cs.SE · 2026-08-18 · No. 88

Software Engineering, 2026-08-18.

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

    TDD-Agent: Test-Driven Reasoning for Code Generation

    Hongyue Yu, Kefan Li, Jiakun Li, Hongzheng Chai, Yuan Yuan, Rui He, Junyi Wei

    cs.SE · cs.AI

    Large Language Models (LLMs) have achieved remarkable progress in code generation, yet ensuring correctness in complex, repository-level tasks remains challenging. Existing approaches often use generated tests as static post-hoc validators, which limits their ability to guide implementation and may introduce misleading feedback when the tests themselves are incomplete or incorrect. In this paper, we introduce TDD-Agent, which operationalizes...

    arxiv.org/abs/2608.16742 · PDF

  2. 02

    The Working Set of a Coding Agent: Coherence Debt in Repository-Scale Tasks

    Bardia Mohammadi, Lars Klein, Aman Chadha, Akhil Arora, Laurent Bindschaedler

    cs.SE · cs.LG

    Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context window. We model this as reconstructing a coupled-fact graph: at each edit, a required fact comes from recent context or parametric memory, and the facts covered by neither form coherence debt. We supply and withhold each channel and inject faults across seven models and five harnesses. As expected, no model...

    arxiv.org/abs/2608.16630 · PDF

  3. 03

    Towards Risk-free AI Agent Deployment

    Yintong Huo, Rangeet Pan, Abhik Roychoudhury

    cs.SE · cs.AI

    LLM-based agents are rapidly moving from research prototypes into the core business processes of organizations, but these agents pose deployment risks to security, compliance, and functionality. In this article, we argue that risk-free deployment must be grounded in the agent's trajectory: the recorded sequence of reasoning steps, tool invocations, and environmental observations. Trajectories are available for any agent, and many failures are...

    arxiv.org/abs/2608.16411 · PDF

  4. 04

    Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026

    Marina Lepp, Joosep Kaimre

    cs.SE · cs.AI · cs.PF

    Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and explain source code. However, their performance on authentic object-oriented programming (OOP) assessments remains insufficiently understood. This study evaluates five widely used GenAI systems, ChatGPT-5.2, DeepSeek-V3, Gemini 2.5 Flash, Claude Sonnet 4.5, and M365 Copilot, using programming...

    arxiv.org/abs/2608.16318 · PDF

  5. 05

    Software Engineering for AI-driven Building Operation

    Philipp Zech, Sascha Hammes, Johannes Weninger, Jürgen Pannosch, Gernot Steidl

    cs.SE · cs.AI

    Building operations are energy-inefficient. Artificial Intelligence (AI)-driven control systems promise benefits through optimization and predictive control, but deploying them in real buildings reveals a significant software engineering (SE) challenge. SE for AI practices assume digital environments where failures mean poor user experience. Buildings are different. A bad control decision wastes energy irreversibly, violates occupant comfort,...

    arxiv.org/abs/2608.16237 · PDF

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