cs.SE · 2026-07-07 · No. 46

Software Engineering, 2026-07-07.

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

01 — The papers

5 entries
  1. 01

    Three-Phase Evaluation of AI-Assisted Software Development Life Cycle

    Joshua Strubel, Professor Carrie Russell, Carson Crockett, Jason Ferraro, Nathan Londhe, Uzayr Syed, Jacob Viehe

    cs.SE · cs.AI

    This paper presents an exploratory evaluation of how increasing levels of AI autonomy affect software development productivity, requirement adherence, and developer cognitive workload. A team of four developers reimplemented the same full-stack web application across three sequential phases: partial AI-assisted development using GitHub Copilot, an AI-exclusive workflow using GitHub Copilot, and an AI-exclusive workflow using AWS Kiro....

    arxiv.org/abs/2607.05125 · PDF

  2. 02

    LLM-Based Test Oracles: Source-of-Authority Taxonomy -- A Systematic Literature Review

    Ali Hassaan Mughal, Muhammad Bilal

    cs.SE · cs.AI

    Large language models (LLMs) are increasingly used to produce test oracles, the part of a test that decides whether observed behavior is correct. Yet a clear account of where these oracles draw their authority is missing. Prior secondary studies organize the area by oracle form or by LLM technique. None organizes it by the source of the verdict's authority, the property that governs how far a verdict can be trusted. This article presents a...

    arxiv.org/abs/2607.05031 · PDF

  3. 03

    An Exploration of Agentic Information Fusion for Test Maintenance Prediction

    Jingxiong Liu, Nasser Mohammadiha, Gregory Gay

    cs.SE · cs.AI · cs.MA

    Test maintenance is a critical, yet costly, activity - particularly as codebases rapidly evolve. To assist, we present MAST, a multi-agent framework that predicts which test cases require maintenance following changes to the production code. This identification task is necessary as a precondition to any subsequent maintenance activities, but remains challenging due to the complex relationships between production and test code. MAST advances...

    arxiv.org/abs/2607.04786 · PDF

  4. 04

    Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards

    Tianhao Niu, Ziyu Han, Qiguang Chen, Shiqi Zhou, Baocai Shan, Hengjie Fang, Qingfu Zhu, Wanxiang Che

    cs.SE · cs.AI · cs.CV

    Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactive dashboards commonly used for real-world data exploration. We introduce Dashboard2Code, a novel task that requires a model to proactively explore an interactive dashboard, acquire and integrate feedback from its own interactions (e.g., clicking and filtering), and...

    arxiv.org/abs/2607.04727 · PDF

  5. 05

    Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning

    Jiaxing Qi, Zhongzhi Luan, Hongyu Zhang, Shaohan Huang, Carol Fung, Yongxin Tong, Hailong Yang, Depei Qian

    cs.SE · cs.DC

    Large language models (LLMs) are increasingly used to interpret operational evidence and assist incident response in cloud-native microservice systems. However, recovery-oriented use cases require more than identifying a root cause. After observing symptoms and diagnosing a fault, an operator or agent must translate the diagnosis into a concrete recovery action, apply it to an admissible target, and verify that service health has been...

    arxiv.org/abs/2607.04623 · PDF

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