ibm-granite-community/granite-code-cookbook
Oct 2024 – Dec 2024
2 Months active
Languages Used
YAMLJupyter NotebookPython
Technical Skills
CI/CDShell ScriptingCode GenerationJupyter NotebooksLangchainLarge Language Models (LLMs)
Dean Wampler
PROFILE
Dean Wampler
Dean Wampler developed automation and reliability enhancements for the ibm-granite-community/granite-code-cookbook repository over a two-month period. He created an end-to-end workflow that uses Hypothesis-based property tests and large language models to generate Python code for a Rational class, integrating Jupyter notebooks and clear setup instructions to streamline test-driven development. Dean also improved CI/CD pipelines by filtering out commented entries in YAML workflow configurations using shell scripting, which reduced runtime errors and manual intervention. His work demonstrated depth in code generation, workflow automation, and property-based testing, resulting in more robust, maintainable, and efficient notebook processing and code delivery.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
568
Activity Months2
Your Network
7 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered an automated Python code-generation workflow from unit tests for the Granite code cookbook. Introduced a new recipe and Jupyter notebook that defines Hypothesis tests for a Rational class and uses an LLM to generate the implementation, along with setup instructions, code-generation steps, and guidance to execute the generated code against the tests. This work establishes an end-to-end, test-driven automation workflow and lays the foundation for broader automation across projects.
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered an automated Python code-generation workflow from unit tests for the Granite code cookbook. Introduced a new recipe and Jupyter notebook that defines Hypothesis tests for a Rational class and uses an LLM to generate the implementation, along with setup instructions, code-generation steps, and guidance to execute the generated code against the tests. This work establishes an end-to-end, test-driven automation workflow and lays the foundation for broader automation across projects.
December 2024
October 2024
1 Commits
Oct 1, 20242024-10 monthly summary focusing on reliability and automation improvements in granite-code-cookbook workflows. The primary effort this month was a targeted bug fix to ensure notebook processing only uses active entries, thereby reducing runtime errors and manual cleanups in automated pipelines.
October 2024
1 Commits
Oct 1, 20242024-10 monthly summary focusing on reliability and automation improvements in granite-code-cookbook workflows. The primary effort this month was a targeted bug fix to ensure notebook processing only uses active entries, thereby reducing runtime errors and manual cleanups in automated pipelines.
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance75.0%
AI Usage50.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonYAML
Technical Skills
CI/CDCode GenerationJupyter NotebooksLangchainLarge Language Models (LLMs)Property-Based TestingPythonShell ScriptingTest-Driven Development (TDD)
Repositories Contributed To
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