Identifying Potential Test Reductions Using Pytest and AST Analysis

Identifying Potential Test Reductions Using Pytest and AST Analysis

Introducing a hybrid AST and parameterized testing approach to reduce test matrix redundancy in machine learning pipelines.

By IEEE Computer Society SF/OAK/EB

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Online

About this event

  • Event lasts 1 hour

The San Francisco Bay Area chapter of the IEEE Computer Society invites for a free and open Tech Talk (no IEEE membership required):

Speaker: Arun Vishwanathan (Connect on LinkedIn)

Title: Identifying Potential Test Reductions Using Pytest and AST Analysis

Abstract: Test matrix explosions are a growing challenge in modern software testing, especially when using parameterized tests in machine learning pipelines. This talk presents a hybrid approach that combines Python's Abstract Syntax Tree (AST) analysis and Python's Test Framework parameterized testing to identify potential redundancies. By strategically trimming the test matrix, we can achieve quicker testing and faster turnarounds.

Bio: Arun Vishwanathan is a Senior Software Development Test Engineer at Apple and specializes in Software Testing and Automation, with over a decade of experience in the field. His work focuses on building tools and frameworks that enhance test automation, boost productivity, and enable cross-team collaboration. Currently, he is developing a test frameworks for evaluating Machine Learning models. He holds a Master’s degree in Computer Science from the University of Southern California, Los Angeles.

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Is an IEEE membership required?

No, everybody is welcome to participate.

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