mule-analysis 1.0.0

Analyze Mule application estates — call graphs, frequency analysis, curl extraction

mule-analysis

Analyze Mule application estates — call graphs, frequency analysis, and curl extraction.

Scans Mule 4 (and 3) XML source files, resolves ${property} placeholders from .properties and .yaml configs, and produces structured output in multiple formats.

Quick Start (uv)

No install required — just run from the project directory:

cd mule-analysis

# Extract curl commands from a Mule app
uv run mule-curl-extract /path/to/apps -r

# Frequency analysis
uv run mule-call-frequency /path/to/apps

# Call graph
uv run mule-call-graph /path/to/apps --format dot -o graph.dot

uv run reads pyproject.toml, resolves dependencies into an ephemeral environment, and runs the command. Nothing is installed globally.

Installation

From PyPI

pip install mule-analysis

# Then run directly
mule-curl-extract /path/to/apps -r

From source (development)

uv sync --extra dev

Requires Python 3.10+.

CLI Tools

mule-curl-extract

Extract outbound HTTP/SOAP calls from Mule apps and generate ready-to-run curl commands.

# Scan a single Mule app
uv run mule-curl-extract /path/to/mule-app

# Scan all apps under a directory
uv run mule-curl-extract /path/to/apps --recursive

# Output as YAML
uv run mule-curl-extract /path/to/apps -r --format yaml

# Write to file
uv run mule-curl-extract /path/to/apps -r -o curls.sh

Resolves ${property} and p('property') placeholders, extracts basic/bearer auth, and converts DataWeave transform bodies into sample JSON for curl -d flags.

mule-call-frequency

Analyze call frequency across a Mule application estate.

# Text report (default)
uv run mule-call-frequency /path/to/apps

# YAML output
uv run mule-call-frequency /path/to/apps --format yaml -o report.yaml

# Custom layer classification
uv run mule-call-frequency /path/to/apps --layer-config layers.yaml

Reports include:

• App inventory with layer classification (experience / process / system)
• Backend system frequency (which systems are called, how often)
• Inter-app call graph (EL → PL → SL)
• Endpoint hotspots (called from multiple apps/flows)
• Shared sub-flow usage

mule-call-graph

Generate a machine-readable call graph of app-to-app and app-to-backend connections.

# Mermaid (default) — renders in GitHub, GitLab, Notion
uv run mule-call-graph /path/to/apps

# Graphviz DOT — render with: dot -Tpng graph.dot -o graph.png
uv run mule-call-graph /path/to/apps --format dot -o graph.dot

# JSON adjacency list
uv run mule-call-graph /path/to/apps --format json -o graph.json

# CSV edge list
uv run mule-call-graph /path/to/apps --format csv -o edges.csv

# Mule-to-Mule only (exclude external backends)
uv run mule-call-graph /path/to/apps --mule-only

# Custom layer classification
uv run mule-call-graph /path/to/apps --layer-config layers.yaml

Package Structure

src/mule_analysis/
├── xml_helpers.py          # XML namespace helpers, constants
├── models/                 # Data models (dataclasses)
│   ├── app.py              # MuleApp
│   ├── backend.py          # BackendConfig, HttpRequest, SoapCall
│   ├── graph.py            # Node, Edge, CallGraph
│   └── frequency.py        # FrequencyReport, EndpointHotspot, ...
├── parsers/                # File IO → models
│   ├── app_discovery.py    # find_mule_apps()
│   ├── properties.py       # .properties + .yaml loading & resolution
│   └── mule_xml.py         # HTTP configs, requests, WSC from Mule XML
├── analysis/               # Models → models (pure functions)
│   ├── layer_classifier.py # App name → experience/process/system (configurable)
│   ├── curl_generator.py   # BackendConfig + HttpRequest → curl string
│   ├── frequency.py        # Multi-app frequency aggregation
│   └── graph_builder.py    # Multi-app call graph construction
├── output/                 # Models → formatted strings
│   ├── curl_output.py      # Shell script with curl commands
│   ├── yaml_output.py      # YAML
│   ├── text_output.py      # Human-readable tables
│   ├── dot_output.py       # Graphviz DOT
│   ├── mermaid_output.py   # Mermaid flowchart
│   ├── csv_output.py       # CSV edge list
│   └── json_output.py      # JSON
└── cli/                    # CLI entry points (thin wrappers)
    ├── curl_extract.py     # mule-curl-extract
    ├── call_frequency.py   # mule-call-frequency
    └── call_graph.py       # mule-call-graph

Design Principles

• No function does both IO and computation. Parsers read files → return models. Analysis takes models → returns models. Output takes models → returns strings.
• All data models are plain dataclasses with typed fields — no methods with side effects.
• All analysis functions are standalone — no class methods, no inheritance.
• Each output format is a separate module.

Testing

make test

Or directly:

uv run --extra dev python -m pytest

Coverage is enforced at 100% (branch coverage) via pyproject.toml.

Layer Classification

Apps are classified into experience/process/system layers by splitting the app name on - and _ and matching segments against known abbreviations.

Default Segments

Segment	Layer
el, exp, eapi, xapi, experience	Experience
pl, prc, papi, process	Process
sl, sys, sapi, system	System

This means all of these classify correctly out of the box:

acme-el-orders       → experience    (segment: el)
acme-exp-api         → experience    (segment: exp)
orders-eapi          → experience    (segment: eapi)
acme-pl-orders       → process       (segment: pl)
customers-prc-svc    → process       (segment: prc)
orders-papi          → process       (segment: papi)
acme-sl-billing      → system        (segment: sl)
customers-sys-api    → system        (segment: sys)
billing-sapi         → system        (segment: sapi)

Inter-app calls are detected by matching CloudHub hostnames (mule-worker-internal-{env}-{app}-{version}.cloudhub.io) and ALB patterns (mu-alb-{layer}.).

Custom Configuration

Override the defaults by passing a --layer-config YAML file to mule-call-frequency or mule-call-graph:

uv run mule-call-graph /path/to/apps --layer-config layers.yaml

Example layers.yaml:

# Override layer segment keywords (split on - and _)
layers:
  experience: [web, frontend, ui]
  process: [orch, bpm, workflow]
  system: [db, adapter, connector]

# Override CloudHub hostname patterns (regex with capture group)
hostname_patterns:
  - 'mule-worker-(?:internal-)?(?:\\w{2,5}-)(.+?)-\\d+-\\d+\\.'
  - 'mu-alb-(\\w+)\\.'

# Override external backend hostname indicators
external_indicators:
  - oracle
  - salesforce
  - sap

Any section can be omitted to keep the defaults for that section. For example, to only add custom layer segments while keeping the default hostname patterns:

layers:
  experience: [el, exp, eapi, xapi, experience, web]
  process: [pl, prc, papi, process, orch]
  system: [sl, sys, sapi, system, db]