cs.SE · 2026-07-09 · No. 48

Software Engineering, 2026-07-09.

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

    Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production

    Arun Malik

    cs.SE · cs.AI · cs.DC · cs.ET · cs.MA

    AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously solved problems. This paper introduces progressive crystallization, a lifecycle that treats agent exploration as a discovery mechanism rather than a permanent execution model. It defines a three-stage execution taxonomy, from fully agent-orchestrated to hybrid to fully deterministic workflows,...

    arxiv.org/abs/2607.07052 · PDF

  2. 02

    Deployment Risk Assessment Using Diff-Aware Features: A Case Study at Prime Video

    Mayur Kurup, Hyunjae Suh, Swathi Vaidyanathan, Pranesh Vyas, Srinidhi Madabhushi, Yegor Silyutin

    cs.SE · cs.LG

    At Amazon Prime Video, we face the critical operational challenge of managing code deployments during live events and rapid feature releases without causing service outages. Current change control approaches use blanket deployment freezes that block all changes regardless of risk, creating significant developer toil. While prior research has explored risky change predictors, these rely on developer-specific metadata or extensive historical...

    arxiv.org/abs/2607.06766 · PDF

  3. 03

    SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models

    Yizhi Wang, Xinghua Gao, Reachsak Ly, Alireza Shojaei

    cs.SE · cs.AI

    Smart home automation platforms increasingly rely on user-authored YAML configuration files to define device behaviors, but these files are prone to syntax, formatting, and semantic logic errors that can cause automation failures and safety risks. Existing YAML validators, static analysis tools, and general-purpose large language models offer limited support for end-to-end diagnosis and repair because they lack domain-specific understanding...

    arxiv.org/abs/2607.06748 · PDF

  4. 04

    Reliable and Developer-Aligned Evaluation of Agents for Software Engineering

    Razvan Mihai Popescu

    cs.SE · cs.AI

    Large language models are rapidly moving towards closing the development cycle, transitioning from simple assistive companions to autonomous contributors deeply embedded into collaborative development environments. Despite their accelerated adoption, existing evaluation techniques are limited due to their fragmented nature and distorted projection of true model capabilities, often obtained from hypothetical syntactic scenarios. This research...

    arxiv.org/abs/2607.06713 · PDF

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