Datasets:

beatsprom
/

ai-code-generation-swe-agents-2026

license: apache-2.0
task_categories:
  - text2text-generation
  - feature-extraction
language:
  - en
tags:
  - code-generation
  - swe-agent
  - swe-bench
  - program-synthesis
  - self-healing-code
  - deepseek-coder
  - ast
  - formal-verification
pretty_name: AI Code Generation & SWE Agents 2026
size_categories:
  - n<1K

💻 AI Code Generation, SWE Agents & Program Synthesis Dataset (2026 Edition)

A structured research dataset featuring 3,181 domain-verified research papers and 771 official code repositories focused on Autonomous Software Engineering Agents (SWE-bench), Program Synthesis, DeepSeek-Coder-V2, Qwen2.5-Coder, Test-Driven Code Repair, Self-Healing Software, AST Semantic Modeling, and Formal Logic Verification (2023–2026).

Built with Universal Scientific Engine V17.1 Gold, providing 47 schema attributes with verified repository attribution, 8 AI topological semantic clusters, pre-calculated Top-3 Semantic Nearest Neighbors Graph, structured benchmark leaderboards, and native 384-dimensional dense PyTorch embeddings.

📊 Dataset Schema Highlights (47 Columns)

Field	Type	Description
`paper_id`	String	Unique ArXiv identifier
`title`	String	Research paper title
`cluster_topic_name`	String	1 of 8 AI Topological Semantic Clusters
`code_agent_execution_mode`	String	Execution mode (SWE-bench Agent, Pair Programmer, Self-Repair)
`coding_foundation_backbone`	String	Model backbone (DeepSeek-Coder-V2, Qwen2.5-Coder, Claude-3.5)
`programming_languages_supported`	List[String]	Supported languages (Python, Rust, C++, TypeScript, Go)
`tested_benchmarks`	List[String]	Benchmarks evaluated (SWE-bench, HumanEval, MBPP, LiveCodeBench)
`benchmark_leaderboard_json`	List[Struct]	Structured pass@1 & resolved scores
`semantic_nearest_neighbors_top3`	List[Struct]	Pre-calculated top-3 related papers with cosine similarity
`commercial_ip_safety_score`	Integer	0–100 commercial compliance index (94% Enterprise Safe)
`tldr_neural_summary`	String	15-word executive summary of key innovation
`title_vector_384d`	List[Float]	384d PyTorch embedding (`all-MiniLM-L6-v2`)
`abstract_vector_384d`	List[Float]	384d dense contextual PyTorch embedding
`reproduction_recipe`	String	1-line bash setup command

🧩 8 AI Semantic Clusters Breakdown

Test-Driven Program Repair & Self-Healing Code Synthesis (599 papers)
Code Foundation Models & Specialized Instruction Distillation (501 papers)
Formal Verification, Theorem Proving & Symbolic Logic (452 papers)
Syntax-Guided AST Modeling & Semantic Code Search (421 papers)
Interactive Coding Assistants & Human-AI Pair Programming (356 papers)
Static Analysis, Vulnerability Detection & Automated Security Patching (336 papers)
Autonomous Repo-Level Software Engineering Agents (SWE-bench) (276 papers)
Multi-Language Code Translation & Cross-Platform Migration (240 papers)

📊 Interactive OpenAngels Visual Dashboard Included

Open DATASET_ANALYTICS_DASHBOARD_100_SAMPLE.html directly in your browser (Chrome/Edge/Safari) to explore the interactive visual intelligence directory with real-time filtering, search, and paper metrics.

💻 1-Click Python Quickstart

import pyarrow.parquet as pq

# Load 100-Sample Teaser
table = pq.read_table("AI_CODE_GENERATION_SWE_AGENTS_PROGRAM_SYNTHESIS_2026_100_SAMPLE.parquet")
df = table.to_pandas()

print(f"Loaded {len(df)} sample AI Coding papers.")
print(f"Top Paper: {df['title'].iloc[0]}")
print(f"Execution Mode: {df['code_agent_execution_mode'].iloc[0]}")
print(f"Top-3 Nearest Neighbors: {df['semantic_nearest_neighbors_top3'].iloc[0]}")

🚀 Get the Full 3,181-Paper Enterprise Edition

The complete commercial production dataset (3,181 papers in Parquet with 384d vectors, SQLite DB, Clean CSV, Interactive OpenAngels HTML Dashboard, and JSON) is available here:

👉 BeatsProm AI Code Generation & SWE Agents Dataset Full Edition