cs.SE · 2026-09-16 · No. 115

Software Engineering, 2026-09-16.

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

01 — The papers

7 entries
  1. 01

    Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead

    Fengshuo Liu, Ying Liu, Ruize Sun, Lie Luo, Siyuan Guo

    cs.SE · cs.AI

    Small differences on coding-agent leaderboards are often read as an ordering of systems. We audit whether the published verdicts support this reading, using 254 SWE-bench submissions across four splits without running models. On Verified, the leading two entries each resolve 396 of 500 instances. The top ten share 285 successes and 51 failures, leaving 164 instances that distinguish their outcomes. Frontier solution sets have median nesting...

    arxiv.org/abs/2609.17394 · PDF

  2. 02

    Type-IV Code Clone Detection via Layer-Wise Non-Contrastive Representation Learning

    Luciano Marchezan, Kevin Delcourt, Eugene Syriani, Houari Sahraoui

    cs.SE · cs.LG

    Software clones are fragments of code that are similar or functionally equivalent to each other. They pose significant challenges for maintenance, refactoring, and bug detection. Detecting Type-IV clones, which are semantically equivalent but may differ syntactically, is particularly difficult for traditional token- or syntax-based methods. Recent machine learning approaches rely on contrastive learning, which requires careful negative...

    arxiv.org/abs/2609.17338 · PDF

  3. 03

    After the Party: Governing What a Viral Agent-Skill Ecosystem Left Behind

    Yunpeng Xiong, Ting Zhang

    cs.SE · cs.AI · cs.CY

    AI agents increasingly act through agent skills, i.e., natural-language instructions, that direct a host agent toward shell, network, credential, file, and process actions, and public registries distribute them at scale. In the first half of 2026, the OpenClaw AI agent went viral, and its public skill registry boomed: the observable stock nearly doubled in 91 days, and a majority of the listings visible in June were created in just two...

    arxiv.org/abs/2609.17274 · PDF

  4. 04

    Grounding SWE-Agent Decisions in Architecture-0 Design: Navigating Unknown Unknowns through Physical Mapping

    Zhongkai Wang, Yan Liu

    cs.SE · cs.AI

    Autonomous Software Engineering Agents (SWE-Agents) excel in deterministic coding tasks but struggle with Architecture 0, the nascent system design phase plagued by implicit engineering constraints, or Unknown Unknowns (UUs) that are rarely stated explicitly. To investigate how agents navigate UUs, we explore a progressive trajectory across pure-text self-play, tool-augmented feedback, and external physical mapping. Our empirical analysis...

    arxiv.org/abs/2609.17221 · PDF

  5. 05

    RepoAtlas: Guiding Coding Agents via Evolving Multimodal Repository Views

    Yunxiang Zhang, Haiquan Wang, JiaWei Guo, Hanyang Xia, Yan Chen, Tong Chen, Zhang Zhiwei, Junchen Ye

    cs.SE · cs.AI

    Large language model (LLM)-powered coding agents have made rapid progress in automating software engineering tasks, yet repository-level issue resolution remains challenging. Beyond generating a plausible patch, an agent must localize relevant code across interdependent files and maintain repository context that is both sufficient and focused. Code graphs expose non-local relations, but linear text interfaces obscure their topology; rendering...

    arxiv.org/abs/2609.16936 · PDF

  6. 06

    AI Policies: Help or Hindrance? A Software Developer's Perspective

    Samuel Ferino, Rashina Hoda, John Grundy, Christoph Treude, Hashini Gunatilake

    cs.SE · cs.AI

    AI policies introduced by software organisations to mitigate LLM-related risks such as sensitive information leaks and unauthorised usage are not useful if software developers do not engage with them. We draw on 19 software developer interviews to show how AI policies help and hinder developers. We suggest approaches to support managers and decision makers with a developer-centric approach to introducing AI policies.

    arxiv.org/abs/2609.16496 · PDF

  7. 07

    Protocol-Preserving Context Trimming for Agentic Workflows: Benefits, Failure Regimes, and Budget Guardrails

    Harish Gaggar

    cs.SE · cs.AI

    Agentic large language model (LLM) systems rely on long interaction histories to preserve instructions, tool states, intermediate decisions, and unresolved dependencies, but unrestricted context growth increases computational cost and can reduce efficiency. This study evaluates protocol-preserving context trimming as a reliability-constrained approach for multi-step agentic workflows. Five trimming strategies - recency-based, relevance-based,...

    arxiv.org/abs/2609.16461 · PDF

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