Symflower for LLMs

Symflower supports LLM development, benchmarking, LLM-based code generation workflows, and Retrieval-Augmented Generation (RAG):

Prompt engineering: Augment prompts with data from Symflower's analytics tools to provide the LLM with more context about the code.
Improving efficiency: Code repair makes LLM-generated code more useful by removing compile errors.
Training data curation: Using Symflower helps filter out projects with problematic code to provide higher-quality training data to the LLM.
LLM training, fine-tuning, RAG: Providing Symflower-generated tests or statically analyzed code to the LLM in a RAG scenario, during LLM training, or fine-tuning helps improve accuracy. Fine-tuning with Symflower showed an 8% improvement in a Java<-> Python transpiler.
Benchmarking: The DevQualityEval benchmark covers a variety of metrics to evaluate code quality and help find the most useful LLMs for the evaluated software development tasks.

Symflower's support for LLMs:

DevQualityEval is a standardized LLM evaluation benchmark and framework to compare and improve code generation with LLMs.

Symflower fix is a static analysis tool to repair code generated by LLMs. Use it on local files to post-process the output of LLMs.

Symflower lint performs static analysis on your code to find bugs, performance issues, and deviations from the standard style

Migrate code, packages, dependencies and whole projects automatically.

Symflower run sends a main function to Symflower for symbolic execution and returns coverage as a result.

Symflower symbols queries all functions and methods in a repository and outputs a list of all the symbols.

Symflower test executes the tests in your project and outputs test results and coverage data.

Symflower test-runner runs test impact analysis on the package level to identify and run tests affected by code changes in your query.

Symflower trace provides visual insights into application execution behavior to analyze application performance.