Aug 2026· International Journal of Innovative Science and Research Technology· 0 citations· 14 references
TL;DR
This paper reviews recent empirical literature to ask what the developer's job is shifting from typing code to directing agents that type code a change often summarized as a move from code generation to code orchestration.
Abstract
Software engineering is in the middle of a quiet but far-reaching handover. For most of the last decade, artificial
intelligence in the developer's tool chain meant auto complete: a model that finished a line, or occasionally a function, while
a human wrote and reviewed everything around it. That arrangement has started to break down. Coding agents such as
Claude Code, OpenAI's Codex CLI, Google's Jules, Devin, and Open Hands can now read an entire repository, plan a multifile change, run the test suite, and iterate on failures with little moment-to-moment supervision. The developer's job is
shifting from typing code to directing agents that type code a change often summarized as a move from code generation to
code orchestration. This paper reviews recent empirical literature to ask what that shift is actually producing.
An exploratory review of the emerging gray literature, which largely agrees on what a well-engineered loop contains: triggered agent runs bounded by machine-checkable stop conditions, persistent state files, verifier sub-agents, token budgets, and defined points of escalation to humans.
Jai Lal Lulla, Vahram Nersesyan, Seyedmoein Mohsenimofidi et al.· 0 citations
This paper synthesizes peer-reviewed software-engineering research, university studies, benchmark audits, production reports from major technology companies, developer telemetry, and cost-management evidence released primarily from 2024 through September 2026 and proposes four engineering concepts: the Agentic SDLC Thr...
The growing adoption of Large Language Models (LLMs) in Software Engineering has reinforced the expectation that coding activities can be largely automated. However, this perception may represent yet another historical search for a solution capable of eliminating the inherent challenges of software development. This ar...
This work measures only rules whose condition is decidable from bytes and whose consequence is a security exposure, a configuration that cannot work as written, or a departure from the Agent Skills specification, and validates every finding before it counts.
Benjamin Kapner, Carmel Soceanu, A. Petrunin et al.· 0 citations
An empirical study of whether complete software artifacts generated by LLM coding agents can be executed in a clean environment using only the code, dependency specifications, and instructions the agent provides suggests that coding-agent evaluation should treat clean-environment executability as a first-class metric a...
The AgentCodeReview system is presented, a multi-agent system that is able to conduct explainable code review and automated bug repair by leveraging software engineering agents with different code review tasks and its utility and extensibility to the field of explainable AI in software quality assurance are demonstrate...
B. N, T. Manasa· International journal of com...· 0 citations
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