The dataweave language can have

[MasudIbne756]

The dataweave codegen project by the mulesoft ml/ai team is a generative ai tool aimed at simplifying the use of mulesoft’s powerful data transformation programming language, dataweave, and making it more accessible for low-code/no-code users.

a steep learning curve, but dataweave codegen simplifies the process by letting users generate dataweave scripts by simply providing a sample input and output data corresponding to the desired dataweave code. By streamlining the data transformation process, the dataweave codegen project enables more users to build mule applications while accelerating development and time-to-value.

We will describe our exploration of techniques and models that are used to generate dataweave code given the sample input and output. These results also serve as an analysis of the generalizability of current state-of-the-art models and techniques to a low-resource programming language like dataweave and the ability of large language america phone number list models (llms) to learn about and adapt to a new language with limited training data.

Broadly, we explore two types of approaches:

using private large language models like gpt 3.5, gpt 4(from openai), claude 1.3 and claude v 2(from anthropic) out-of-the-box via their apis
creating training data and using it to fine-tune pretrained open-source 7b parameter llms
we will also describe the evaluation methodology to evaluate the models and the approaches we tried.