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Software Engineering Radio - the podcast for professional software developers

SE Radio 589: Zac Hatfield-Dodds on Property-Based Testing in Python

Nov 9, 2023
56:47
Snipd AI
Zac Hatfield-Dodds, Assurance Team Lead at Anthropic, speaks about property-based testing in Python using the Hypothesis library. They explore defining properties for Python functions, implementing test cases, and using advanced features of Hypothesis. They also discuss automating testing, input shrinking, fuzz testing, and introduce Anthropic, an AI research company.
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Podcast summary created with Snipd AI

Quick takeaways

  • Property-based testing focuses on checking program properties instead of specific examples.
  • Property-based testing benefits include comprehensive test coverage, identification of edge cases, and improved code reusability.

Deep dives

Property-Based Testing and Hypothesis

Property-based testing is a different approach to software testing that focuses on checking properties of a program rather than specific examples. It involves generating inputs based on a set of rules or constraints and checking if the program exhibits the desired behavior. Hypothesis is a popular property-based testing library for Python that helps developers generate test data and check properties using strategies, decorators, and generators. By using Hypothesis, developers can leverage the power of property-based testing to find edge cases, explore different behaviors, and improve the reliability of their software.

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