arXiv:2609.24348v1 Announce Type: cross Abstract: AI coding agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level...
arXiv:2606. 16842v1 Announce Type: cross Abstract: Teaching Software Engineering for AI-enabled systems entails addressing the integration of AI components within full-scale software architectures under realistic constraints.
The paper titled "Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025" examines how experienced developers employ AI agents in software development. Through field observations and surveys, it finds that developers value agents for productivity but maintain control over design and implementation to ensure quality. They use agents as collaborative tools rather than full delegation, selecting tasks based on suitability and leveraging their expertise to guide agent behavior.
arXiv:2607. 21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows.
arXiv:2606. 21894v2 Announce Type: replace-cross Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers?
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.
arXiv:2512. 22256v2 Announce Type: replace-cross Abstract: Software issue resolution aims to address real-world issues in software repositories based on natural language descriptions provided by users, and represents a key aspect of software maintenance.
arXiv:2606. 01152v1 Announce Type: cross Abstract: The work of a professional software engineer has begun to consist, increasingly, of directing agents rather than writing code, and the empirical evidence for the shift is now several years deep.
arXiv:2606. 05608v2 Announce Type: replace-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.
arXiv:2608. 16318v1 Announce Type: cross Abstract: Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and explain source code.
DataCanvas-EDU is an agentic framework that lets instructors guide the creation of synthetic datasets for business analytics courses. Instructors set teaching goals and desired patterns via conversation, and an AI agent writes generation code, verifies the data, and produces assignments, reference solutions, and rubrics. The process is organized into four phases—Plan, Create, Verify/Test Analysis, and Evaluate—to streamline case preparation and enable students to explore new patterns with AI.
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.