arXiv AI
It Is Not My Code Anymore
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arXiv AI
Jun 17
Software Delegation Contracts: Measuring Reviewability in AI Coding-Agent Work
arXiv:2606. 17099v1 Announce Type: cross Abstract: AI coding agents increasingly accept assigned software tasks, modify repositories under bounded authority, and return work packages for review.
arXiv AI
2d ago
Ownership in AI-Assisted Everyday Tasks
The study investigates when work done with AI feels like one's own, using a qualitative survey where participants described tasks that felt owned versus not owned. Findings show that ownership depends on the collaboration process: people feel ownership when they lead, iterate, or rewrite, but disown work when merely approving AI suggestions. Ownership also extends to tasks where people set the vision but rely on AI for execution, yet loss of personal voice and lack of comprehension erode ownership, and willingness to disclose AI use is driven more by community norms than by pride.
arXiv AI
Jun 6
Human Oversight and Overload: Two Hidden and Costly Burdens of AI-Assisted Software Engineering
arXiv:2606. 05770v1 Announce Type: cross Abstract: AI is changing how software engineers work, but it often comes with hidden burdens and costs.
arXiv AI
Jul 2
Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering
arXiv:2607. 01087v1 Announce Type: cross Abstract: Generative AI is shifting software engineering from a practice organized around scarce implementation effort toward one organized around abundant, low-cost code production.
arXiv AI
Sep 11
Builder, Defender, Breaker: Measurable Independence and Bounded Autonomy When Generative Models Build, Defend and Test Software
The article discusses how generative models increasingly act as builders, defenders, and breakers of software, challenging the assumption that full autonomy is the ultimate goal. It introduces a framework that defines measurable independence between lifecycle roles based on shared generative substrates, and proposes five autonomy levels, three human roles, and five decision criteria to guide oversight. The authors argue that human authority should focus on specification, accountability, and emergency intervention, and they outline testable hypotheses and protocols to evaluate independence and oversight effectiveness.
arXiv AI
3d ago
A Study of the Reliability of Agentic AI-Generated Programs
The paper investigates the reliability of software produced by agentic AI by comparing AI-generated versions of ten well-known Linux utilities to their human-written counterparts. Using fuzz testing (both black-box and coverage-guided AFL++), the authors find that AI-generated code is often as reliable or more reliable than the latest human versions, with fewer memory errors but a higher incidence of hangs. The study emphasizes that robust AI-generated software requires careful prompting, skilled human oversight, and that the AI workflow can serve as a cost-effective specification for sustainable code.