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                <datestamp>2025-10-13T12:10:08Z</datestamp>
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    <dc:creator>Fahim, Md Asif</dc:creator>
    <dc:title>Dataset of Software Self-evolution with SelfEvolve:  an Agentic Architecture for Runtime Code Generation</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>  A collection of 11 curated datasets for evaluating AI agents capable of runtime self-evolution through autonomous code generation, codebase integration, and cross-session        
  composition. Used in ICSE 2026 paper &quot;Self-evolving Systems: a Runtime Architecture for Autonomous Code Generation&quot;.

Dataset Categories:

  Integration Tasks (4 datasets): Multi-file codebases (577-783 LOC, 7-20 classes)
  • Patient Risk Analyzer (Healthcare) - 752 LOC, 12 files, 20 classes
  • Student GPA Calculator (Education) - 783 LOC, 10 files, 10 classes
  • Salary Analyzer (HR Analytics) - 616 LOC, 13 files, 12 classes
  • Inventory Low Stock Alert (E-commerce) - 577 LOC, 8 files, 7 classes

  Compositional Tasks (3 datasets): Cross-session capability building
  • Matrix Eigenvalue, Portfolio Risk, IoT Sensor Pipeline

  Data Processing Tasks (4 datasets): External data manipulation
  • Book Recommender (100 books CSV), Friend Suggester (100 users JSON)
  • Movie API (100 movies CSV), Performance Tracker (50 reviews CSV)

Statistics:65 files, ~80 KB compressed, 49 Python classes, 2,685 LOC, 9 domains (Healthcare, Education, HR Analytics, E-commerce, Finance, Linear Algebra, IoT, Media,       
  Social Networks)

  Contents: Each dataset includes problem.json (task specification), Python source files (PEP 8 compliant with type hints), __init__.py (package exports), and data files       
  (CSV/JSON where applicable).

  Evaluation Results: Pass@1: 92.7% (WITH TDD) vs 72.7% (WITHOUT TDD); Avg iterations: 2.2 vs 4.7; Wilcoxon test: W=55, p&lt;0.001, r=0.98

  Dependencies: Python 3.8+, minimal (NumPy for 1 dataset)

  License:CC BY 4.0
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    <dc:subject>Software Engineering</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/pysnkp9n23.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/pysnkp9n23.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
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    <dc:date>2025-10-13T12:10:08Z</dc:date>
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