beatsprom/ai-code-generation-swe-agents-2026
š» 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 (2023ā2026). Built with Universal Scientific Engine V17.1 Gold, providing 47ā¦ See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/ai-code-generation-swe-agents-2026.
0143
1# Quickstart: Semantic Search on AI Code Generation & SWE Agents Dataset (Universal V17.0 Platinum)
2import pyarrow.parquet as pq
3import numpy as np
4
5# 1. Load Parquet Dataset
6table = pq.read_table("AI_CODE_GENERATION_SWE_AGENTS_PROGRAM_SYNTHESIS_2026_FULL.parquet")
7df = table.to_pandas()
8
9print(f"Loaded {len(df)} AI Code Generation & SWE Agent research papers.")
10print(f"Top Paper: {df['title'].iloc[0]} (Citations: {df['academic_citations_count'].iloc[0]} | Stars: {df['github_stars'].iloc[0]})")
11print(f"Execution Mode: {df['code_agent_execution_mode'].iloc[0]}")
12print(f"Backbone: {df['coding_foundation_backbone'].iloc[0]}")
13print(f"Cluster: {df['cluster_topic_name'].iloc[0]}")
14
15# 2. Example Semantic Vector Search
16query_vector = np.random.randn(384).astype(np.float32)
17query_vector /= np.linalg.norm(query_vector)
18
19abstract_vectors = np.vstack(df['abstract_vector_384d'].values)
20similarities = np.dot(abstract_vectors, query_vector)
21top_5_idx = np.argsort(similarities)[::-1][:5]
22
23print("\n--- TOP 5 AI CODING AGENT VECTOR SEARCH RESULTS ---")
24for idx in top_5_idx:
25 print(f"Score: {similarities[idx]:.4f} | {df['title'].iloc[idx]} (Mode: {df['code_agent_execution_mode'].iloc[idx]} | Cluster: {df['cluster_topic_name'].iloc[idx]})")
26