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
Contributed to the hpi-sam/ASE-GenAI repository by building analytics and machine learning infrastructure focused on data-driven insights and reproducible workflows. Developed LLM-assisted SQL queries to analyze social network interactions, integrating both automated and manually refined variants for improved maintainability and performance. Established a data analysis and ML pipeline using Python, pandas, and scikit-learn, implementing data loading, preprocessing, and evaluation metrics to support model training. Added governance and reflection features to enhance data quality oversight and classifier maintenance. Designed a Jupyter Notebook-based framework for evaluating bug explanation quality with NLP metrics, supporting scalable, LLM-ready summarization and transparent, iterative development.
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
36 peopleSame Organization
@online.de18
Shared Repositories
18
2raymooriMember
TonybodoMember
Christian JacobMember
Christian Adriano, PhD candidateMember
David SchlerethMember
sm1llaMember
HendrikDrosteMember
Henok LachmannMember
ibrahimozkanMember
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
Repositories Contributed To
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