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## LLM-based Code and Test Generation: Generating Code by Test Specifications with LASSO's Sequence Sheet Notation (SSN)
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LASSO's Sequence Sheet Notation (SSN) allows for a unique form of code generation based on test specifications. This *test-driven code generation* approach enables you to pinpoint code that produces the expected behavior defined by your test specifications, represented as stimulus sheets in SSN. This is extremely helpful for verifying functional correctness and ensuring code meets established requirements.
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**Learn More & Get Started:**
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*[**Try the Test-Driven Code Generation**](lasso/ssn?recommendation=gen) - Begin generating code based on your SSN test specifications.
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*[**SSN Documentation**](docs/datastructures/ssn) - Understand the structure and capabilities of SSN for defining test specifications.
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A public demo LASSO instance (i.e., playground) is available. To try LASSO, head over to [TDSEHub](./hub), select an example LSL pipeline script and press the `Try Now!` button.
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### Generative AI (LLM) - Code and Test Generation
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For example, run your first code/test generation workflow with OpenAI models [here](lasso/submit?exampleId=OPENAI_GEN).
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## Code Search Services
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The platform offers several code search services, including *interface-driven code search* as well as *test-driven code search*.
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Visit the [Search](search) page for details.
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**Learn More & Get Started:**
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*[**Visit the Code Search Page**](search) for more details
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## Code and Test Generation with Generative AI
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The platform offers several code and test generation services, supported by the platform's sequence sheet notation and LASSO's query language.
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**Learn More & Get Started:**
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*[**Visit the Code Generation Page**](generation) for more details
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*[**TDSEHub**](lasso/submit?exampleId=OPENAI_GEN) - view example code/test generation LSL pipeline workflows with OpenAI models
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## LSLFlow - A Visual Graph Editor for LSL Pipelines
apiKey = "demo" // see https://docs.langchain4j.dev/integrations/language-models/open-ai/
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model = "gpt-4o-mini"
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samples = 1
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// custom DSL command offered by the action (for each stimulus matrix, create one prompt to obtain impls)
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prompt { stimulusMatrix ->
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// can by for any prompts: FA, impls, models etc.
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def prompt = [:] // create prompt model
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prompt.promptContent = """implement a java class with the following interface specification, but do not inherit a java interface: \`\`\`\${stimulusMatrix.lql}\`\`\`. Only output the java class and nothing else."""
apiKey = "demo" // see https://docs.langchain4j.dev/integrations/language-models/open-ai/
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model = "gpt-4o-mini"
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samples = 1
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prompt { stimulusMatrix ->
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def prompt = [:] // create prompt model
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prompt.promptContent = """generate a junit test class to test the functionality of the following interface specification: \`\`\`\${stimulusMatrix.lql}\`\`\`. Assume that the specification is encapsulated in a class that uses the same naming as in the interface specification. Only output the JUnit test class and nothing else."""
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