hpi-sam/ASE-GenAI
Nov 2024 – Feb 2025
4 Months active
Languages Used
MarkdownSQLPythonCSVJupyter Notebook
Technical Skills
Data AnalysisDatabase QueryingLLM-assisted DevelopmentLLM-assisted developmentSQLSQL query optimization
hendrix15
PROFILE
Hendrix15
Hendrik Woelert developed core analytics and machine learning infrastructure for the hpi-sam/ASE-GenAI repository, focusing on data-driven insights and reproducible workflows. He implemented LLM-assisted SQL analytics for social network data, combining automated and manual query generation to analyze relationships and message interactions. Using Python, Pandas, and Scikit-learn, he established pipelines for data loading, preprocessing, and model evaluation, including metrics for correctness and readability. Hendrik also created a Jupyter-based framework to assess bug explanation quality with NLP metrics and visualizations, and introduced governance documentation to support classifier maintenance. His work demonstrated depth in data preparation, technical writing, and workflow reproducibility.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
5
Lines of code
7,542
Activity Months4
Your Network
33 people
Work History
February 2025
6 Commits • 1 Features
Feb 1, 2025February 2025 — hpi-sam/ASE-GenAI: Delivered a Bug Explanation Quality Evaluation Framework in Jupyter Notebooks, establishing a notebook-based pipeline to analyze bug explanations using NLP metrics (BLEU, cosine similarity, readability indices) and preparing for LLM-based summarization. Implemented initial notebook scaffolding (task2.ipynb, task3.ipynb), integrated NLP libraries (NLTK, sentence-transformers, textstat, OpenAI), implemented metric calculations, visualizations, and documentation refinements. Progress on Task 2 includes completed work and improved plots; minor fixes included typos corrections and explanation refinements. This work creates a repeatable quality-assessment workflow for bug explanations, enabling data-driven improvements and scalable LLM-ready summarization. Overall impact: Establishes a foundation for automated bug-explanation quality assessment, accelerating feedback loops, improving bug-fix communications, and supporting future ML-driven summarization and reporting.
6 Commits • 1 Features
Feb 1, 2025February 2025 — hpi-sam/ASE-GenAI: Delivered a Bug Explanation Quality Evaluation Framework in Jupyter Notebooks, establishing a notebook-based pipeline to analyze bug explanations using NLP metrics (BLEU, cosine similarity, readability indices) and preparing for LLM-based summarization. Implemented initial notebook scaffolding (task2.ipynb, task3.ipynb), integrated NLP libraries (NLTK, sentence-transformers, textstat, OpenAI), implemented metric calculations, visualizations, and documentation refinements. Progress on Task 2 includes completed work and improved plots; minor fixes included typos corrections and explanation refinements. This work creates a repeatable quality-assessment workflow for bug explanations, enabling data-driven improvements and scalable LLM-ready summarization. Overall impact: Establishes a foundation for automated bug-explanation quality assessment, accelerating feedback loops, improving bug-fix communications, and supporting future ML-driven summarization and reporting.
February 2025
January 2025
2 Commits • 2 Features
Jan 1, 2025Month: 2025-01 — Key outcomes in ASE-GenAI: governance/reflection addition and data preparation groundwork for model training. This enhances data quality oversight, classifier maintenance planning, and training readiness for LLM-enabled workflows, with explicit traceability to commits.
January 2025
2 Commits • 2 Features
Jan 1, 2025Month: 2025-01 — Key outcomes in ASE-GenAI: governance/reflection addition and data preparation groundwork for model training. This enhances data quality oversight, classifier maintenance planning, and training readiness for LLM-enabled workflows, with explicit traceability to commits.
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: December 2024 Summary of work focused on delivering a solid data analysis and ML pipeline foundation for the ASE-GenAI repository, with an emphasis on reproducibility, data quality, and preparation for model evaluation. No major bug fixes were reported for this period; the work was concentrated on feature delivery that establishes core infrastructure for subsequent iterations.
1 Commits • 1 Features
Dec 1, 2024Month: December 2024 Summary of work focused on delivering a solid data analysis and ML pipeline foundation for the ASE-GenAI repository, with an emphasis on reproducibility, data quality, and preparation for model evaluation. No major bug fixes were reported for this period; the work was concentrated on feature delivery that establishes core infrastructure for subsequent iterations.
December 2024
November 2024
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11. Key feature delivered: LLM-assisted Social Network Analytics SQL Queries for hpi-sam/ASE-GenAI, including both LLM-generated and manually extended variants to analyze message likes and relationships, plus documentation/reflection to improve readability, maintainability, and performance. No major bugs fixed this period. Overall impact: enabled data-driven insights on social interactions, strengthened analytics capabilities within ASE-GenAI, and contributed to maintainability through reflection-backed documentation. Technologies/skills demonstrated: SQL analytics, LLM-assisted query generation, manual query extension, documentation and reflection practices, and strong commit-level traceability.
November 2024
2 Commits • 1 Features
Nov 1, 2024Month: 2024-11. Key feature delivered: LLM-assisted Social Network Analytics SQL Queries for hpi-sam/ASE-GenAI, including both LLM-generated and manually extended variants to analyze message likes and relationships, plus documentation/reflection to improve readability, maintainability, and performance. No major bugs fixed this period. Overall impact: enabled data-driven insights on social interactions, strengthened analytics capabilities within ASE-GenAI, and contributed to maintainability through reflection-backed documentation. Technologies/skills demonstrated: SQL analytics, LLM-assisted query generation, manual query extension, documentation and reflection practices, and strong commit-level traceability.
Activity
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Quality Metrics
Correctness82.8%
Maintainability81.8%
Architecture78.2%
Performance71.8%
AI Usage50.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookMarkdownPythonSQL
Technical Skills
AI IntegrationCode EvaluationCode RefactoringData AnalysisData PreparationData PreprocessingData ScienceData VisualizationDatabase QueryingJupyter NotebookLLMLLM IntegrationLLM-assisted DevelopmentLLM-assisted developmentMachine Learning
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