Building a Python API Automation Testing Framework: Design, Configuration, Logging, Database, and Email Integration

After the company's testing focus shifted from web UI to API testing, the author decided to build a Python automation framework to replace manual Postman/JMeter work, noting Python's simplicity compared to Java.

The framework starts with a config.ini file that stores immutable settings such as database credentials, HTTP endpoints, and email parameters, allowing easy updates without changing code. readConfig.py reads these settings using configparser and provides getter methods ( get_email, get_http, get_db) for other modules.

The Log.py module creates a thread‑safe logger: it builds a timestamped result directory, configures a logging.FileHandler with a formatter, and exposes the logger via a singleton MyLog class. ConfigHttp.py wraps the requests library, exposing set_url, set_headers, set_params, set_data, and set_files methods, and provides get and post functions that handle timeouts and logging.

The common package includes utilities for reading test cases from Excel ( xlrd) and SQL statements from XML, as well as the logging class used throughout the framework.

Database interaction is encapsulated in MyDB, which reads connection parameters from config.ini, establishes a MySQL connection via pymysql, executes SQL, fetches results, and closes the connection.

Email reporting is implemented in configEmail.py. It builds a multipart message, attaches a zipped test report, and sends it through a 163.com SMTP server, with thread‑safe singleton access via MyEmail.

The test runner ( runAll.py) reads a caselist.txt file, loads test cases with unittest, runs them using HTMLTestRunner to generate an HTML report, and optionally sends the report via email based on the on_off flag.

Directory layout: result stores timestamped logs and reports; testCase holds unittest scripts prefixed with test; testFile contains the Excel case files and XML SQL files; caselist.txt lists the cases to execute.

Overall, the article provides a complete, modular Python framework for API testing, demonstrating how to separate configuration, logging, HTTP handling, data management, and reporting to create a maintainable automated test suite.