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Table of Contents
maxLevel2

Target audience:

Users of the extension providing Python support.


Info

Summary: This document provides basic information about the extension providing Python support.

...

com.castsoftware.python

What's new?

Please see Python 1.4 - Release Notes for more information

Description

...

Files analyzed

IconsFileExtensionNote

Image RemovedImage Added

Python.py, Python files - standard extension.

Image RemovedImage Added

Jython.jyBy convention, Python files to be run in a Java implementation of the Python interpreter.
-YAML (YAML Ain't Markup Language)*.yml, *.yaml, Files related to the YAML language, commonly used for configuration purposes. Necessary to interpret Amazon Web Services deployment code.

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Function Points
(transactions)
Quality and SizingSecurity
(tick)(tick)(tick)

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AIP Core compatibility

This extension is compatible with:

8.2.x
CAST AIP release
Supported
8.3.x(tick)(tick)
8.1.x(tick)
8.0.x(tick)
7.3.4 and all higher 7.3.x releases(tick)

Supported DBMS servers

This extension is compatible with the following DBMS servers:

CAST AIP releaseOracle

DBMS

Supported

CSS / PostgreSQL(tick)Microsoft
All supported releases(tick)(tick)(error)

Prerequisites

Prerequisites

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

Dependencies with other extensions

...

Info
Note that when using the CAST Extension Downloader to download the extension and the Manage Extensions interface in CAST Server Manager to install the extension, any dependent extensions are automatically downloaded and installed for you. You do not need to do anything.

Download and installation instructions

Please see:

Info

The latest release status of this extension can be seen when downloading it from the CAST Extend server.

Packaging, delivering and analyzing your source code

Once the extension is installed, no further configuration changes are required before you can package your source code and run an analysis. The process of packaging, delivering and analyzing your source code is as follows:

Discovery

A discoverer is provided together with the extension to automatically detect Python code. One Python project will be discovered for the package's root folder when .py or .jy (jython) files are detected in the root folder or any sub-folders.

Packaging and delivery

Using the CAST Delivery Manager Tool:

  • create a new Version
  • create a new Package for your Python source code using the Files on your file system option:

Click to enlarge:

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  • Define a name for the package and the root folder of your Application source code:

Click to enlarge:

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  • Run the Package action: a Python project will be discovered for the package's root folder when at least one .py or .jy (jython) file is detected in the root folder or any sub-folders:

Click to enlarge:

Image Removed

  • Deliver the Version

Analyzing

Using the CAST Management Studio:

  • Accept and deploy the Version in the CAST Management Studio. In the Current Version tab, An Analysis Unit will be created automatically related to the Python source code whenever a Python project has been detected by the CAST Delivery Manager Tool. In addition, if your Python related source code is part of a larger application, then other Analysis Units may also be created automatically:

Click to enlarge:

Image Removed

  • Run a test analysis on the Analysis Unit before you generate a new snapshot.
Info

Note that it is possible to manually create a Python Analysis Unit if necessary:

  • In the Current Version tab, add a new Analysis Unit specifically for your Python source code, selecting the Add new Universal Analysis Unit option:

Image Removed

  • Edit the new Analysis Unit and configure in the Source Settings tab:
    • a name for the Analysis Unit
    • ensure you tick the Python option
    • define the location of the deployed Python source code (the CAST Management Studio will locate this automatically in the Deployment folder):

Image Removed

Automatic skipping of unit-test code and external libraries

...

Framework support

Web Service Frameworks
(client-side requests and/or server-side operations)

Support

requests(tick)
urllib(tick)
urllib2(tick)
urllib3(tick)
httplib(tick)
httplib2(tick)
http.client(tick)
aiohttp(tick)
flask(tick)
falcon(tick)
web2py(tick)
Cherrypy(tick)
FastAPI(tick)

Dependencies with other extensions

  • Web Services Linker (internal technical extension)
  • CAST AIP Internal extension (internal technical extension)
Info
Note that when using the CAST Extension Downloader to download the extension and the Manage Extensions interface in CAST Server Manager to install the extension, any dependent extensions are automatically downloaded and installed for you. You do not need to do anything.

Download and installation instructions

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:

Anchor
discovery
discovery
Source code discovery

A discoverer is provided with the extension to automatically detect Python code: a Python project will be discovered for the package's root folder when at least one .py or .jy (jython) file is detected in the root folder or any sub-folders. For every Python project located, one Universal Technology Analysis Unit will be created:

Image Added

Analysis - Automatic skipping of unit-test code and external libraries

The analyzer skips files that are recognized as forming part of testing code, i.e., in principle, code not pertaining to production code. The reason to avoid inclusion of testing code is that many Quality Rule violations are overrepresented in test code, either because code tends to be of poorer quality (certainly not critical) or prevalence of particular testing patterns. Accounting for test code would negatively impact the total score of the project.

...

The heuristics used by the analyzer are based on detecting (unit-test) library imports, and file and path naming conventions as summarized in the table below: 

Type

Value

HeaderLines

MinimumCount

Type

Value

HeaderLines

MinimumCount

FilePath**/test_*.py

FilePath**/*_test.py

FilePath**/*_test_*.py

FilePath**/test/*.py

FilePath**/tests/*.py

FileContentimport unittest12
FileContentfrom unittest import12
FileContentfrom nose.tools import12
FileContentself.assert
2
FilePath**/site-packages/**

FilePath**/dist-packages/**
Info
The ** symbol represents any arbitrary path string, whereas * represents any string without directory slashes.

...



FilePath**/Python*/Lib/**

FilePath**/Python*/Scripts/**

FilePath**/Python*/Include/**

FilePath**/Python*/Bin/**


Info
  • The ** symbol represents any arbitrary path string, whereas * represents any string without directory slashes.
  • The heuristics above should also similarly valid for .jy (jython) files.
  • FilePath match is case-insensitive

What results can you expect?

Once the analysis/snapshot generation has completed, you can view the results in the normal manner:

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Python Class and method example

...

The following specific objects are displayed in CAST Enlighten:

IconDescription

Image Modified

Python Project, Python External Library
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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 ServicePython Flask, aiohttp Web

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 ServicePython

POST (urllib, urllib2, httplib, httplib2, aiohttpservice
Python POST service request
Python (Flask, aiohttp) Web Service

Post Operation

POST operation
Python Web Service Post Operation

Image Modified

Python

Put Urllib, Httplib, Httplib2, aiohttp ServicePython

PUT (urllib, urllib2, httplib, httplib2, aiohttpservice
Python PUT service request
Python (Flask, aiohttp

 Web Service Put

) Web Service PUT operation
Python Web Service Post Operation

Image Modified

Python

Delete Urllib, Httplib, Httplib2, aiohttp Service

DELETE (urllib, urllib2, httplib, httplib2, aiohttpservice
Python DELETE service request
Python Flask, aiohttp Web

Service Delete

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 Added

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 Removed

Image Added

Python AWS SQS Publisher, Python AWS SNS Publisher
Image Removed

Image Added

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

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Python DynamoDB Database

Image Added

Python DynamoDB Table

Image Added

Python Unknown DynamoDB Table

Image AddedImage AddedImage Added

Python Email, Python SMS

Python callable artifact

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

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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:

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

urllib

Example for GET request:

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

urllib2

Example for GET request:

Code Block
languagepy
import urllib2

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

Example for POST request.

Code Block
languagepy
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()
Info
PUT and DELETE calls are not supported by the urllib2 module (Python version 2.x) by default. Workarounds to bypass this limitation are not detected by the analyzer.

urllib3

Example for GET request:

Code Block
languagepy
# 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:

Code Block
languagepy
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:

Code Block
languagepy
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':

Code Block
languagepy
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':

Code Block
languagepy
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

Code Block
languagepy
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:

Code Block
languagepy
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.

Code Block
languagepy
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.

Code Block
languagepy
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.

Code Block
languagepy
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'))
  

Image Removed

falcon

Falcon routeannotations for web service operations (GET, PUT, POST, DELETE) are supported. 

In the following example, a default GET operation is ascribed to the functionon_get from GetResourceclass,and the POST and PUT operations to the on_putandon_postfunctions fromPut_PostResourcewith two differents urls routing:

...

importfalcon
 
classGetResource():
defon_get():
print('on_get function')
 
classPut_PostResource():
defon_put():
print('on_get function')
defon_post():
print('on_get function')
 
app=falcon.App()
 
app.add_route('', GetResource())
app.add_route('/url/example/1', Put_PostResource())
app.add_route('/url/example/2', Put_PostResource())

The link between the GET operation named after the routing URL "/"  and the called functionon_get 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 POST operations is "/url/example/1/" and  "/url/example/2/" after the routing url "/url/example/1" and "/url/example/2".

Image Removed

Sinks are supported with the following rules : If no route matches a request, but the path in the requested URI matches a sink prefix, Falcon will pass control to the associated sink, regardless of the HTTP method requested. If the prefix overlaps a registered route template, the route will take precedence and mask the sink.

 In this case Web Service Operation objects generated as sinks will be named as/that/, and not as/this/since another Web Service Operation object exists with an overlapping url.

importfalcon
 
app=falcon.App()
 
classGetResource():
defon_get():
print('on_get function')
 
defsink_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

Image Removed

The optionnal suffixkeyword argument of Falcon add_route is supported. In this way, multiple closely-related routes can be mapped to the same resource.

...

importfalcon
app=falcon.App()
 
classPrefixResource(object):
 
defon_get(self, req, resp):
pass
 
defon_get_foo(self, req, resp):
pass
 
defon_post_foo(self, req, resp):
pass
 
defon_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')

web2py

Example for GET request:

Code Block
languagepy
from gluon.tools import fetch
page = fetch('http://www.google.com/')

Database access

PEP 249

Simple database queries consistent with the Python Database API Specification (PEP 249) are recognized. This allows to support a large number of important libraries interfacing Python and SQL databases (SQLite, MySQL, etc). The analyzer identifies execute method calls as potential database queries and searches for generic SQL statements passed in as an argument ('SELECT ...", "INSERT ...)". In the example below data from the stocks table is retrieved via a SELECT statement passed explicitly by a string to the execute method of a cursor object.

Code Block
languagepy
# 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.

Code Block
languagepy
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.

Image Removed

In some cases SQL queries can be defined via SQL files.

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

where the file db_queries.sql contains SQL code that is analyzed independently by the sqlanalyzer extension.

Code Block
languagesql
CREATE TABLE IF NOT EXISTS informations;
SELECT * FROM informations

In this situation, the analyzer will create a Python File Query object with the name of the sql file. This object will make the link between the method containing the query and the SQL script (if it is present, and dependencies between SQL and Python are configured as previously mentioned), so that the end point of the transaction (for example, a table) can be reached.

Image Removed

Info

Only files containg '.sql' extensions are supported.

SQLAlchemy

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:

Code Block
languagepy
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:

Code Block
languagepy
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.

Code Block
languagepy
# 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 FacilityUNIXLinuxand 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

Code Block
languagepy
import stomp

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

Example of ActiveMQ Consumer

Code Block
languagepy
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

Code Block
languagepy
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

Code Block
languagepy
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

Code Block
languagepy
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

Code Block
languagepy
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). 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:

Code Block
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:

Code Block
languagepy
# 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:

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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:

Code Block
languagepy
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:

Code Block
# 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

Code Block
# 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.

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

...

Note: it is under consideration the creation of generic 'callLinks' for the rest of the API methods acting on S3 buckets (https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#client).

...

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

Code Block
languagepy
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

Code Block
languagepy
# 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).

Code Block
languagepy
# 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

Code Block
languagepy
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.

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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:

Children Display
pagePython 1.4 - Analysis Results

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).