arXiv:2606. 05608v1 Announce Type: cross Abstract: For over half a century, software engineering has operated on a foundational premise: human engineers decompose problems, encode decision logic into static code, and manually adapt that code as requirements evolve.
By Zhenfeng Cao
The article examines the psychological costs that software professionals face when organizations adopt artificial intelligence (AI) in software engineering workflows. Through a case study involving 21 interviews at a large software development services company, the authors identify several negative impacts—accountability anxiety, craft identity disruption, erosion of meaning and satisfaction, increased cognitive load and workload, and uncertainty distress. They also describe how practitioners cope by restoring control, adopting protective adaptations, or absorbing the costs, arguing that AI adoption should be viewed as a human transition rather than merely a technological or organizational change.
By Adam Alami, Elda Paja, Abhishek Tiwari
Discover how leaders can build AI-ready organizations using clear strategy, training, governance, and accelerated innovation.
arXiv:2609.24348v1 Announce Type: cross
Abstract: AI coding agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level...
By Andreas Martin, Sandro Schwander
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
The report examines how software architects view the growing use of AI development agents in their field. A focus group of 22 industry and academic participants discussed current practices, trust, validation, governance, and educational implications, concluding that decision‑making, accountability, and guardrail authoring remain human responsibilities. They introduced the concept of harness engineering—building systems that govern AI‑assisted creation—and identified criticality and cognitive debt as key factors for calibrating human oversight.
By Uwe van Heesch, Olaf Zimmermann, Christian Kohls