hpi-sam/ASE-GenAI
Jan 2025 – Feb 2025
2 Months active
Languages Used
JavaPythonJupyter NotebookMarkdown
Technical Skills
Data AnalysisData PreprocessingLLM IntegrationMachine LearningScikit-learnTensorFlow
Christian Jacob
PROFILE
Christian Jacob
Contributed to the hpi-sam/ASE-GenAI repository by developing machine learning workflows and explainability features over a two-month period. Built reproducible Jupyter Notebook pipelines for data preprocessing, model training, and evaluation using Python, TensorFlow Decision Forests, and Scikit-learn. Integrated large language models to generate and consolidate failure explanations, introducing evaluation metrics such as ROUGE and BLEURT to assess explanation quality. Enhanced the bug explanation analysis pipeline by implementing ML-driven correctness prediction and refactoring LLM-based components for improved traceability and scalability. Focused on streamlining experimentation, supporting data-driven decisions, and establishing a robust foundation for future explainability and analysis work.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
2
Lines of code
8,111
Activity Months2
Your Network
18 peopleShared Repositories
18
2raymooriMember
TonybodoMember
Christian Adriano, PhD candidateMember
David SchlerethMember
sm1llaMember
HendrikDrosteMember
hendrix15Member
Henok LachmannMember
ibrahimozkanMember
Work History
February 2025
2 Commits • 1 Features
Feb 1, 2025In February 2025, focused on enhancing the ML-driven bug explanation analysis and generation pipeline for ASE-GenAI. Implemented an ML model to analyze bug report explanations and predict their correctness using TensorFlow Decision Forests, with evaluation on holdout data. Refactored LLM-driven consolidation of failure explanations, improved prompts, progress tracking, and introduced evaluation metrics (ROUGE, BLEURT). Updated processing and storage of explanations, with notes on LLM performance to guide future iterations. Delivered two core commits toward a robust bug-explanation platform.
2 Commits • 1 Features
Feb 1, 2025In February 2025, focused on enhancing the ML-driven bug explanation analysis and generation pipeline for ASE-GenAI. Implemented an ML model to analyze bug report explanations and predict their correctness using TensorFlow Decision Forests, with evaluation on holdout data. Refactored LLM-driven consolidation of failure explanations, improved prompts, progress tracking, and introduced evaluation metrics (ROUGE, BLEURT). Updated processing and storage of explanations, with notes on LLM performance to guide future iterations. Delivered two core commits toward a robust bug-explanation platform.
February 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 highlights focused on feature delivery and experiment infrastructure for Assignment 3 in the hpi-sam/ASE-GenAI repository. The work delivers a reproducible notebook workflow, model evaluation, and explainability components to accelerate data-driven decisions and future experimentation. No major defects were reported this month; minor scaffolding improvements enhanced reliability.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 highlights focused on feature delivery and experiment infrastructure for Assignment 3 in the hpi-sam/ASE-GenAI repository. The work delivers a reproducible notebook workflow, model evaluation, and explainability components to accelerate data-driven decisions and future experimentation. No major defects were reported this month; minor scaffolding improvements enhanced reliability.
Activity
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Quality Metrics
Correctness83.4%
Maintainability73.4%
Architecture80.0%
Performance63.4%
AI Usage76.6%
Skills & Technologies
Programming Languages
JavaJupyter NotebookMarkdownPython
Technical Skills
Code RefactoringData AnalysisData PreprocessingData VisualizationLLM IntegrationMachine LearningNatural Language ProcessingPandasPython ProgrammingScikit-learnTensorFlow
Repositories Contributed To
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