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LoadRunner to JMeter Converters

A collection of tools for converting LoadRunner performance test assets into Apache JMeter format. Includes both a correlation rules converter and a full script converter available in four languages.

Converters at a Glance

Script Language What It Converts Output
app.py Python Correlation rules (.cor) BlazeMeter Correlation Recorder JSON
lr2jm.pl Perl Full LR script directory JMeter .jmx test plan
lr2jm.py Python Full LR script directory JMeter .jmx test plan
lr2jm.js JavaScript Full LR script directory JMeter .jmx test plan
lr2jm.ts TypeScript Full LR script directory JMeter .jmx test plan

1. Correlation Rules Converter (app.py)

Converts LoadRunner correlation rule files (.cor) into JSON for JMeter's BlazeMeter Correlation Recorder plugin.

Prerequisites

  • Python 3
  • pip install -r requirements.txt
  • JMeter with BlazeMeter Correlation Recorder plugin

Usage

python app.py -f <your-LoadRunner-Correlation-File>

Example:

python app.py -f LR.cor

The tool prompts for metadata (id, description, version, components, response filters). Press Enter to accept defaults.

Output

Two JSON files are created in a folder named after the first correlation group:

  • {id}-{version}-template.json — correlation rules with regex extractors
  • {id}-repository.json — version metadata for JMeter's plugin manager

Loading Rules in JMeter

  1. Add bzm - Correlation Recorder to your test plan
  2. Click Load Template > Config
  3. Enter a unique Id and the URL where the JSON files are hosted
  4. Install and load the rules from Templates Manager
  5. Fill in Match Number, Group Number, and Correlation Replacement fields as needed

2. Full Script Converter (lr2jm)

Converts an entire LoadRunner script directory into a runnable JMeter .jmx test plan. Available in Perl, Python, JavaScript, and TypeScript — all produce identical output.

What It Converts

LoadRunner Feature JMeter Equivalent
web_url() HTTPSampler (GET)
web_submit_data() HTTPSampler (POST) with form parameters
web_custom_request() HTTPSampler (custom method) with body parameters
web_reg_save_param() RegexExtractor post-processor
Parameter data files (.dat) CSVDataSet (.csv)
{ParamName} references ${VariableName} substitution

JMX Structure Generated

jmeterTestPlan
  └── TestPlan
       └── ThreadGroup (1 thread, 1 loop)
            ├── HTTP Request Defaults (port 80)
            ├── HTTP Cookie Manager
            ├── CSVDataSet (one per parameter table)
            └── HTTPSampler (one per request)
                 └── RegexExtractor (for correlations)

Input: LoadRunner Script Directory

The converter expects a directory containing:

MyScript/
  MyScript.usr        # Script manifest (lists .c action files)
  MyScript.prm        # Parameter definitions (optional)
  Action.c            # Action file(s) with web_* function calls
  users.dat           # Parameter data file(s) (optional)

Usage

Choose whichever language runtime you have available:

Perl

perl lr2jm.pl <LoadRunner_Script_Directory>

Requires: XML::DOM, XML::Tidy, File::Util

Python

python lr2jm.py <LoadRunner_Script_Directory>

Requires: Python 3.9+ (stdlib only, plus colorit for colored output)

JavaScript

node lr2jm.js <LoadRunner_Script_Directory>

Requires: Node.js (no dependencies)

TypeScript

npx tsc lr2jm.ts --strict --target ES2020 --module commonjs --types node
node lr2jm.js <LoadRunner_Script_Directory>

Requires: Node.js, TypeScript, @types/node

Output

  • <ScriptDir>/<ScriptName>.jmx — complete JMeter test plan
  • <ScriptDir>/<tablename>.csv — converted parameter data files (if .dat files exist)

Example

python lr2jm.py test_lr_script
# Output: test_lr_script/test_lr_script.jmx

Project Structure

.
├── app.py                  # Correlation rules converter (.cor → JSON)
├── lr2jm.pl                # Full script converter — Perl (original)
├── lr2jm.py                # Full script converter — Python
├── lr2jm.js                # Full script converter — JavaScript
├── lr2jm.ts                # Full script converter — TypeScript
├── LR.cor                  # Sample correlation rules file
├── ASP.NET/                # Sample app.py output
├── test_lr_script/         # Sample lr2jm input/output
├── utils/
│   ├── __init__.py
│   └── display.py          # Colored console output helper
├── requirements.txt        # Python dependencies
├── package.json            # Node.js/TypeScript dependencies
└── assets/                 # README images

Tested With

  • Micro Focus LoadRunner 2020 SP 2
  • Apache JMeter 5.3
  • BlazeMeter Correlation Recorder 1.1

License

MIT