Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
For software engineering researchers and practitioners, this paper calls for a new paradigm to address the limitations of data-driven approaches in handling complex, interdependent tasks.
The paper argues that current deep learning-based software engineering tools are insufficient for modern demands and proposes a shift towards causal reasoning to enable machines to actively amplify engineers' reasoning rather than just automate or predict.
Software engineering is an intellectually demanding, creative discipline that juggles a web of interdependent tasks to design, build, and assure the quality of increasingly complex systems. As our expectations from software soar - with demands spanning AI-driven products, pervasively distributed and cloud-native architectures, and deeply embedded cyber-physical environments - its complexity steadily increases. In response, a new wave of co-engineering methods and tools, fueled by deep learning, has emerged to augment the process, enhancing automation and decision support. Yet, these advances remain far from delivering the kind of intelligent support that modern software development demands. We call for a new paradigm of human-machine cooperation: one where machines don't just automate routine tasks or predict from learned patterns, but actively amplify engineers' reasoning through the lens of causation. As software becomes smarter, a smarter support is needed.