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

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

This paper characterizes oracle signals in test files of agent-authored pull requests and assesses their impact on merge outcomes.

The paper introduces Code-QA-Bench, a novel framework that rigorously separates genuine code reasoning from mere documentation memorization in repository-level code understanding benchmarks.

This study analyzes over 20,000 real-world coding sessions to show that AI coding agents frequently fail users through subtle misalignment, requiring constant manual correction even when major system…

This paper investigates the relationship between bug report features and repair success for automated program repair agents, finding that concrete, executable, and well-localized information is most b…

DeepSWE is a new benchmark for evaluating coding agents with original tasks written from scratch and hand-graded verifiers.

The paper introduces JAW, a novel framework that demonstrates how adversaries can hijack agentic workflows on automation platforms like GitHub Actions by manipulating inputs based on context-grounded…

This paper measures the integration friction in code repositories caused by autonomous coding agents and finds that about half of the variation in friction is repository-level, and agent-authored cont…

This paper presents a case study on using AI coding agents in software engineering and develops a process model for governing high-velocity agentic implementation.

The study found that while multi-agent LLM code generation architectures significantly affect code complexity, the added complexity does not translate into better functional correctness, suggesting ar…

This paper introduces TRIM, an algorithm that minimizes redundant edits in AI-generated code, called CodeSlop, by minimizing agent trajectories, reducing CodeSlop by 17.9%-32.9% with negligible perfor…

The paper introduces AxDafny, a verifier-guided repair framework that improves verification success in Dafny by generating implementations, invariants, assertions, and termination arguments.

This paper revisits N-version programming with AI coding agents and finds substantial common-mode failures but also practical benefits.

This paper proposes a framework to transform historical real-bug mechanisms into executable feedback targets for LLM-based unit test generation, improving real-bug detection over various baselines.

The paper investigates how AI coding assistants shift developers' security focus from proactive prevention to reactive review, finding that this structural change is reinforced by current tool interac…

The paper introduces Vertical Integration Bias (VIB) to quantify whether LLMs favor their own provider's ecosystem when generating code, finding that this bias is significant in both direct and agenti…