arXiv:2608. 19674v1 Announce Type: cross Abstract: Computing has been an astonishing success - but the accumulated technical debt exposes us all to huge costs in business and societal risk.
By Peter Sewell, Jean Pichon-Pharabod
arXiv:2608. 10450v1 Announce Type: cross Abstract: Complex software systems develop over timescales that exceed the lifespan of any individual coding agent.
By Beichen Huang, Zhenyu Liang, Bowen Zheng, Ran Cheng
The paper examines how AI coding agents are evolving beyond simple autocomplete to perform complex tasks such as repository inspection, multi-file editing, tool execution, test writing, pull request creation, and long-duration work with minimal supervision. It highlights that while these agents boost coding activity, significant bottlenecks remain in review, integration, testing, security, deployment, and production operations, and that the economics of software development are shifting toward variable token, tool, sandbox, CI, and rework costs. The authors synthesize recent research and industry data to propose four engineering concepts—Agentic SDLC Throughput Paradox, Production-Qualified Change, Verification Tax, and an Agentic SDLC Control Plane—to guide the allocation of autonomy within cost, reliability, and human-attention constraints, ultimately reframing the research focus to production-qualified value per dollar, reviewer-hour, and operational risk.
By Happy Bhati
The paper introduces Spec-Driven Agentic Development (SDAD), a framework that leverages large language models to ingest extensive functional requirement documents and repository context in a single workflow, turning specification quality into the engine for autonomous software delivery. SDAD blends disciplined upfront formalisation with rapid implementation, encompassing intent capture, machine‑readable specifications, agentic synthesis, and multi‑agent verification with human sign‑off. It positions AI‑code as a fourth production paradigm, compares it to traditional Waterfall and Agile approaches, and extends the model to team role evolution, quantitative governance metrics, and a staged migration blueprint for practical adoption.
By Vu Hung Nguyen, Thanh Nguyen
arXiv:2608. 06640v1 Announce Type: cross Abstract: The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity.
By Michael Tran, Fred Lewis, Kun Yang, Saksham Thakur, Aditya Kini, Aditya Patil, Milad Hashemi, Parthasarathy Ranganathan
arXiv:2606. 30182v1 Announce Type: new Abstract: AI models are rapidly improving at autonomous coding, as shown by benchmark progress and one-off demonstrations such as AI implementing a C compiler.
By Tom Adamczewski, David Owen, David Rein, Florian Brand, Giles Edkins, Allen Hart, Daniel O'Connell