cs.SE · 2026-06-08 · No. 17

Software Engineering, 2026-06-08.

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

01 — The papers

3 entries
  1. 01

    Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills

    Chuan Xiao, Zhengbo Jiao, Shaobo Wang, Wei Wang, Bing Zhao, Hu Wei, Linfeng Zhang, Lin Qu

    cs.SE · cs.AI

    LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by the availability of high-quality SWE tasks. Existing synthetic data methods typically create tasks through fixed mutation or bug-injection procedures, making the resulting distributions largely independent of the agent's own weaknesses and training progress. We introduce Socratic-SWE, a...

    arxiv.org/abs/2606.07412 · PDF

  2. 02

    MetaConfigurator: AI-Assisted RDF Authoring from JSON Data

    Felix Neubauer, Mahdi Jafarkhani, Kenichi Endo, Jürgen Pleiss, Benjamin Uekermann

    cs.SE · cs.AI

    Scientific workflows increasingly generate structured JSON data that is easy to exchange but difficult to interpret consistently across systems due to lacking semantic interoperability. While JSON Schema ensures structural validation, it provides no native support for Linked Data semantics. This paper presents an RDF Authoring View extending the open-source JSON Schema editor MetaConfigurator, enabling researchers to transform existing JSON,...

    arxiv.org/abs/2606.07094 · PDF

  3. 03

    LLM Agent-Assisted Reverse Engineering with Quantitative Readability Metrics

    Neil Archibald, Ruben Thijssen

    cs.SE · cs.AI

    Automatic decompilers produce functionally correct but often unreadable C code. This paper addresses one stage of the reverse engineering workflow: improving the readability of decompiled code using LLM agents guided by quantitative metrics. We present a three-phase research evolution. Phase 1 (tool-driven steering via Ghidra MCP) suffered from incomplete coverage and inconsistent improvements due to lack of quantitative guidance. Phase 2...

    arxiv.org/abs/2606.06838 · PDF

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