cs.SE · 2026-06-11 · No. 20

Software Engineering, 2026-06-11.

4 new papers in cs.SE. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Rule Taxonomy and Evolution in AI IDEs: A Mining and Survey Study

    Guangzong Cai, Ruiyin Li, Peng Liang, Zengyang Li, Mojtaba Shahin

    cs.SE · cs.AI

    The adoption of AI-powered Integrated Development Environments (AI IDEs) has introduced "Rules" as a novel software artifact, allowing developers to persistently inject project-specific constraints and architectural guidelines into the context of Large Language Models (LLMs). Despite their role in aligning AI behavior with developer intent, the taxonomy, evolution, and practical impact of these rules remain largely unexplored. To bridge this...

    arxiv.org/abs/2606.12231 · PDF

  2. 02

    Exploration Structure in LLM Agents for Multi-File Change Localization

    Akeela Darryl Fattha, Kia Ying Chua, Lingxiao Jiang, Laura Wynter

    cs.SE · cs.AI

    Software engineering tools increasingly rely on LLM based agents to localize files to change to resolve a software issue. Most AI agents explore repositories linearly, that is, visiting one directory or file per step. We postulate that this is a structural mismatch for changes that span several subsystems. We compare linear sequential exploration against non-linear, domain-scoped parallel agentic exploration. Using SWE Bench Pro as initial...

    arxiv.org/abs/2606.11976 · PDF

  3. 03

    Characterizing Software Aging in GPU-Based LLM Serving Systems

    Domenico Cotroneo, Bojan Cukic

    cs.SE · cs.AI

    This paper proposes an empirical methodology to study software aging in GPU-based LLM serving systems. Traditional aging studies focus on CPU-centric software with relatively regular workloads; LLM serving is different, spanning a Python host and a CUDA device, handling requests whose cost varies by orders of magnitude, and relying on rapidly evolving software stacks. We run a 216-hour campaign across six co-located deployments under...

    arxiv.org/abs/2606.11916 · PDF

  4. 04

    Agents All the Way Down; A Methodology for Building Custom AI Agents from Substrate to Production

    Marc Alier Forment, Juanan Pereira, Francisco José García-Peñalvo, María José Casañ Guerrero

    cs.SE · cs.AI

    Custom AI agents areagents that live inside their own application, talk to their own data and tools, enforce their own security boundaries, and carry their own brand and audit trail. What separates them from the general-purpose tier is fit, not capability: each is built for one job, by the engineer who will maintain it. No published practice sets out how to build one end to end. The pieces are everywhere (function-calling APIs, the Model...

    arxiv.org/abs/2606.11869 · PDF

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