Identifying and Reducing Test Case Combinatorial Explosions with Python's Abstract Syntax Tree (AST) and Pytest Framework

Test matrix explosions are a growing challenge in modern software testing, especially when using parameterized tests in machine learning pipelines.

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This article presents a hybrid approach, combining Python's Abstract Syntax Tree (AST) analysis and the parameterized testing framework Pytest to identify a mapping of test parameterizations, thereby revealing hidden redundancies. By strategically trimming the test matrix, this approach reduced test cases for the development team I'm on by 35% and reduced CI/CD pipeline execution times by more than one-fourth to half, demonstrating the power of static analysis for smarter test management.

Python-based testing frameworks like Pytest are widely used for unit, integration, and system-level testing. As testers, we strive to maximize coverage across diverse workflows, from simple user interactions to complex developer pipelines. However, test matrices tend to grow exponentially as features evolve, especially when leveraging parameterized testing.

The Challenge: When Parameterization Becomes Unmanageable

Parameterization enhances coverage, but it can silently grow to an unmanageable scale. This is particularly common in machine learning (ML) environments, where tests involve varying model parameters.

My workplace supports multiple open-source ML model formats and validates their conversion within our ecosystem. This led to scenarios such as:

• Longer test execution times delaying feedback loops

• Redundant testing, where similar paths were repeatedly verified

• Increased maintenance overhead, making test suite management harder

• Scaling challenges when running tests across multiple devices and platforms

To maintain efficient testing without sacrificing coverage, I wanted to analyze and optimize the test matrix strategically.

The testing pipeline followed a consistent workflow:

1. Parameterize the test function.