Model Card for Model ID

JMeter RoBERTa

Model Details

Model Description

JMeter RoBERTa is a fine-tuned language model based on RoBERTa that helps users find the appropriate JMeter components for their performance testing needs. It translates natural language queries into specific JMeter element recommendations, making JMeter more accessible to users who aren't familiar with its extensive component library.

Model Architecture

Base Model: RoBERTa Fine-tuning Method: Sequence classification Number of Labels: 122 (representing different JMeter elements) Training Epochs: 8

Intended Use

This model is designed to be used as an API that can be called from Java code or other applications to help users identify the correct JMeter components based on their natural language descriptions of what they want to accomplish.

Primary intended uses

Assist users in finding appropriate JMeter components Integrate with JMeter plugins or extensions to provide intelligent component suggestions Support documentation and learning tools for JMeter

Primary intended users

JMeter users (beginners to experts) Performance testing engineers QA teams working with JMeter Developers of JMeter extensions or plugins

Training Data

The model was trained on a dataset of natural language queries paired with corresponding JMeter element information. The dataset includes:

Various phrasings of common JMeter tasks
Multiple variations of each query to improve robustness
Mappings to JMeter element details including class name, GUI class, version, and deprecation status

Training procedure

The model was fine-tuned using the Transformers library with the following parameters:

Learning rate: 5e-5 Batch size: 16 Training epochs: 8 Max sequence length: 128 Optimizer: AdamW

Performance and Limitations

Performance

The model achieves high accuracy in identifying the correct JMeter element for common queries. It performs particularly well for:

HTTP samplers
JDBC connections
Timers
Assertions
Controllers
Limitations

The model may struggle with very specialized or complex JMeter components that were underrepresented in the training data It works best with concise, clear queries rather than lengthy, ambiguous descriptions

The model is specific to JMeter version 5.4.3; newer components or deprecated components in future versions may not be correctly identified

Ethical Considerations

This model is designed for a specific technical domain (JMeter performance testing) and has minimal ethical concerns. It does not process personal data or make decisions that impact individuals.