoning, and uncertainty quantification across the entire software lifecycle. CSE synergistically combines these causal foundations with emerging AI technologies, including AIOps and LLM-based agents. Our contributions include a causality-first workflow design, a phased roadmap for tool implementation, and an evaluation benchmark. This framework provides reliable, interpretable methodologies for predicting the effects of interventions, thereby significantly enhancing the quality and robustness of software engineering decisions.

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.

Problem

• causal reasoning
• software engineering
• interventional questions
• counterfactual analysis
• decision under uncertainty

Innovation

• Causal Software Engineering
• causal reasoning
• counterfactual diagnosis
• interventional analysis
• software lifecycle