mlsanityData to 7 Oct 2026

A Lean and Spec-Driven AI-Assisted Software Development Lifecycle for Applied AI Education: The AI-SDLC Approach

arXiv 2609.24348 ↗First posted 21 Sep 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

AI coding agents increasingly support software development beyond code completion, including planning, implementation, testing, and repository-level task execution. Their practical use, however, often remains only weakly connected to established software engineering practices. The aim of this work is to develop and evaluate a lightweight, spec-driven lifecycle for governed agentic software engineering. The lifecycle combines established software engineering practices with repository-local guidance through specifications, AGENTS.md, and phase-specific agent skill files. The approach was developed in the context of the FHNW course AI-assisted Software Development and applied by students to business-oriented software use cases. Its educational and practical applicability is explored through a student survey combining closed rating items with open-ended questions. The contribution of this work is a process-oriented framework that enables AI coding agents to operate with bounded autonomy within an explicit, reviewable, and test-oriented software development lifecycle.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 27 references that are in the map. First posted on arXiv 21 Sep 2026.

Builds on

7 of 27 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents?12 Feb
  2. 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub18 Sep
  3. 2025A Survey on Code Generation with LLM-based Agents31 Jul
  4. 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI26 May
  5. 2025Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents8 May

Other references · 2

  1. 2025Is Vibe Coding Safe? Benchmarking Vulnerability of Agent-Generated Code in Real-World Taskscited by 18
  2. 2026Spec-Driven Development:From Code to Contract in the Age of AI Coding Assistantscited by 14

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  1. 2026SDAD: Spec-Driven Agentic Development for the AI-Native SDLC
  2. 2026Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering
  3. 2026How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests
  4. 2025Agentsway -- Software Development Methodology for AI Agents-based Teams
  5. 2026Agentic Agile-V: From Vibe Coding to Verified Engineering in Software and Hardware Development
  6. 2026Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
  7. 2026From Prompt to Process: a Process Taxonomy and Comparative Assessment of Frameworks Supporting AI Software Development Agents
  8. 2026Rethinking Software Engineering for Agentic AI Systems