cs.SE · 2026-07-29 · No. 68

Software Engineering, 2026-07-29.

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

    How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair

    Ramtin Ehsani, Irene Manotas, Saurabh Pujar, Luca Buratti, Preetha Chatterjee

    cs.SE · cs.AI

    Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent. Even when provided with the same contextual information, an LLM may generate a correct patch for one bug but fail on another closely related bug. Why this happens remains poorly understood, and it is unclear how LLMs prioritize the diverse information in bug reports and whether model attention affects repair...

    arxiv.org/abs/2607.25873 · PDF

  2. 02

    KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models

    Fuyuan Xia, Qixin Zhang, Chenhao Ying, Haojin Zhu, Shuai Wang, Yuan Luo, Pingchuan Ma, Yuxuan Du

    cs.SE · cs.AI · cs.MA · quant-ph

    As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical. To this end, many LLM-based fuzzing approaches towards quantum libraries have been proposed to uncover potential bugs. However, these methods still suffer from limitations such as insufficient flexibility and low efficiency, which hinder the progress of the quantum computing field. To address these...

    arxiv.org/abs/2607.25647 · PDF

  3. 03

    Specula: Scaling formal specifications for autonomous model checking of system code

    Qian Cheng, Saad Mohammad Rafid Pial, Ruize Tang, Yiming Su, Emilie Ma, Finn Hackett, Ivan Beschastnikh, Yu Huang, Tianyin Xu

    cs.SE · cs.AI · cs.DC · cs.OS

    Specula is a push-button agentic system that generates high-quality formal specifications for large, complex system code and uses the specifications for highly effective model checking and bug finding. Specula employs large language model (LLM) based coding agents to autonomously develop TLA+ specifications, including invariants that describe correctness properties of the target system and formal models that describe the system implementation...

    arxiv.org/abs/2607.25333 · PDF

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