cs.SE · 2026-05-25 · No. 9

Software Engineering, 2026-05-25.

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

01 — The papers

6 entries
  1. 01

    AI Assurance: A Comprehensive Testing Strategy for Enterprise AI Systems

    Chitra Badagi, Divye Singh, Animesh Sen, Adinath Shirsath

    cs.SE · cs.AI

    Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software quality assurance was never designed to address. These systems are probabilistic, context-sensitive and emergent: they cannot be verified to be correct in the classical sense, but only evaluated with increasing confidence. This paper presents a comprehensive assurance strategy for enterprise AI...

    arxiv.org/abs/2605.23459 · PDF

  2. 02

    Philosophical Dispositions as Behavioral Constraints for AI-Assisted Code Review: An Empirical Study

    Kaushal Bansal

    cs.SE · cs.AI

    AI-assisted code review tools typically operate as generic "expert reviewer" agents, producing homogeneous findings regardless of the analysis type needed. We present a system that constrains AI reviewer behavior through philosophical dispositions -- coherent personality lenses grounded in specific epistemological traditions (Pyrrhonist Skepticism, Navya-Ny=aya logic, Diogenes' Cynicism, Confucian relational ethics) that direct attention to...

    arxiv.org/abs/2605.23108 · PDF

  3. 03

    Security of LLM-generated Code: A Comparative Analysis

    Srivathsan G Morkonda, Mahmoud Selim, Hala Assal

    cs.SE · cs.AI · cs.CR

    The majority of software developers use or are planning to use Artificial Intelligence (AI) tools in their development processes. Their top reasons include improving productivity and faster learning. In fact, Large Language Model (LLM)-generated code is currently in production, including in major tech companies. However, concerns were raised about the risks associated with the use of AI tools to generate code. In this paper, we focus our...

    arxiv.org/abs/2605.23091 · PDF

  4. 04

    A measurement substrate for agentic Kubernetes operations: Methodology and a case study in retrieval-compounding falsification

    Joshua Odmark, Gideon Rubin, Deon van der Vyver

    cs.SE · cs.AI

    Empirical claims about autonomous Kubernetes operations agents are largely unfalsifiable. Published work reports observational results without controlled comparisons against an agent-disabled baseline, selection bias is endemic, pre-registered decision matrices are absent, and samples are typically too small for the noise level of the underlying scoring system. The cause is the same gap that limits the agents themselves: code agents have a...

    arxiv.org/abs/2605.23058 · PDF

  5. 05

    LLM Code Smells: A Taxonomy and Detection Approach

    Zacharie Chenail-Larcher, Brahim Mahmoudi, Naouel Moha, Quentin Stiévenart, Florent Avellaneda

    cs.SE · cs.AI

    Large Language Models (LLMs) are increasingly integrated into software systems for diverse purposes, due to their versatility, flexibility, and ability to simulate human reasoning to some extent. However, poor integration of LLM inference in source code can undermine software system quality. Therefore, inadequate LLM integration coding practices must be documented to help developers mitigate such issues. Following our earlier work on LLM code...

    arxiv.org/abs/2605.22976 · PDF

  6. 06

    Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents

    Ting Liu

    cs.SE · cs.AI

    Skills are increasingly used to package agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, skills often need to express more than task guidance: they must make goals, input boundaries, permissions, evidence requirements, output contracts, quality criteria, verification steps, human approval points, and handoff rules inspectable. This paper proposes contractual skills, a GovernSpec-inspired design...

    arxiv.org/abs/2605.22634 · PDF

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