Code review is where AI-authored pull requests (AIPRs) must earn reviewer trust before integration, yet it is unclear whether reviewers apply the same criteria used for human-authored pull requests (HPRs). We surveyed 239 practitioners from 31 countries using matched HPR and AIPR questions to compare trust, evidence re...
Md Shamimur Rahman, Khairul Alam, Banani Roy et al.· 0 citations
AIPR governance is reframe around making human judgment observable before generated contributions consume scarce reviewer attention, and conceptualize missing rationale, validation, and ownership as reviewability debt, the work reviewers must absorb when generated code lacks sufficient human grounding.
Md Shamimur Rahman, Khairul Alam, Banani Roy et al.· 0 citations
A large-scale empirical study of Galaxy describes Galaxy maintenance as a distributed ecosystem-level process and identifies opportunities to improve diagnostic reporting, lifecycle-aware triage, cross-space traceability, and the reuse of community-support knowledge.
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 offers a scalable method for continuously monitoring developer pain points and improving the usability, reliability, and deployability of FL systems.
