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From Coding Assistants to Self-Healing Terminals: NeuraOS A Critical Review of AI-Driven Autonomous Command Execution and Error Recovery on Linux Systems
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: 10.64388/IREV10I4-1724405
Abstract
The Linux command-line interface continues to serve as the primary control surface for software development, systems administration, and cloud operations, yet it executes instructions syntactically, without semantic awareness of user intent and without built-in mechanisms for diagnosing or repairing runtime failures. Over roughly the last decade, three largely distinct research communities have each addressed part of this problem: large language model (LLM)-based coding assistants embedded in integrated development environments (IDEs), natural-language-to-command translation systems that lower the barrier to shell usage, and operating-systems research on self-healing and autonomic computing that targets distributed and cloud infrastructure rather than the interactive terminal. This paper presents a structured, critical review of these three strands, together with the log-based anomaly-detection and AIOps literature that supplies much of the diagnostic machinery such systems would require. Literature was retained where it addressed semantic command interpretation, real-time or near-real-time failure diagnosis, autonomous or semi-autonomous recovery, or persistence of prior failure-resolution experience. The reviewed work is organized along four analytical dimensions — runtime integration, autonomy level, diagnostic depth, and knowledge retention — and compared qualitatively across these dimensions. The synthesis shows that autonomy and diagnostic sophistication increase substantially when moving from IDE assistants toward self-healing infrastructure research, but this increase is accompanied by a shift away from the single-operator, single-machine terminal context, and that no reviewed system combines real-time in-terminal diagnosis, multi-pathway recovery, and durable per-machine experiential learning while remaining confined to user space. The review further discusses recurring safety and governance themes — confidence-gated autonomy, privilege-aware execution, and explanation-first human oversight — that constrain the design space for any terminal-level orchestration layer, and it identifies four concrete research gaps together with a near-, mid-, and long-term research roadmap. The review does not claim that any single technology described is either novel or previously unstudied in isolation; its contribution is the cross-domain synthesis and the identification of the specific combination of properties that remains unaddressed.
Keywords
Self-Healing Systems; Autonomic Computing; Autonomous Error Recovery; Intelligent Shells; Natural-Language Command Interfaces; Large Language Models; AIOps; Linux Command-Line Interface; Human-in-the-Loop AI; Systematic Literature Review
References
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How to cite this paper
@article{1724405,
author = {Prajwal Pawar, Dr. Brijendra Gupta},
title = {From Coding Assistants to Self-Healing Terminals: NeuraOS A Critical Review of AI-Driven Autonomous Command Execution and Error Recovery on Linux Systems},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4341-4362},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724405.pdf},
abstract = {The Linux command-line interface continues to serve as the primary control surface for software development, systems administration, and cloud operations, yet it executes instructions syntactically, without semantic awareness of user intent and without built-in mechanisms for diagnosing or repairing runtime failures. Over roughly the last decade, three largely distinct research communities have each addressed part of this problem: large language model (LLM)-based coding assistants embedded in integrated development environments (IDEs), natural-language-to-command translation systems that lower the barrier to shell usage, and operating-systems research on self-healing and autonomic computing that targets distributed and cloud infrastructure rather than the interactive terminal. This paper presents a structured, critical review of these three strands, together with the log-based anomaly-detection and AIOps literature that supplies much of the diagnostic machinery such systems would require. Literature was retained where it addressed semantic command interpretation, real-time or near-real-time failure diagnosis, autonomous or semi-autonomous recovery, or persistence of prior failure-resolution experience. The reviewed work is organized along four analytical dimensions — runtime integration, autonomy level, diagnostic depth, and knowledge retention — and compared qualitatively across these dimensions. The synthesis shows that autonomy and diagnostic sophistication increase substantially when moving from IDE assistants toward self-healing infrastructure research, but this increase is accompanied by a shift away from the single-operator, single-machine terminal context, and that no reviewed system combines real-time in-terminal diagnosis, multi-pathway recovery, and durable per-machine experiential learning while remaining confined to user space. The review further discusses recurring safety and governance themes — confidence-gated autonomy, privilege-aware execution, and explanation-first human oversight — that constrain the design space for any terminal-level orchestration layer, and it identifies four concrete research gaps together with a near-, mid-, and long-term research roadmap. The review does not claim that any single technology described is either novel or previously unstudied in isolation; its contribution is the cross-domain synthesis and the identification of the specific combination of properties that remains unaddressed.},
keywords = {Self-Healing Systems; Autonomic Computing; Autonomous Error Recovery; Intelligent Shells; Natural-Language Command Interfaces; Large Language Models; AIOps; Linux Command-Line Interface; Human-in-the-Loop AI; Systematic Literature Review},
month = {October},
doi = {https://doi.org/10.64388/IREV10I4-1724405}
}