The role of the software engineer is fundamentally changing. AI-assisted software development has, in many cases, become a new norm, fostering a demand for unprecedented speed in production. Yet the transition from traditional coding to AI-powered development is not as simple as receiving a boost to productivity. It requires both brand-new skills on the part of the developer and a more fundamental rethinking of how software — especially at scale — is generated. In this rapidly evolving field, new understandings of how to capture user intent, ensure system quality at AI-generative scales, and train the next generation of developers are essential.
Beyond Code Report
Beyond Code: The Workshop
To better understand and address these challenges, the Computing Community Consortium (CCC) held its Beyond Code: Engineering Trustworthy Software Systems with AI at Scale workshop — co-sponsored by the IEEE Computer Society — from February 25-26, 2026 in San Francisco, CA. The workshop brought together experts in artificial intelligence, software engineering, programming languages, cybersecurity, and systems engineering to assess the evolving role of AI in developing complex and large-scale software systems.
CCC’s newest report contains findings and recommendations from the Beyond Code workshop. The report is authored by Randal Burns (Johns Hopkins University), Sebastian Elbaum (University of Virginia), Gabrielle Allen (University of Wyoming), Nils Aschenbruck (Osnabrück University), Terry Benzel (University of Southern California), William Gropp (University of Illinois Urbana-Champaign), Rick Kazman (University of Hawaii), and Manish Parashar (University of Utah). It presents critical challenges and recommendations for the future of large-scale AI-generated software development.
Central Topics
Participants of the Beyond Code workshop worked to identify current gaps and struggles in the current capacity to generate large-scale AI-generated software systems. In addition to those findings, they collaborated to find the best ways forward given the rapid changes taking place in the area, setting out concrete recommendations for the future of the field. Some of the topics central to the report include:
- The intent gap: natural language as a lossy interface
Natural language is the primary interface to code models, but its ambiguity and lack of formal semantics lose information in translation.
- Machine representations beyond human-centric syntax
First-generation models are constrained by syntax designed for human readability, which forces inefficient trial-and-error generation loops.
- Quality attributes and formal assurance
Models produce functional logic but fail on performance, security, and numerical accuracy. Generation velocity and volume outpace human auditing capacity and make traditional quality metrics obsolete.
- Security as an intrinsic property
Goal-driven security interfaces and secure-by-construction generation, rather than security applied after the fact.
- Multi-agent orchestration and deployment
Operating teams of agents complicates things. It introduces coordination friction, composability failures across siloed outputs, and integration liabilities at deployment.
- Design rationale, maintainability, and legacy systems
As developers distance themselves from code generation, the rationale behind design choices is lost. Codebases become confusing and the capacity to maintain legacy systems diminishes.
- The developer role, deskilling, and education
Generative tools amplify existing productivity differences. They can enhance expert capability while also creating intellectual dependency and deskilling among the less-experienced. The role of software engineer itself is changing to be a multi-agent orchestrator. Curricula and training must follow.
- The research ecosystem
With industry commanding frontier models and compute infrastructure, academia risks relegation to post-hoc evaluation of commercial artifacts.
- Training data transparency and IP governance
Frameworks for data transparency and IP governance for AI-generated code, including membership inference and provenance tracking.
The report poses recommendations for tackling these pressing challenges within the computing research community.
Read the Full Report
The Beyond Code: Engineering Trustworthy Software Systems with AI at Scale report is available now. Its recommendations aim to create an ecosystem where large-scale AI-generated software is complex, interdependent, and, above all, trustworthy. We encourage all members of the computing community to read the full report here.
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This material is based upon work supported by the U.S. National Science Foundation (NSF) under Award Nos. 1734706 and 2300842. This award supports the Computing Community Consortium (CCC), a programmatic committee of the Computing Research Association (CRA). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
