mlsanityData to 7 Oct 2026

The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE

arXiv 2604.15468 ↗First posted 16 Apr 2026cs.SE cs.AIcited by 2

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

Abstract (arXiv)

AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engineering. Foundation models get stronger, agents can plan and act across multiple steps, and tasks such as scaffolding, routine test generation, straightforward bug fixing, and small integration work look more exposed than they did only a few years ago. The result is visible unease not only among students and junior developers, but also among experienced practitioners who worry that hard-won expertise may lose value. This paper argues for a different reading. The important shift is not that software engineering loses relevance. It is that the thing being engineered expands beyond executable code to semi-executable artifacts; combinations of natural language, tools, workflows, control mechanisms, and organizational routines whose enactment depends on human or probabilistic interpretation rather than deterministic execution. The Semi-Executable Stack is introduced as a six-ring diagnostic reference model for reasoning about that expansion, spanning executable artifacts, instructional artifacts, orchestrated execution, controls, operating logic, and societal and institutional fit. The model helps locate where a contribution, bottleneck, or organizational transition primarily sits, and which adjacent rings it depends on. The paper develops the argument through three worked cases, reframes familiar objections as engineering targets rather than reasons to dismiss the transition, and closes with a preserve-versus-purify heuristic for deciding which legacy software engineering processes, controls, and coordination routines should be kept and which should be simplified or redesigned. This paper is a conceptual keynote companion: diagnostic and agenda-setting rather than empirical.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 22 of its 47 references that are in the map. First posted on arXiv 16 Apr 2026.

Builds on

22 of 47 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics27 Mar
  2. 2025Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy1 Mar
  3. 2024Large Language Model-Based Agents for Software Engineering: A Survey4 Sep
  4. 2024GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making19 Aug
  5. 2023Towards Autonomous Testing Agents via Conversational Large Language Models8 Jun

Other references · 17

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  2. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
  3. 2023Large Language Models for Software Engineering: Survey and Open Problemscited by 178
  4. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 126
  5. 2025Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivitycited by 93
  6. 2021Software Engineering for AI-Based Systems: A Surveycited by 68
  7. 2020A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challengescited by 33
  8. 2024Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositoriescited by 16
  9. 2025Automating a Complete Software Test Process Using LLMs: An Automotive Case Studycited by 8
  10. 2018Ways of Applying Artificial Intelligence in Software Engineeringcited by 7
  11. 2022Similarity-based web element localization for robust test automationcited by 3
  12. 2022Cognition in Software Engineering: A Taxonomy and Survey of a Half-Century of Researchcited by 2
5 more
  1. 2025An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AIcited by 2
  2. 2019The Unfulfilled Potential of Data-Driven Decision Making in Agile Software Developmentcited by 1
  3. 2020Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelinescited by 1
  4. 2025Cross-Functional AI Task Forces (X-FAITs) for AI Transformation of Software Organizationscited by 1
  5. 2026Bridging the Socio-Emotional Gap: The Functional Dimension of Human-AI Collaboration for Software Engineeringcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Rethinking Software Engineering for Agentic AI Systems
  2. 2026Agentic Software: How AI Agents Are Restructuring the Software Paradigm
  3. 2026From Determinism to Delegation: AI-Native Software Engineering and the Evolution of the Agentic Engineer
  4. 2025Toward Agentic Software Engineering Beyond Code: Framing Vision, Values, and Vocabulary
  5. 2026How Do Practitioners Build SE Agents? Insights from a Mixed-Methods Study
  6. 2025Agentic Software Engineering: Foundational Pillars and a Research Roadmap
  7. 2026Reproducible, Explainable, and Effective Evaluations of Agentic AI for Software Engineering
  8. 2026Agentic AI in the Software Development Lifecycle: Architecture, Empirical Evidence, and the Reshaping of Software Engineering