We provide most of the reports that are present in our dashboard as APIs which can be integrated into your own system/Business Intelligence tools or some other Dashboards/systems, which will provide insights and help track progress on your own system.

Find the API Documentations on the url:

https://apidocs.appsecengineer.com

Steps:

• Generating API keys

These APIs are authorized using api keys which can be generated from the AppSecEngineer Admin section. Login into AppSecEngineer Portal, go to Admin → Integrations → Enterprise apis

You’ll find the api url and a button to generate api secret. Generate the api secret and note it.

• Review Api docs

We provide different types of reports such as reporting categorized by learning paths, per user reporting, learning journeys reports etc. Carefully check the payload, http method and api path.

The required headers and other attributes are also documented in the api docs guide.

Example code in python:

import requests

api_url = "https://api.appsecengineer.app/enterprise/report/proficiency-report"

headers = {

   "X-ASE-API-Token": "<secret api key>",

   "X-ASE-Customer-ID": "<customer id shown in dashboard>",

   "Content-Type": "application/json",

}

body = {}

try:

   response = requests.post(api_url, json=body, headers=headers)

   if response.status_code == 200:

       print("Request successful!")

       print("Response:", response.json())

   else:

       print(f"Request failed with status code: {response.status_code}")

       print("Response:", response.text)

except requests.exceptions.RequestException as e:

   print("An error occurred:", e)

We only support JSON data as request and response

The response can be transformed into csv/sheet/pdf etc using your own system if needed. Or it can be directly represented in the BI tools.

•  Process and Clean the Data 

Objective: Prepare the retrieved data for reporting by extracting, cleaning, and organizing it

• Parse the JSON response and extract relevant fields. 

• Format the data (e.g., clean dates, remove unnecessary fields). 

• Structure the data into a consistent format for report generation.

Example Python:

1. cleaned_data = [ ] 

2. for item in data['results']: 

3.     cleaned_data.append({ 

4.         'date': item['date'], 

5.         'sales': item['sales'], 

6.         'transactions': item['transactions']

7.  })

• Generate the Report 

Objective: Create a professional report in the desired format (e.g., CSV, Excel, PDF, or HTML)

Options for Report Generation:

CSV Format (Using csv Library):

1. import csv

2. keys = cleaned_data[0].keys() 

3. with open('report.csv', mode='w', newline='') as file: 

4.        writer = csv.DictWriter(file, fieldnames=keys) 

5.        writer.writeheader() 

6.        writer.writerows(cleaned_data)

Excel Format (Using pandas):

1. import pandas as pd 

2. 
   

3. 
   

4. df = pd.DataFrame(cleaned_data) 

5. df.to_excel('report.xlsx', index=False)

• HTML Format: Generate web-friendly reports using HTML tables or templates.

Step 6: Automate Report Generation (Optional)

Objective: Schedule automated report generation for regular intervals (e.g., daily, weekly, monthly)
Automation Methods:

• Linux Cron Jobs: Automate the execution of the script at specific intervals. 

Example:

1. crontab -e 0 0 * * * python3 /path/to/report_script.py # Run at midnight daily

• Windows Task Scheduler: Schedule scripts to run on Windows systems. 

• Cloud Services: Use tools like AWS Lambda or Google Cloud Functions for serverless automation.

Step 7: Distribute the Report 

Objective: Share the generated report with stakeholders or automate the distribution process.
Options for Distribution:

Email: Use email services to send the report automatically. 

Example (Python with smtplib):

1. import smtplib from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email import encoders sender_email = "your_email@example.com" receiver_email = "receiver@example.com" password = "your_email_password" msg = MIMEMultipart() msg['From'] = sender_email msg['To'] = receiver_email msg['Subject'] = "Monthly Sales Report" attachment = MIMEBase('application', 'octet-stream') with open('report.csv', 'rb') as file: attachment.set_payload(file.read()) encoders.encode_base64(attachment) attachment.add_header('Content-Disposition', 'attachment', filename='report.csv') msg.attach(attachment) with smtplib.SMTP('smtp.example.com', 587) as server: server.starttls() server.login(sender_email, password) server.sendmail(sender_email, receiver_email, msg.as_string())

• Cloud Storage: Upload the report to platforms like Google Drive or AWS S3 and share access links.

Step 8: Monitor and Improve 

Objective: Continuously optimize the report generation process.

• Feedback Collection: Gather input from stakeholders on the report’s accuracy, relevance, and design. 

• Improvements: Refine parameters, enhance formatting, and address any data inconsistencies. 

• Error Handling: Implement error logging and handling to ensure reliability

Track learning journey results through our guide on Reports about Learning Journeys

Summary of Steps

1. Select API: Identify the appropriate API for retrieving the required data.

2. Authenticate: Set up secure access using API keys or OAuth tokens.

3. Fetch Data: Retrieve data using well-configured API requests.

4. Process Data: Clean and organize the data for analysis and reporting.

5. Generate Report: Create reports in desired formats (CSV, Excel, PDF, HTML).

6. Automate: Schedule recurring report generation using automation tools.

7. Distribute: Share reports via email or upload them to cloud storage platforms.

8. Monitor & Improve: Continuously test and enhance the process for reliability and usability