cs.SE · 2026-07-23 · No. 62

Software Engineering, 2026-07-23.

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

    Don't Trust the Label: License Laundering in AI Supply Chains

    James Jewitt, Hao Li, Gopi Krishnan Rajbahadur, Bram Adams, Ahmed E. Hassan

    cs.SE · cs.AI

    AI artifacts move through a multi-platform supply chain, spanning datasets and models on Hugging Face and applications on GitHub. While each artifact carries a license whose obligations should propagate through redistribution, no study has yet measured whether those obligations survive the chain or are stripped and replaced as artifacts move downstream. We trace 232,270 dataset$\rightarrow$model$\rightarrow$application chains and quantify two...

    arxiv.org/abs/2607.20300 · PDF

  2. 02

    Multi-stage Dynamic Selection for Cross-Project Defect Prediction

    Juscimara G. Avelino, Juscelino S. A. Junior, George D. C. Cavalcanti, Rafael M. O. Cruz

    cs.SE · cs.LG

    Cross-Project Defect Prediction (CPDP) involves building models using data from external projects, called training projects, to predict modules from the target project. However, traditional CPDP methods suffer from the distribution shift between training and target projects that affects the model's performance. This paper proposes a novel CPDP framework that addresses this issue by proposing a two-stage multiple classifier system (MCS)...

    arxiv.org/abs/2607.20151 · PDF

  3. 03

    Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes

    Yuhao Tan, Zhibang Yang, Fangkai Yang, Yuan Yao, Yu Kang, Lu Wang, Pu Zhao, Xin Zhang, Xiaoxing Ma, Qingwei Lin,...

    cs.SE · cs.AI

    Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug reports remains underconstrained. Bug reproduction tests (BRTs) help close this gap by turning a bug report into an executable, bug-specific signal that can guide repair and validate candidate patches. Existing work has therefore studied BRT generation as a core subproblem in APR and mainly...

    arxiv.org/abs/2607.19843 · PDF

  4. 04

    PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

    Ryan Deng, Yuanzhe Liu, Bastian Lipka, Yao Ma, Xuhao Chen, Tim Kaler, Jatin Ganhotra

    cs.SE · cs.AI

    Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and feature implementation in real codebases. However, they still struggle with repository-level code optimization, which requires preserving behavior while improving runtime performance. Passing tests is not enough in this setting; a patch must preserve behavior, implement code optimization, and approach...

    arxiv.org/abs/2607.19653 · PDF

  5. 05

    Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub

    Sahand Saed, Khairul Alam, Banani Roy

    cs.SE · cs.AI

    Federated Learning (FL) enables collaborative model training without centralizing raw data, but building and operating FL systems remains difficult due to distributed execution, rapidly evolving frameworks, and privacy and governance requirements. In this paper, we present an empirical study of FL developer challenges by independently analyzing 495 Stack Overflow posts and 9,116 GitHub issues and pull requests from 92 FL-related projects....

    arxiv.org/abs/2607.19621 · PDF

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