Code Search
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Benchmarks
CodeSearchNet
CoDesc
CodeXGLUE - AdvTest
CoIR
CodeSearchNet - Ruby
CodeXGLUE - WebQueryTest
Most implemented
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation
AutoCodeRover: Autonomous Program Improvement
MELT: Mining Effective Lightweight Transformations from Pull Requests
Papers
MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities
Code search in large-scale ecosystems is often hindered by the lexical gap between user queries and implementation details, alongside the trade-off between the low latency of traditional Information Retrieval (IR) and th…
Information RetrievalCode SearchHarness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable
The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execution. As models, APIs, environments, and …
Code SearchScientific Code Search at Scale: A Multi-Domain Dataset and Benchmark
Scientists increasingly rely on open-source tools to support their research workflows, yet discovering relevant software among over 600 million GitHub repositories remains challenging. Existing code search benchmarks foc…
Information RetrievalCode SearchDecoupling Code Complexity from Newcomer Participation: A Causal Study of AI Coding Agent Adoption in OSS
Open-source projects depend on a steady inflow of newcomers. A growing concern is that AI coding agents (tools such as Cursor and Claude Code that write code from natural-language instructions) will crowd them out, by ab…
Code SearchUniCoder: Unified Visual-to-Code Generation via Symbolic Rewards and Reference-Guided Code Optimization
Visual-to-Code generation, which transforms scientific plots, vector graphics, and webpages into executable scripts, demands a level of pixel-precise alignment that standard Multimodal Large Language Models (MLLMs) fail …
Reinforcement LearningCode GenerationCode SearchRecall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval
Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, efficiency, and scalability of contemporary deep learning models for first-…
Code Search