spotflow 1.1.0

Runtime analysis for Python programs

SpotFlow

SpotFlow is a tool to ease the runtime analysis of Python programs. With SpotFlow, you can easily extract information about executed methods, run lines, argument values, return values, variable states, and thrown exceptions.

Install

pip install spotflow

Quick usage

A simple code to be monitored in sample.py:

# content of sample.py
def absolute(x):
    if x < 0:
        x = -x
    return x

absolute(-10) # 10

To run and monitor function absolute in sample.py:

$ python -m spotflow -t absolute sample

The result:

Running and monitoring: sample
====================== Result ======================
MonitoredProgram
- methods: 1
- calls: 1
MonitoredMethod
- name: absolute
- calls: 1
MethodCall
- distinct_run_lines: [2, 3, 4]
- run_lines: [2, 3, 4]
ArgState
- x: -10
VarStateHistory
- x: -10, 10
ReturnState: 10

---

Another code to be monitored in sample.py, with two calls:

# content of sample.py
def sum(iter, start=0):
    count = start
    for i in iter:
        count += i
    return count

sum([1, 2]) # 3
sum([1, 2, 3], 1) # 7

To run and monitor function sum in sample.py:

$ python -m spotflow -t sum sample

The result:

Running and monitoring: sample
====================== Result ======================
MonitoredProgram
- methods: 1
- calls: 2
MonitoredMethod
- name: sum
- calls: 2
MethodCall
- distinct_run_lines: [2, 3, 4, 5]
- run_lines: [2, 3, 4, 3, 4, 3, 5]
ArgState
- iter: [1, 2]
- start: 0
VarStateHistory
- iter: [1, 2]
- start: 0
- count: 0, 1, 3
- i: 1, 2
ReturnState: 3
MethodCall
- distinct_run_lines: [2, 3, 4, 5]
- run_lines: [2, 3, 4, 3, 4, 3, 4, 3, 5]
ArgState
- iter: [1, 2, 3]
- start: 1
VarStateHistory
- iter: [1, 2, 3]
- start: 1
- count: 1, 2, 4, 7
- i: 1, 2, 3
ReturnState: 7

---

You can also monitor tests. For example, to monitor the test case TestMessageAPI of the Python Standard Library email:

$ python -m spotflow -t email unittest test.test_email.test_email.TestMessageAPI

Monitoring other tests of the Python Standard Library:

$ python -m spotflow -t ast unittest test.test_ast
$ python -m spotflow -t gzip unittest test.test_gzip
$ python -m spotflow -t urllib unittest test.test_urlparse
$ python -m spotflow -t json unittest test.test_json
$ python -m spotflow -t calendar unittest test.test_calendar

Usage

SpotFlow can be run from the command-line or programmatically via API.

Command-line

We can use SpotFlow to collect data from the execution of any Python program. For example, to run my_program.py, we could originally use the following command-line:

# Running a Python program
$ python -m my_program

The same program can be run (and monitored) under SpotFlow with following command-line:

# Running a Python program + SpotFlow
$ python -m spotflow -t <target> my_program

The argument -t represents the target entity to be monitored. We can pass the full name of the target method (in the format module.Class.method) or a prefix to monitor multiple methods. For example, we can pass the prefix parser (to monitor all methods of a specific module), parser.StringParser (to monitor all methods of a specific class), or the full method name (to monitor a single method). The final mandatory argument is the original command-line, which is in this case my_program.

In a similar way, we can use SpotFlow to monitor the execution of test suites. For example, to run a test testX.py under SpotFlow, the following change would be needed with the unittest framework:

# Running unittest
$ python -m unittest testX

# Running unittest + SpotFlow
$ python -m spotflow -t <target> unittest testX

API

The other way to run SpotFlow is via its API. For example, consider a function called my_program(). It can be run and monitored with SpotFlow with the following code:

flow = SpotFlow()
flow.target_methods(target)

flow.start()

# code to be run and monitored
my_program()

flow.stop()

# the result is a MonitoredProgram object
monitored_program = flow.result()

Class SpotFlow represents the programmatic access to SpotFlow. In method target_methods(), we can pass the target entities to be monitored, which can be the full name of the target method or a prefix to monitor multiple methods, like in the command-line. Method start() starts the monitoring. The monitoring only occurs in code called after start(). After calling start(), we must also call stop() to stop the monitoring. Lastly, method result() provides a MonitoredProgram object.

Monitored entities

• MonitoredProgram: This class is a repository of monitored methods, which can be used to access all collected data.

• MonitoredMethod: It represents a monitored method (or function). It has method calls and contains static information about the method/function, like name, full name, class name (for methods), file name, LOC, source code, etc.

• MethodCall: This class represents a method call that happens at runtime. It includes data about the caller, call stack, and executed lines. A method call also has a call state, which records information like variable and argument states.

• CallState: This class holds the state of a method call, with information about argument states (ArgState), return states (ReturnState), thrown exceptions (ExceptionState), and local variable states (VarStateHistory).

• State: It represents a state at runtime, such as a variable, argument, return, or exception state. All states have information about its runtime value, runtime type, and line number in code. In addition, VarState and ArgState have information about its name.

• VarStateHistory: This class holds all the states of a local variable in a method call. Notice that it is composed of variable states, representing the fact that a variable may change its value and therefore can have multiple states over time.