cs.SE · 2026-08-31 · No. 101

Software Engineering, 2026-08-31.

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

    On the Maintenance and Co-evolution of Agent Plugins: An Empirical Study of Claude Code Plugin Marketplaces

    Ahmed Hereiz, Yingzhe Lyu, Hao Li, Bram Adams, Ahmed E. Hassan

    cs.SE · cs.AI

    AI coding agents, software tools that automate development tasks through reasoning and tool use, are increasingly extended through plugin marketplaces, yet the structure, maintenance, and co-evolution dynamics of these emerging repositories remain empirically unexplored. Unlike traditional software packages that deliver functionality through source code, agent plugins deliver functionality through a combination of natural-language instruction...

    arxiv.org/abs/2608.28497 · PDF

  2. 02

    Post-Edit Re-Verification in Simulator-Backed Engineering Agents: A Controlled Comparison of Verification-Cadence Guidance

    Qingchuan Zhu, Shuyue Tong, Pengju Ren

    cs.SE · cs.AI

    Engineering agents that interact with external simulators may need to coordinate design modification with reacquisition of engineering evidence for the modified state. We ask whether first post-edit re-verification changes when explicit verification-cadence guidance is retained versus omitted while verification-relevant state/facts are held constant. Cadence-Guided (CG) retained an instruction to request a new simulation after a substantive...

    arxiv.org/abs/2608.28147 · PDF

  3. 03

    Antipatterns in AI-assisted Qualitative Data Analysis: A Catalog of Temptations and Pitfalls for Software Engineering Researchers

    Rashina Hoda, Carolyn Seaman, Victoria Gomes, Rodrigo Spinola

    cs.SE · cs.AI

    AI-assisted qualitative data analysis (QDA) offers unprecedented opportunities to streamline software engineering (SE) research, yet uncritical use risks compromising analytical rigor and flooding the field with accelerated production of low-quality research. While tactical best practices will naturally evolve over time, SE researchers currently lack strategic guidance to identify and mitigate methodological risks when attempting AI-assisted...

    arxiv.org/abs/2608.27927 · PDF

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