# The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE arXiv: https://arxiv.org/abs/2604.15468 ยท HTML: https://mlsanity.com/paper/2604.15468/ ## Key facts - Cited by 2 papers in the map (data through 14 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. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 22 of 22) - [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023) - [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023) - [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023) - [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024) - [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022) - [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025) - [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021) - [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020) - [Prompting in the Wild: An Empirical Study of Prompt Evolution in Software Repositories](https://mlsanity.com/paper/2412.17298/) (2024) - [Towards Autonomous Testing Agents via Conversational Large Language Models](https://mlsanity.com/paper/2306.05152/) (2023) - [Automating a Complete Software Test Process Using LLMs: An Automotive Case Study](https://mlsanity.com/paper/2502.04008/) (2025) - [Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy](https://mlsanity.com/paper/2503.00481/) (2025) - [Ways of Applying Artificial Intelligence in Software Engineering](https://mlsanity.com/paper/1802.02033/) (2018) - [GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making](https://mlsanity.com/paper/2408.09785/) (2024) - [GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics](https://mlsanity.com/paper/2503.21735/) (2025) - [Similarity-based web element localization for robust test automation](https://mlsanity.com/paper/2208.00677/) (2022) - [Cognition in Software Engineering: A Taxonomy and Survey of a Half-Century of Research](https://mlsanity.com/paper/2201.05551/) (2022) - [An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI](https://mlsanity.com/paper/2501.15691/) (2025) - [The Unfulfilled Potential of Data-Driven Decision Making in Agile Software Development](https://mlsanity.com/paper/1904.03948/) (2019) - [Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines](https://mlsanity.com/paper/2005.09959/) (2020) - [Cross-Functional AI Task Forces (X-FAITs) for AI Transformation of Software Organizations](https://mlsanity.com/paper/2505.10021/) (2025) - [Bridging the Socio-Emotional Gap: The Functional Dimension of Human-AI Collaboration for Software Engineering](https://mlsanity.com/paper/2601.19387/) (2026) ## Built on by (most cited first, 2 of 2) - [Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networks](https://mlsanity.com/paper/2606.01975/) (2026) - [From Traceability to Justifiability: Accountability Structures in Agentic Software Engineering](https://mlsanity.com/paper/2608.23610/) (2026) ## 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.