B. Adams

Suzhen Zhong, Shayan Noei, Bram Adams et al.

Code review helps maintain software quality before code integration, but it also imposes a substantial workload on human reviewers. As generative artificial intelligence becomes part of software development, code review is shifting from a primarily human review process toward AI-supported review processes in which large language model (LLM) reviewers and AI agent reviewers participate alongside human reviewers. However, we still lack empirical evidence on how this transition affects review efficiency and review quality. In this paper, we study 1.02 million reviewed pull requests from 207 GitHub projects that transition across three code review eras: human-centric review, LLM-assisted review, and agentic code review. We identify three AI reviewer adoption practices: Gradual AI Adoption, Rapid LLM Adoption, and Rapid AI Agent Adoption. We further model pull request review discussions as reviewer interaction sequences to characterize how human, LLM, and AI agent reviewers collaborate during the review process. Our results show that agent-involved collaboration patterns, especially reviews initiated by AI agents or involving multiple AI agents, are associated with faster review decisions under Gradual AI Adoption and Rapid AI Agent Adoption. However, these efficiency gains do not translate into better review quality. We also find that review activity and pull request type remain important across eras, while human-AI collaboration patterns become the strongest explanatory factor for review efficiency once LLM and AI agent reviewers participate. These findings provide empirical guidance for designing AI-supported code review processes that improve efficiency without weakening review quality.

Adekunle Ajibode, Oussama Ben Sghaier, Keheliya Gallaba et al.

Pre-trained language models (PTLMs) hosted on platforms such as Hugging Face form complex lineage structures similar to software dependency graphs. However, unlike traditional software ecosystems, PTLM repositories often lack reliable provenance due to missing metadata, such as licenses, reuse methods, pipeline tags, model types, and training libraries. To address this gap, we introduce Semantic Fingerprinting (SemFin), a lightweight approach that combines Hugging Face (HF) configuration files with model repository tags to automatically impute missing model metadata fields and reconstruct model lineage chains. We evaluate SemFin on a large-scale dataset of 317,133 PTLMs. Our results show that configuration files typically encode the technical requirements necessary to instantiate and reuse models, enabling them to serve as a structural blueprint for model reuse, particularly for transformer-based architectures. By combining these configuration files with model repository tags, SemFin significantly outperforms the existing propagation-based imputation approaches, improving prediction accuracy by up to 31.4% and 26.6% compared to Graph Avg and Hub Avg baselines. Importantly, SemFin also imputes metadata for 16.6% of isolated models where propagation-based methods fail. Applying SemFin to impute missing reuse-method and license metadata for 167,089 unlabeled models reveals that traceable reuse method chains expand by 75.9% and license lineage chains by 53.6%, uncovering 86 previously invisible reuse method patterns, while the proportion of incompatible license patterns only increases from 34.8% to 36.8%. These findings demonstrate how automatically derived structural signals can support the automated construction of AI Bills of Materials (AIBOMs), helping transform metadata from an error-prone manual declaration into information inferred directly from model artifacts.