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A solution for chatbot constructors to identify problems in flow structure.

Project description

Blip Flow Analysis

Blip Flow Analysis provides a solution for chatbot constructors to identify problems in flow structure that can be originated from bad structuring or poor organization.

Installation

Use pip to install:

pip install blip_flowanalysis

Usage

Missing Trackings analysis

Using the MissingTrackigns analyser:

import blip_flowanalysis as bfa

# Replace __chatbot_as_json__ parameter by your json bot.
bot_flow = bfa.Flow(__chatbot_as_json__)
analyser = bfa.MissingTrackings(minimum=1)

# Return `True` if amount of Trackings is above minimum, `False` otherwise.
print(analyser.analyse(bot_flow)) 

Process HTTP Return Validation analysis

Two types of validation are performed for each HTTP request in the chatbot flow:

  • Next action validation: check if request is validated by the action immediately after it.
  • Outputs validation: check if the request is validated by the outputs of the request's state.

The HTTP request validation process has three possible outcomes:

  • status validation: validation found using the request's status variable (best practice);
  • body validation: validation found using the request's body variable (warning);
  • no validation: validation wasn't found using either status or body variable (warning).

Using the ProcessHTTPReturnValidation analyser:

import blip_flowanalysis as bfa

# Replace __chatbot_as_json__ parameter by your json bot.
bot_flow = bfa.Flow(__chatbot_as_json__)
http_analyser = bfa.ProcessHTTPReturnValidation()

# Return validation report with "summary" and "warnings".
validation_report = http_analyser.analyse(bot_flow)

print(validation_report)

The structure of the validation report is presented bellow.

{
    "summary":
    {
        "total": number of HTTP requests found,
        "status validation": number of requests with validation of status variable,
        "body validation": number of requests with validation of body variable,
        "no validation": number of requests without validation of either status or body variable
    },
    "warnings":
    [
        {
            "state id": state id in which the request action is located,
            "action type": either "inputActions" or "outputActions",
            "status variable": status variable name,
            "body variable": body variable name,
            "validation": either "body validation" or "no validation"
        }
    ]
}

Long Scripts analysis

Using the LongScript analyser:

# import json
import blip_flowanalysis as bfa

bot_json = str("Your chatbot json here.")
# bot_json = json.load("or your chatbot json file here")

bot_flow = bfa.Flow(bot_json)
ls_analyser = bfa.LongScript()

# Report with "summary" and "details" with long scripts detections.
report = ls_analyser.analyse(bot_flow)

print(report)

Output is like:

{
    "summary": {
        "scripts count": n_scripts,
        "scripts too long": n_long_scripts,
        "states count": n_states,
        "states with too long scripts": n_irregular_states
    },
    "details": [
        {
            "state id": state_id,  # State ID on bot flow.
            "state name": state_name,  # State name on bot flow.
            "io action": io_action,  # Any of inputActions and outputActions.
            "action number": action_number,  # Action position on actions list.
            "script": script,  # Script.
            "chars": length,  # Code characters quantity.
            "lines": lines,  # Not blank code lines quantity.
            "functions": functions,  # Top level functions quantity.
            "commands": commands,  # Commands quantity.
            "lines by commands": lines_by_commands,  # Lines commands ratio.
            "causes": cause  # Explain why this script was detected.
        },
        ...
    ]
}

Author

Take Data&Analytics Research

License

MIT License

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