AIDev is a new open dataset of 456k AI-agent pull requests showing agents submit code faster than humans but with lower acceptance rates and simpler changes.
arXiv preprint arXiv:2506.14683 , year=
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AgenticSZZ reframes bug-inducing commit identification as temporal knowledge graph search navigated by an LLM agent, reporting F1 scores of 0.47-0.79 and up to 34% improvement over prior SZZ methods on three datasets.
Introduces the first benchmark for Java reproduction test generation from repository issues and adapts a prior Python tool to produce high performance on it.
TestPrune minimizes regression test suites to improve bug reproduction and patch validation in LLM-based agentic repair pipelines, delivering 6-13% relative gains on SWE-Bench benchmarks at low API cost.
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
citing papers explorer
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The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering
AIDev is a new open dataset of 456k AI-agent pull requests showing agents submit code faster than humans but with lower acceptance rates and simpler changes.
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AgenticSZZ: Temporal Knowledge Graph-Guided Agentic Bug-Inducing Commit Identification
AgenticSZZ reframes bug-inducing commit identification as temporal knowledge graph search navigated by an LLM agent, reporting F1 scores of 0.47-0.79 and up to 34% improvement over prior SZZ methods on three datasets.
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Reproduction Test Generation for Java SWE Issues
Introduces the first benchmark for Java reproduction test generation from repository issues and adapts a prior Python tool to produce high performance on it.
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Can Old Tests Do New Tricks for Resolving SWE Issues?
TestPrune minimizes regression test suites to improve bug reproduction and patch validation in LLM-based agentic repair pipelines, delivering 6-13% relative gains on SWE-Bench benchmarks at low API cost.
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A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.