cs.SE · 2026-05-22 · No. 7

Software Engineering, 2026-05-22.

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

    Contractual Skills: A GovernSpec Design Framework for Enterprise AI Agents

    Ting Liu

    cs.SE · cs.AI

    Skills are increasingly used to package agent instructions, workflows, scripts, and reference materials. In enterprise settings, however, skills often need to express more than task guidance: they must make goals, input boundaries, permissions, evidence requirements, output contracts, quality criteria, verification steps, human approval points, and handoff rules inspectable. This paper proposes contractual skills, a GovernSpec-inspired design...

    arxiv.org/abs/2605.22634 · PDF

  2. 02

    SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering?

    Yuxuan Sun, Yuze Zhao, Yufeng Wang, Yao Du, Zhiyuan Ma, Jinbo Wang, Mengdi Zhang, Kai Zhang, Zhenya Huang

    cs.SE · cs.AI

    Evaluating software engineering capabilities has become a core component of modern large language models (LLMs); however, the key bottleneck hindering further scaling lies not in the scarcity of high-quality solutions, but in the lack of high-quality test suites. Test suites are indispensable both for synthesizing program repair trajectories and for providing precise feedback signals in reinforcement learning. Unfortunately, due to the high...

    arxiv.org/abs/2605.22175 · PDF

  3. 03

    From Patches to Trajectories: Privileged Process Supervision for Software-Engineering Agents

    Murong Ma, Tianyu Chen, Yun Lin, Shuai Lu, Qinglin Zhu, Yeyun Gong, Zhiyong Huang, Peng Cheng, Yan Lu, Jin Song Dong

    cs.SE · cs.AI

    Supervised fine-tuning (SFT) on long teacher trajectories is the dominant way to instill investigation and reasoning in open software-engineering (SWE) agents. Since every retained response becomes an imitation target, the student inherits the final outcome and intermediate flaws, including ungrounded leaps and redundant loops. High-quality training data must be effective(each step is grounded and narrows the agent's epistemic gap to the...

    arxiv.org/abs/2605.21996 · PDF

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