This paper synthesizes practitioner discourse at scale to build a causal model explaining the impact of AI on code review, recovering mechanisms behind observed trends.

This paper presents an empirical study on how developers respond to agentic code reviews using CodeRabbit, revealing mixed reception and opportunities for improvement.

This paper provides the first large-scale characterisation of Domain-Driven Design (DDD) adoption and implementation on GitHub.

This paper presents an empirical investigation on the distribution, evolution, and maintenance implications of Domain-Driven Design (DDD) building blocks in open-source GitHub repositories.

This study provides an ecosystem-scale measurement of commit signing on GitHub, finding that current signing adoption rates are misleading and that developers struggle to maintain consistent, long-ter…

This paper compares the quality and coverage of epic-organized LLM-generated Gherkin acceptance criteria with requirement-aligned generation, using four requirements documents from the PURE dataset.

This paper analyzes 25,264 agentic pull requests from 2,361 GitHub repositories to investigate adoption, productivity, and collaboration patterns of agentic coding tools.

The paper introduces WebKnoGraph, an open-source framework for systematically evaluating internal linking strategies on websites by modeling the site as a graph and assessing trade-offs between author…

The paper evaluates dynamic coordination strategy selection for enterprise multi-agent systems, finding that a calibrated default routing approach is effective, even if a deterministic winner-selectio…

Phoenix is a multi-agent system that uses seven safety controls and a test evaluation strategy to resolve GitHub issues, achieving 75% oracle-resolution with no regressions on a curated benchmark and…

This paper proposes conversational AI review assistants for code review, systems that engage in conversation with developers instead of just generating comments.

The paper presents a large-scale study demonstrating that tool cloning is a pervasive and severe source of hidden duplication in agent-tool ecosystems, necessitating changes in how tool diversity is m…

The paper experimentally evaluates 12 multi-agent LLM collaboration topologies for software design, finding that structural adversarial prompting and cross-model review are the most effective approach…

The paper introduces a new structural merge tool that ensures parsability and universality in comparison to existing tools, resulting in fewer incorrect merge results.

This paper investigates the distribution and evolution of aspect-level sentiments in peer review comments of accepted papers from Nature Communications, revealing a consistent trend of increasing posi…

The paper introduces CITETRACE, a large-scale dataset and evaluation framework that systematically measures structural citation failures in search-augmented LLMs, revealing a pattern called Verified M…