cs.SE · 2026-07-27 · No. 66

Software Engineering, 2026-07-27.

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

    MineValiCoder: Reliable Code Generation with Test Case Quality Mining and Bipartite Graph-Based Mutual Validation

    Zhen Zhao, Qihang Yang, Feifei Dai, Xiangfang Li, Bo Li

    cs.SE · cs.AI

    Large Language Model (LLM)-based Test-Driven Development (TDD) has advanced automated code generation. However, existing approaches depend heavily on human-crafted test cases and cannot operate effectively when only natural-language requirements are available. Although recent work enables automatic test generation, it often overlooks the inherent stochasticity of LLMs, leading to two key defects: faulty tests generate misleading feedback that...

    arxiv.org/abs/2607.22471 · PDF

  2. 02

    How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests

    Iren Mazloomzadeh, Mohammad Mehdi Morovati, Foutse Khomh

    cs.SE · cs.LG

    Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not...

    arxiv.org/abs/2607.21832 · PDF

  3. 03

    From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs

    Kaiwen Zhang, Guanjun Liu

    cs.SE · cs.AI

    Concurrent stateful library APIs expose behavior through evolving resource ownership, lifecycle states, and competing interleavings. Large language models can synthesize executable Rust tests, but their outputs often violate API preconditions, remain shallow, or reduce concurrency to accidental sequential traces. Conversely, model-based and systematic testing techniques provide semantic control but commonly require substantial handwritten...

    arxiv.org/abs/2607.21530 · PDF

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