cs.SE · 2026-09-30 · No. 129

Software Engineering, 2026-09-30.

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

    AgentBug-Smith: Automatically Reproducing Real-World Harness Bugs in Agentic Systems

    Yiming Cheng, Alfin Wijaya Rahardja, Mengshi Zhang, Zihao Chen, Zhenpeng Chen, Yiling Lou

    cs.SE · cs.AI

    Agent harness bugs exhibit unique characteristics and remain challenging for state-of-the-art software agents to repair. Progress in this area is further hindered by existing benchmarks, which contain only a small and fixed number of executable harness bugs while requiring hundreds of human hours to construct. This work presents AgentBug-Smith, an automated harness bug reproduction approach that continuously discovers and reproduces...

    arxiv.org/abs/2609.37864 · PDF

  2. 02

    Is manual software optimization a thing of the past?

    Pavlin G. Poličar, Martin Špendl, Tomaž Hočevar

    cs.SE · cs.AI

    Scientific software is increasingly required to process larger datasets while maintaining acceptable execution times. Software optimization traditionally requires substantial expertise in programming, algorithms, and numerical methods. Recent advances in large language models (LLMs) offer the possibility of automating much of this process. We investigate whether LLM-based agents can autonomously achieve substantial performance improvements in...

    arxiv.org/abs/2609.37849 · PDF

  3. 03

    Retrieve, Reproduce, Reveal: Dissecting Retrieval-Augmented Software Vulnerability Detection

    Sabrina Kaniewski, Tim Krämer, Julius Bächle, Markus Enzweiler, Michael Menth, Tobias Heer

    cs.SE · cs.AI

    Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding predictions in retrieved vulnerability knowledge, such as vulnerability reports. However, existing RAG-based software vulnerability detection (RAG4SVD) systems are often evaluated using proprietary models, which challenges open science and reproducibility. Further, studies use different datasets,...

    arxiv.org/abs/2609.37669 · PDF

  4. 04

    Independent Verification Paths Are Not Independent: A Case Study of Common-Mode Failure in a Satellite Catalogue Pipeline

    Fabio Rovai

    cs.SE · cs.AI · cs.DB

    A common safeguard for a data pipeline is redundant computation: derive each published number by two routes built on different technology and refuse to exit when they disagree. We report one such gate failing, in a cross-catalogue integrity study of two open registers of Earth-orbiting objects. A gate comparing a set-based Python path with SPARQL queries over the emitted RDF graph printed ALL CROSS-CHECKS AGREE on seven counts. Three were...

    arxiv.org/abs/2609.37603 · PDF

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