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DESCRIPTION:Speaker: Arun Vishwanathan ([Connect on LinkedIn](https://www.l
 inkedin.com/in/arun-vishwanathan-16b56917/))\n\nTitle: Identifying Potenti
 al Test Reductions Using Pytest and AST Analysis\n\nAbstract: Test matrix 
 explosions are a growing challenge in modern software testing\, especially
  when using parameterized tests in machine learning pipelines. This talk p
 resents a hybrid approach that combines Python&#39;s Abstract Syntax Tree (AST
 ) analysis and Python&#39;s Test Framework parameterized testing to identify p
 otential redundancies. By strategically trimming the test matrix\, we can 
 achieve quicker testing and faster turnarounds.\n\nBio: 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 tes
 t automation\, boost productivity\, and enable cross-team collaboration. C
 urrently\, he is developing a test frameworks for evaluating Machine Learn
 ing models. He holds a Master’s degree in Computer Science from the Univ
 ersity of Southern California\, Los Angeles.\n\nSpeaker(s): Arun Vishwanat
 han\n\nVirtual: https://events.vtools.ieee.org/m/490367
LOCATION:Virtual: https://events.vtools.ieee.org/m/490367
ORGANIZER:ruben.glatt@ieee.org
SEQUENCE:15
SUMMARY:Tech Talk: Identifying Potential Test Reductions Using Pytest and A
 ST Analysis
URL;VALUE=URI:https://events.vtools.ieee.org/m/490367
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;strong&gt;Speaker: &lt;/strong&gt;Arun Vishwanatha
 n (&lt;a title=&quot;https://www.linkedin.com/in/arun-vishwanathan-16b56917/&quot; href
 =&quot;https://www.linkedin.com/in/arun-vishwanathan-16b56917/&quot; target=&quot;_blank&quot;
  rel=&quot;nofollow noopener noreferrer ugc&quot;&gt;Connect on LinkedIn&lt;/a&gt;)&lt;/p&gt;\n&lt;p&gt;&lt;
 strong&gt;Title:&lt;/strong&gt; Identifying Potential Test Reductions Using Pytest 
 and AST Analysis&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Test matrix explosions
  are a growing challenge in modern software testing\, especially when usin
 g parameterized tests in machine learning pipelines. This talk presents a 
 hybrid approach that combines Python&#39;s Abstract Syntax Tree (AST) analysis
  and Python&#39;s Test Framework parameterized testing to identify potential r
 edundancies. By strategically trimming the test matrix\, we can achieve qu
 icker testing and faster turnarounds.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Bio:&lt;/strong&gt; Arun V
 ishwanathan is a Senior Software Development Test Engineer at Apple and sp
 ecializes in Software Testing and Automation\, with over a decade of exper
 ience in the field. His work focuses on building tools and frameworks that
  enhance test automation\, boost productivity\, and enable cross-team coll
 aboration. Currently\, he is developing a test frameworks for evaluating M
 achine Learning models. He holds a Master&amp;rsquo\;s degree in Computer Scie
 nce from the University of Southern California\, Los Angeles.&lt;/p&gt;
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