Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering

• URL: http://arxiv.org/abs/2606.27960v1
• Date: Fri, 26 Jun 2026 11:03:24 GMT
• Title: Reasoning Beyond Prediction: From Data-Driven to Causal Software Engineering
• Abstract summary: We call for a new paradigm of human-machine cooperation.<n>Machines don't just automate routine tasks, but actively amplify engineers' reasoning.<n>As software becomes smarter, a smarter support is needed.
• Score: 9.659620367206408
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• The Specification Paradox: Rethinking Requirements Engineering in the Age of AI [0.0]
  This article discusses the transition from a code-centered paradigm to Specification-Driven Development.<n>We argue that artificial intelligence reduces some of the effort associated with writing source code, but it does not eliminate the complexity of developing professional software systems.<n>We conclude that the future of Software Engineering will depend not only on machines' ability to generate code, but also on humans' ability to correctly specify, evaluate, and evolve what is intended to be built.
  arXiv  Detail & Related papers  (2026-08-17T14:19:14Z)
• Artificial Intelligence as a Catalyst for Innovation in Software Engineering [0.0]
  This article explores how AI serves as a powerful catalyst for enhancing agility and fostering innovation.<n>The integration of AI (specifically through Machine Learning (ML) and Natural Language Processing (NLP))facilitates the automation of tedious tasks.
  arXiv  Detail & Related papers  (2026-03-11T17:20:30Z)
• Compiler.next: A Search-Based Compiler to Power the AI-Native Future of Software Engineering [3.8060608371616778]
  We present Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems.<n>Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution.<n>We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search constructs, and interoperability between compilers.
  arXiv  Detail & Related papers  (2025-10-27T21:01:48Z)
• A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
  Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
  arXiv  Detail & Related papers  (2025-08-26T08:38:01Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm [9.900581015679935]
  We propose Neurosymbolic Software Engineering as a promising paradigm combining neural learning with symbolic (rule-based) reasoning.<n>This hybrid methodology aims to enhance efficiency, reliability, and transparency in AI-driven software engineering.
  arXiv  Detail & Related papers  (2025-05-04T22:10:21Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Challenges and Paths Towards AI for Software Engineering [55.95365538122656]
  We discuss progress in AI for software engineering in threefold manner.<n>First, we provide a structured taxonomy of concrete tasks in AI for software engineering.<n>Second, we outline several key bottlenecks that limit current approaches.
  arXiv  Detail & Related papers  (2025-03-28T17:17:57Z)
• Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future [0.0]
  The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user.
  arXiv  Detail & Related papers  (2024-10-28T13:03:09Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM)<n>NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
  The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
  arXiv  Detail & Related papers  (2020-05-27T11:56:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.