cs.SE · 2026-06-18 · No. 27

Software Engineering, 2026-06-18.

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

01 — The papers

3 entries
  1. 01

    Where Did the Variability Go? From Vibe Coding to Product Lines by Regeneration

    Xhevahire Tërnava

    cs.SE · cs.AI

    In vibe coding, an emerging AI-driven paradigm, an LLM generates an entire program from a natural language prompt, but what happens to the variability that traditional software engineering carefully builds into code? To answer this question, we conducted an exploratory analysis on 10 vibe coded C/C++ projects, which suggests that there is near-zero in-artifact variability, i.e., at compile and runtime. All variability decisions are resolved...

    arxiv.org/abs/2606.19042 · PDF

  2. 02

    CAPRA: Scaling Feedback on Software Architecture Deliverables with a Multi-Agent LLM System

    Marco Becattini, Niccolò Caselli, Matteo Minin, Roberto Verdecchia, Enrico Vicario

    cs.SE · cs.AI

    Automated assessment in software engineering education has advanced significantly for code grading and essay scoring. However, reviewing software architecture deliverables, which requires analyzing structural completeness and requirements traceability, has not yet been fully automated. Applying Large Language Models (LLMs) to this task requires robust architectures to ensure technical feedback is accurate and reliable for students. This paper...

    arxiv.org/abs/2606.18976 · PDF

  3. 03

    SWE-Future: Forecast-Conditioned Data Synthesis for Future-Oriented Software Engineering Agents

    Qiao Zhao, JianYing Qu, Jun Zhang, Yehua Yang, Hanwen Du, Zhongkai Sun

    cs.SE · cs.AI

    Realistic coding-agent benchmarks often replay public GitHub issues and pull requests, making them vulnerable to overlap with model pretraining, fine-tuning, synthetic-data generation, or benchmark-driven model selection. Fully synthetic tasks avoid direct historical replay, but can drift away from real repository needs. We propose SWE-Future, a forecast-conditioned data synthesis method for future-oriented coding tasks. Given a forecast...

    arxiv.org/abs/2606.18733 · PDF

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