cs.SE · 2026-06-25 · No. 34

Software Engineering, 2026-06-25.

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

    Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study

    Giulian Biolo, Michael Tezza, Yuanjun Gong, Fabio Massacci

    cs.SE · cs.AI

    Software vulnerability remediation is a cognitively demanding task that requires specialized security expertise often lacking in general developers. In the meantime, Large Language Models (LLMs) assisted tools show potential in vulnerability detection, location, and repair tasks. [Hypothesis:] While LLM-assistance is hypothesized to accelerate patching, it also risks introducing hallucinations or insecure code, leading to a higher likelihood...

    arxiv.org/abs/2606.25973 · PDF

  2. 02

    Evaluating LLMs on Real-World Software Performance Optimization

    Ezgi Sarıkayak, Wenchao Gu, Hesham Ghonim, Chunyang Chen

    cs.SE · cs.AI · cs.CL

    Software performance optimization is a notoriously complex and manual task. Despite the growing use of Large Language Models (LLMs) for code refinement, we still lack benchmarks that capture how optimization actually happens in real-world codebases. Existing frameworks often oversimplify the problem by focusing on isolated functions or a single performance metric, missing the critical trade-offs between execution time and memory footprint,...

    arxiv.org/abs/2606.25530 · PDF

  3. 03

    The impact of artificial intelligence on enterprise software user roles

    Isabel Unger, Elizangela Valarini, Martin Schrepp, Nina Hollender, Gabriela Rocha, Erik Bertram

    cs.SE · cs.AI

    Artificial Intelligence (AI) is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms. This qualitative study investigates how AI alters professional responsibilities within the context of SAP's Business Technology Platform (BTP), combining expert interviews (n=20) and a participatory workshop (n=24). The results reveal substantial shifts in...

    arxiv.org/abs/2606.25525 · PDF

  4. 04

    LibEvoBench: Probing Temporal Knowledge Stratification in Code Generation Models

    Daniele Cipollone, Sergey Titov, Maliheh Izadi, Egor Bogomolov, Arie van Deursen

    cs.SE · cs.AI

    Large software projects often depend on older versions of libraries, even as APIs continue to evolve across releases. This creates a challenge for LLMs: they must maintain knowledge of multiple API versions, not merely the latest or most common one. However, current LLMs are trained on temporally mixed corpora and lack explicit mechanisms for such version-specific reasoning, leading to anachronistic errors - calling APIs as they exist in a...

    arxiv.org/abs/2606.25402 · PDF

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