...

Files analyzed

...

An installation of any compatible release of AIP Core (see table above)

Dependencies with other extensions

Some CAST extensions require the presence of other CAST extensions in order to function correctly. The Python extension requires that the following other CAST extensions are also installed:

• Web Services Linker (internal technical extension)
• CAST AIP Internal extension (internal technical extension)

...

Framework support

Web Service Frameworks (client-side requests and/or server-side operations)	Support
requests
urllib
urllib2
urllib3
httplib
httplib2
http.client
aiohttp
flask
falcon
web2py
Cherrypy
FastAPI

Dependencies with other extensions

Some CAST extensions require the presence of other CAST extensions in order to function correctly. The Python extension requires that the following other CAST extensions are also installed:

• Web Services Linker (internal technical extension)
• CAST AIP Internal extension (internal technical extension)

...

The extension will be automatically downloaded and installed in AIP Console when you deliver Python code. You can also manually install the extension using the Application - Extensions interface. When installed, follow the instructions below to run a new analysis/snapshot to generate new results:

• Advanced Modern application onboarding - run and validate the initial deep analysisAdvanced onboarding - snapshot generation and validation
• Legacy application onboarding - Step-by-step onboarding - Run the analysis

Anchor
discovery discovery

Source code discovery

...

The following specific objects are displayed in CAST Enlighten:

Icon	Description
Image Modified	Python Project, Python External Library

Image Removed

Image Added	Python Module
Image Modified	Python Class
Image Modified	Python Method
Image Modified	Python Script
Image Modified	Python GET (urllib, urllib2, httplib, httplib2, aiohttp) service Python GET service request Python (Flask, aiohttp) Web Service GET operation Python Web Service Get Operation
Image Modified	Python POST (urllib, urllib2, httplib, httplib2, aiohttp) service Python POST service request Python (Flask, aiohttp) Web Service POST operation

Image Removed

Python PUT

Python Web Service Post Operation
Image Added	Python PUT (urllib, urllib2, httplib, httplib2, aiohttp) service Python PUT service request Python (Flask, aiohttp) Web Service PUT operation Python Web Service Post Operation
Image Modified	Python DELETE (urllib, urllib2, httplib, httplib2, aiohttp) service Python DELETE service request Python Flask, aiohttp Web Service DELETE operation Python Web Service Delete Operation
Image Added	Python Web Service Any Operation
Image Modified	Python Query, Python ORM Mapping, Python File Query
Image Modified	RabbitMQ Python QueueCall ActiveMQ Python QueueCall IBM MQ Python QueueCall
Image Modified	RabbitMQ Python QueueReceive ActiveMQ Python QueueReceive IBM MQ Python QueueReceive
Image Modified	Python Call To Java Program
Image Modified	Python Call To Generic Program
Amazon Web Services
Image ModifiedImage Modified	Python Call to AWS Lambda Function
Image Modified	Python Call to Unknown AWS Lambda Function
Image Modified	Python AWS Lambda GET Operation
Image Modified	Python AWS Lambda POST Operation
Image Modified	Python AWS Lambda PUT Operation
Image Modified	Python AWS Lambda DELETE Operation
Image Modified	Python AWS Lambda ANY Operation
Image Modified	Python AWS SQS Publisher, Python AWS SNS Publisher
Image Modified	Python AWS SQS Receiver, Python AWS SNS Receiver
Image Modified	Python AWS SQS Unknown Publisher, Python AWS SNS Unknown Publisher
Image Modified	Python AWS SQS Unknown Receiver, Python AWS SNS Unknown Receiver
Image Added	Python S3 Bucket
Image Added	Python Unknown S3 Bucket
Image Added	Python DynamoDB Database
Image Added	Python DynamoDB Table
Image Added	Python Unknown DynamoDB Table
Image ModifiedImage ModifiedImage Modified	Python Email, Python SMS

Python callable artifact

Python Script, Python Module and Python Method objects form part of Python (callable) artifacts.

...

The following structural rules are provided:

...

...

...

...

Web Service calls and operations support

The following libraries are supported for Web Service operations (left) and Web Service HTTP API calls (right):

Image Removed

Once the Python extension analysis is finished, the analyzer will output the final number of web service call and operation objects created.

requests

Example for GET request:

import requests
r = requests.get('https://api.github.com/events')

urllib

Example for GET request:

import urllib.request
with urllib.request.urlopen('http://python.org/') as response:
   html = response.read()

urllib2

Example for GET request:

import urllib2

req = urllib2.Request('http://python.org/')
response = urllib2.urlopen(req)
the_page = response.read()

Example for POST request.

import urllib2
import urllib

values = {'name' : 'Michael Foord',
          'location' : 'Northampton',
          'language' : 'Python' }

data = urllib.urlencode(values)

req = urllib2.Request('http://python.org/', data)
response = urllib2.urlopen(req)
the_page = response.read()

urllib3

Example for GET request:

# using PoolManager
import urllib3
http = urllib3.PoolManager()
r = http.request('GET', 'http://httpbin.org/robots.txt')

# using HTTPConnectionPool
import urllib3
pool = urllib3.HTTPConnectionPool()
r = pool.request('GET', 'http://httpbin.org/robots.txt')

Note: The urllib3 web service object is represented with the same Python GET urllib service as that used for urllib.

httplib

Example for GET request:

from httplib import HTTPConnection
def f():
    conn = HTTPConnection("www.python.org")
    conn.request("GET", "/index.html")

Example link from method "f" to the get httplib service:

Image Removed

http.client

Example for GET request:

from http.client import HTTPConnection
def f():
    conn = HTTPConnection("www.python.org")
    conn.request("GET", "/index.html")

In this case a Python Get Httplib Service will be generated (the httplib module from Python 2 has been renamed to http.client in Python 3).

httplib2

The following code will issue a http get to the url 'https://api.github.com/events':

import httplib2
h = httplib2.Http(".cache")
(resp, content) = h.request("https://api.github.com/events")

aiohttp

The following code will issue a http get to the url 'https://api.github.com/events':

import aiohttp
session = aiohttp.ClientSession()
res = session.get('https://api.github.com/events')

The aiohttp module can be also used in server mode, implementing web service operations

from aiohttp import web
async def handler(request):
    return web.Response(text="Welcome in Python")
app = web.Application()
app.router.add_get('/index', handler)
web.run_app(app)

In this case a Web Service Operation object associated to the function (coroutine) handler will be generated similar to the example for flask given below.

flask

Flask route annotations for web service operations (GET, PUT, POST, DELETE) are supported. In particular, any decorator with the format @prefix.route is considered as a flask annotation where prefix can be a Flask application object or blueprint object. In the following example, a default GET operation is ascribed to the function f, and the POST and PUT operations to the upload_file function:

from flask import Flask
app = Flask(__name__)
 
@app.route('/')
def f():
    return 'hello world!'
 
@app.route('/upload', methods=['POST', 'PUT'])
def upload_file()
	if request.method == 'POST':
            pass
	# ...

The link between the GET operation named after the routing URL "/"  and the called function f is represented by an arrow pointing to the function:

Image Removed

The name of a saved Web Service Operation object will be generated from the routing URL by adding a final slash when not present. In this example the name of the PUT and POST operations is "/upload/" after the routing url "/upload".

Image Removed

URL query parameters such as @app.route('/user/<username>') are supported. In this case the generated Web Service Operation object will be named as /user/{}/, as shown in the example below.

from flask import Flask
app = Flask(__name__)
 
@app.route('/user/<username>')
def show_user_profile(username):
    return 'User %s' % username

Image Removed

Similarly double slashes // in flask routing URLs are transformed into /{}/. Additional backslashes inside URL query parameters of type path [ @app.route('/<path:path>') ] are not resolved (which in principle could catch any URL) so the web service will be named as a regular parameter /{}/.

The equivalent alternative to routing annotations using the Flask add_url_rule is also supported.

from flask import Flask
app = Flask(__name__)    
 
def index():
    pass
    
app.add_url_rule('/', 'index')

Plugable views are also supported for Flask add_url_rule.

from flask.views import MethodView

class InformationAPI(MethodView):

    def get(self):
        information = Information.from_data(request.data)
        ...

app.add_url_rule('/<info>/informations/', view_func=InformationAPI.as_view('informations'))

importfalcon
 
app=falcon.App()
 
class GetResource():
def on_get():
print('on_get function')
 
def sink_method(resp,**kwargs):
resp.body="Sink"
pass
 
app.add_route('this/is/the/way', GetResource())
app.add_sink(sink_method, prefix='/that') # get, post, put & delete routes will be created and linked to sink_method
app.add_sink(sink_method, prefix='/this') # no routes created because Url overlaps another route

import falcon
app=falcon.App()
 
class PrefixResource(object):
 
def on_get(self, req, resp):
pass
 
def on_get_foo(self, req, resp):
pass
 
def on_post_foo(self, req, resp):
pass
 
def on_delete_bar(self, req, resp):
pass
 
app.add_route('get/without/prefix', PrefixResource())
app.add_route('get/and/post/prefix/foo', PrefixResource(), suffix='foo')
app.add_route('delete/prefix/bar', PrefixResource(), suffix='bar')

from gluon.tools import fetch
def m(self):
    page = fetch('http://www.google.com/')

Generic service requests

Python GET/POST/PUT/DELETE service request objects will be used as generic objects for new supported frameworks implementing APIs to access web services.

# query.py
import sqlite3

conn = sqlite3.connect('example.db')
c = conn.cursor()
c.execute('SELECT * FROM informations') 

In addition to execute method calls, the analyzer identifies raw method calls which are used in Django framework. SQL queries can be defined directly or via a method.

from django.db import models
...
def function(self):
    sql = 'SELECT * FROM informations'
    return model.objects.raw(sql)

The analyzer creates a Python Query object with name SELECT * FROM informations (first 4 words are only used as naming convention) representing a call to a database. Provided analysis dependencies between SQL and Python are configured in CAST Management Studio, the analyzer will automatically link this object to the corresponding Table object, in this case informations, that has been generated by a SQL analysis unit.

def function(self):
	file_path = "db_queries.sql"
	sql = open(file_path).read()
	cursor.execute(sql)

CREATE TABLE IF NOT EXISTS informations;
SELECT * FROM informations

SQLAlchemy is a Python SQL toolkit providing a way of interaction with databases. SQLAlchemy includes both a database server independent SQL expression language and an Object Relational Mapper (ORM). An ORM presents a method of associating user-defined Python classes with database tables and instances of the classes(objects) with rows in their corresponding tables. The analyzer identifies query method calls in addition to execute method calls.

Example using query method call:

class UserTable:
    __tablename__ = "users"
    
    def __init__(self):
        pass

class User(UserTable):
    __tablename__ = "users_table"

    def __init__(self):
        UserTable.__init__()
        
    def f(self):
        query = UserTable.query().filter(UserTable.name == "new_user") #query().filter(...) is equivalent to SELECT statement

Example using execute method call:

class Information:
    __tablename__ = "informations"

    def find_information(self):
        informations_table = Information.__table__
        select_query = (
            informations_table.filter(informations_table.id == target.information_id)
            )
        connection.execute(select_query)

In this example the analyzer creates a Python ORM Mapping object with the name of the table designated by __tablename__ in class. As in the case of creation of Python Query objects, it is assumed that analysis dependencies between SQL and Python are correctly configured in CAST Management Studio. Then, links between these objects and the corresponding Table objects (in this example informations, generated by a SQL analysis unit) will automatically be created by the analyzer. The type of the link in this particular case is useSelectLink (Us) because of the filter() method call present in the query expression.

Image Removed

File system access functions

Representing end points based on file system access is important to automatically configure transactions. Towards this goal we aim at providing automatic recognition of standard library input/output functions. Currently we provide support for the built-in open function and the most common methods associated to file-like objects write, read, writelines, readline, readlines, and close,  as shown in the example below.

# file1.py
 
data = """<html>
<header><title>This is title</title></header>
<body>
Hello world
</body>
</html>
"""
 
f = open('page.html', 'w')
f.write(data)
f.close()

The objects corresponding to the code of the file1.py file are inside the Universal Directory root object. Additionally the analyzer will automatically generate a Python external library object representing the Python Standard Library. Within this, the Python built-in library is abstracted as a Python source code object named builtins.py, with its corresponding open function and file class (abstracting general file-like objects) that contains the above mentioned methods. No differences are considered between Python2 and Python3 built-in functions. Notice the external character of these objects denoted by gray-shaded icons in the left Object Browser panel.

Image Removed

Due to implementation constraints in CAIP versions [7.3.6, 8.1] a spurious link is generated between the Python external library object and a PY File object.

Message Queues support

Introduction

Message queues are software-engineering components used for inter-process communication, or for inter-thread communication within the same process. They use a queue for messaging. A producer posts messages to a queue. At the appointed time, the receivers are started up and process the messages in the queue. A queued message can be stored and forwarded, and the message can be redelivered until the message is processed. Message queues enable asynchronous processing, which allows messages to be queued without the need to process them immediately.

Message Queues currently handled by the Python analyzer

ActiveMQ

Apache ActiveMQ is an open source message broker written in Java together with a full Java Message Service (JMS) client. The goal of ActiveMQ is to provide standards-based, message-oriented application integration across as many languages and platforms as possible. ActiveMQ acts as the middleman allowing heterogeneous integration and interaction in an asynchronous manner.

IBM MQ

IBM MQ is a family of network message-oriented middle ware products that IBM launched. It was originally called MQSeries (for "Message Queue"), and was renamed WebSphere MQ to join the suite of WebSphere products. IBM MQ allows independent and potentially non-concurrent applications on a distributed system to securely communicate with each other. IBM MQ is available on a large number of platforms (both IBM and non-IBM), including z/OS (mainframe), OS/400 (IBM System i or AS/400), Transaction Processing Facility, UNIX, Linux, and Microsoft Windows.

RabbitMQ

RabbitMQ is an open source message-queueing software called a message broker or queue manager RabbitMQ implements AMQP. It supports multiple messaging protocols. RabbitMQ can be deployed in distributed and federated configurations to meet high-scale, high-availability requirements.

Message queue applications using the below mentioned frameworks/clients are handled:

• Library interface with STOMP protocol for ActiveMQ

• Pika client with AMQP protocol for RabbitMQ
• MQ-Light client with TCP/IP for IBM MQ
• Pymqi python extension for IBM MQ

CAST Enlighten screenshots

When a message queue application is analyzed by the Python analyzer, the following transactions can be found at the end of analysis:

Example of ActiveMQ Producer

import stomp

conn = stomp.Connection10()
conn.start()
conn.connect()
conn.send('SampleQueue', 'Its working!!')
conn.disconnect()

Example of ActiveMQ Consumer

import stomp

queue = 'SampleQueue'
conn = stomp.Connection10()
conn.start()
conn.connect()
conn.subscribe(queue)
conn.disconnect()

CAST Enlighten screenshot of ActiveMQ Transaction

Image Removed

Example of RabbitMQ Producer

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))
channel = connection.channel()
 
channel.queue_declare(queue = "sample_queue")
channel.basic_publish(exchange = '', routing_key = "sample_queue", body = "Hello world!" )
connection.close()

Example of RabbitMQ Consumer

import pika

def callback(ch, method, properties, body):
    print("[x] Received % r" % body)
 
connectionconnection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))
channel = connection.channel()
channel.queue_declare(queue = "sample_queue")
channel.basic_consume(callback, queue = "sample_queue", no_ack = True)
channel.start_consuming()

CAST Enlighten screenshot of RabbitMQ Transaction

Image Removed 

Example of IBM MQ Producer

import pymqi

def send_message(self):
    queue_manager = "QM01"
    channel = "SVRCONN.1"
    host = "192.168.1.135"
    port = "1434"
    queue_name = "TEST.QUEUE1"
    message = "Hello from Python!"

    qmgr = pymqi.connect(queue_manager, channel, conn_info)
    queue = pymqi.Queue(qmgr, queue_name)
    queue.put(message)
    queue.close()
    qmgr.disconnect()

Example of IBM MQ Consumer

import pymqi

def on_message(self,headers, msg):
    queue_manager = "QM01"
    channel = "SVRCONN.1"
    host = "192.168.1.135"
    port = "1434"
    queue_name = "TEST.QUEUE1"

    qmgr = pymqi.connect(queue_manager, channel, conn_info)
    queue = pymqi.Queue(qmgr, queue_name)
    message = queue.get()
    queue.close()
    qmgr.disconnect()

CAST Enlighten screenshot of IBM MQ Transaction

Image Removed

...

The library boto3 is supported, the AWS SDK for python (with certain limitations). Support to Amazon Simple Notification Service (SNS) for Python in boto3 (Python SDK for Amazon Web Services) is available in Python versions ≥ 1.4.0-beta5. Configuration YAML files are also analyzed in search of serverless deployment frameworks.

...

Serverless framework, Serverless Application Model (SAM), and Cloudformation are supported. These are frameworks using *.yml and *.yaml (or *.json, currently not supported in this extension) file to set up AWS environment. 
Whenever the runtime set in these files is pythonX.Y, the com.castsoftware.python extension is responsible for creating the corresponding Python AWS Lambda Function, Python AWS Lambda Operation (which represent AWS APIGateway events), and Python AWS Simple Queue objects. 

For example in the .yml deployment file below (taken from the Serverless examples for AWS) a Lambda function is defined (hello) and the handler's method name is referred:

service: aws-python # NOTE: update this with your service name

frameworkVersion: '2'


provider:
  name: aws
  runtime: python3.8
  lambdaHashingVersion: 20201221

functions:
  hello:
    handler: handler.hello

where the Python code of the handler:

# handler.py

def hello(event, context):
    body = {
        "message": "Go Serverless v2.0! Your function executed successfully!",
        "input": event,
    }

    return {"statusCode": 200, "body": json.dumps(body)}

The results in Enlighten:

Image Removed

Boto3 AWS sdk for Python

...

Supported API methods (boto3)

...

• botocore.client.Lambda.invoke

...

Python Call to AWS Lambda Function

...

• botocore.client.Lambda.invoke_async

Example

A simple example showing representation of an invocation of a AWS Lambda function:

def func():
    lambda_client.invoke(FunctionName='otherfunctionname',
                     InvocationType='RequestResponse',
                     Payload=lambda_payload)

Image Removed

...

Supported API methods (boto3)

...

• botocore.client.SQS.send_message

• botocore.client.SQS.send_message_batch

...

Python AWS SQS Publisher
Python AWS SQS Unknown Publisher

...

• botocore.client.SQS.receive_message

...

Python AWS SQS Unknown Receiver
Python AWS SQS Receiver

...

Code samples

In this code, the module sqs_send_message.py publishes a message into the "SQS_QUEUE_URL" queue and in sqs_receive_message.py is received:

# Adapted from https://boto3.amazonaws.com/v1/documentation/api/latest/guide/sqs-example-sending-receiving-msgs.html#example
# sqs_receive_message.py

import boto3

# Create SQS client
sqs = boto3.client('sqs')

queue_url = 'SQS_QUEUE_URL'

# Receive message from SQS queue
response = sqs.receive_message(QueueUrl=queue_url, ...)

and

# Adapted from https://boto3.amazonaws.com/v1/documentation/api/latest/guide/sqs-example-sending-receiving-msgs.html#example
# sqs_send_message.py
 
import boto3

# Create SQS client
sqs = boto3.client('sqs')

queue_url = 'SQS_QUEUE_URL'

# Send message to SQS queue
response = sqs.send_message(QueueUrl=queue_url, ...)

The results derived from the analysis of the above code can be seen Enlighten:

Click to enlarge

Image Removed

Note: when the name of the queue passed to the API method calls is resolvable (either because of unavailability or because of technical limitations), the analyzer will create Unknown Publisher and Receive objects.

...

There are two different APIs to manage SNS services, one based on a low-level client and the higher-level one based on resources.

...

Supported API methods (boto3)

...

botocore.client.SNS.create_topic

...

N/A

...

Python callable artifact

...

Python AWS SNS Publisher,
Python AWS SNS Unknown Publisher, Python SMS

...

Python AWS SNS Receiver,
Python AWS SNS Unknown Receiver

...

Python Call to AWS Lambda Function, 
Python AWS SQS Publisher, Python SMS, Python Email

...

Python AWS SNS Publisher,
Python AWS SNS Unknown Publisher, Python SMS

...

Python AWS SNS Receiver,
Python AWS SNS Unknown Receiver

...

Python AWS SNS Publisher,
Python AWS SNS Unknown Publisher, Python SMS

The supported protocols are the following:

...

protocol

...

object created

...

name of the object

...

The code example below shows a basic usage of the boto3 library and the results as seen in Enlighten after analysis of the code.

import boto3

client = boto3.client('sns', region_name='eu-west-3')
topicArn1 = client.create_topic( Name = "TOPIC1")['TopicArn']

def publish(topic):
    client.publish(TopicArn=topic, Message='<your message>')

def subscribe(topic):
    client.subscribe(TopicArn=topic, Protocol="email", Endpoint="lili@lala.com")
    client.subscribe(TopicArn=topic, Protocol="sms", Endpoint="123456789")
    client.subscribe(TopicArn=topic, Protocol="sqs", Endpoint="arn:partition:service:region:account-id:queueName")
    client.subscribe(TopicArn=topic, Protocol="http", Endpoint="http://foourl")
    client.subscribe(TopicArn=topic, Protocol="lambda", Endpoint="fooarn:function:lambda_name:v2")
    
publish(topicArn1)
subscribe(topicArn1)

The callLink links between the Publisher and the respective Subscribers are created by the Web Services Linker extension during application level.

Image Removed

Note that for each method a maximum of one subscriber per given topic will be created as shown in the image above. In the absence of a well-resolved topic, the analyzer will create Unknown Publishers and Subscribers. There is no link created between unknown objects.

We can also have direct sms deliveries from calls to publish API methods:

import boto3
AWS_REGION = "us-east-1"

def send_sms_from_resource():
    sns = boto3.resource("sns", region_name=AWS_REGION)
    platform_endpoint = sns.PlatformEndpoint('endpointArn')
    platform_endpoint.publish(PhoneNumber='123456789')

def send_sms():
    conn = boto3.client("sns", region_name=AWS_REGION)
    conn.publish(PhoneNumber='123456789')

where the corresponding objects and links are:

Image Removed

AWS DynamoDB

See DynamoDB support for Python source code.

AWS S3

...

Supported API methods

...

botocore.client.S3.create_bucket

...

N/A

...

botocore.client.S3.put_object

...

Python S3 Bucket, Python Unknown S3 Bucket

...

botocore.client.S3.delete_bucket

...

Python S3 Bucket. Python Unknown S3 Bucket

...

botocore.client.S3.delete_object

...

botocore.client.S3.get_object

...

botocore.client.S3.get_object_torrent

...

botocore.client.S3.list_objects

...

botocore.client.S3.put_bucket_logging

...

...

Introduction

Python, often used to glue together different components of an application, provides various mechanisms to call external programs. By supporting these calls the analyzer can provide the linkage between different technology layers.

...

Supported API methods

...

os.system

...

Python Call to Java Program, Python Call to Generic Program

...

os.popen

...

subprocess.call

...

subprocess.check_call

...

subprocess.run

...

subprocess.Popen

Technologies currently handled by the Python analyzer

The Python analyzer currently supports calls to the following technologies

• Cobol
• Java: classes and .jar
• Python
• Shell

The Java technology is specific and has its own object because links are made using the fullname of the class, package and class name.

Furthermore, the link is not made to the class object but directly to its main method. Indeed, Java program can only be called if they contain a main method.

CAST Enlighten screenshots

When a call to an external program is analyzed by the Python analyzer, the following transactions can be found at the end of analysis:

Example of call to an external program

import subprocess
from subprocess import Popen

subprocess.call('/bin/java com.cast.Classe')
cmd = './hello.sh'
popen = Popen(cmd)

CAST Enlighten screenshot of call to an external program

Image Removed

Python code can also call a different Python program via the python (or jython) executable. Then the analyzer will create, as shown before, "Python Call to Generic Program" objects and they will be linked to the corresponding "Python Main" objects during application level analysis via web service linker extension. For example launch.py will invoke the run.py script in the code below

# launch.py

import subprocess
from subprocess import Popen

cmd = 'python run.py'
popen = Popen(cmd)

where the target code contains a code block in the top-level script environment (signaled by the "if __name__ ..." structure).

# run.py

def run():
    print("running...")

if __name__=="__main__":
    run()

so as a results we would have

Image Removed

Links handled by command line parsers

The Python analyzer fulfills the call-links handled by the plac framework that facilitates the manipulation of command line arguments of Python scripts.

Example of call from plac module

class Interface():
    commands = ['Method2']

    def Method1(self):
        pass

    def Method2(self):
        pass

if __name__ == '__main__':
    plac.Interpreter.call(Interface)

In this example, the "script" character of the source file is followed by the presence of the "if __name__ == ..." structure. This structure is represented by the analyzer with a Python main object that serves as an entry point for receiving (external) calls. The call handled by plac between plac.Interpreter.call() and Method2 will be modelized as call-link by the Python analyzer as shown below.

CAST Enlighten screenshot of call handled by plac.

...

You can also find a global list here: https://technologies.castsoftware.com/rules?sec=t_1021000&ref=||

Anchor
expectedresultsforsupportedframeworks expectedresultsforsupportedframeworks

Expected results for supported frameworks

Refer the following pages for the results that you can expect for each supported framework:

Limitations

• Not fully supported Python Decorator function.
• Quality rules do not apply to code inside the class definition (class or "static" variables)
• The "Avoid disabling certificate check when requesting secured urls" for 'urllib3' is only partially supported by detecting the call to 'urllib3.disable_warnings'.
• Limited Python resolution that leads to missing links:
  • No support for __all__
  • No support for variable of type class, function
• Flask:
• Objects for other web service operations such as PATCH are not generated.
• The endpoint abstraction layer between functions and annotations is not considered. When using  add_url_rule the endpoint argument is taken as the calling function name.
• Cherrypy:

  • Only support default request.dispatcher "cherrypy.dispatch.MethodDispatcher()".

• Django framework is not supported.
• Java-Python interoperability via Jython is not supported. However the files with the specific extension .jy for Jython is analyzed as a regular Python file.
• Message queues
  •  To generate queue message objects the queue name has to be initialized explicitly in the code (dynamic naming not supported).