Este documento describe cómo las escuelas tradicionales están siendo atravesadas por las nuevas tecnologías de la información y la comunicación (TIC), pero no las están incorporando de manera efectiva. Las escuelas mantienen una actitud defensiva y de sospecha hacia las TIC en lugar de usarlas de forma creativa en los procesos de enseñanza y aprendizaje. Los docentes no están capacitados para usar las TIC, mientras que los estudiantes son nativos digitales. Se necesita una política educativa que incluya a los docentes en el
The document discusses property-based testing and provides examples of property tests in different programming languages like Java, PHP, Go, and Rust. It explains the concept of property-based testing where tests make statements about the output of code based on different inputs. Examples include testing properties of a rectangle area function, offer calculations, mean/variance/standard deviation calculations, and quickcheck tests.
The document discusses property-based testing and how it can be used to test code more effectively than example-based testing. It introduces the concepts of testing properties like commutativity, associativity, and identity to specify requirements at a deeper level. QuickCheck is presented as a tool that generates random test values to check properties, finding bugs more quickly than testing by hand. JUnit Quickcheck and JSVerify are given as libraries for implementing property tests in Java and JavaScript. Potential applications of property testing like testing SDKs and data processing frameworks are outlined.
Making property-based testing easier to read for humansLaura M. Castro
Agile practices have taught us that both stakeholder involvement and early testing are key to quality software. However, it is usually the case that tools for good communication are not that good for testing, and vice-versa.
In this talk, readSpec (one of the results of the PF7 EU PROWESS project) is presented, a tool that attempts to fill in this gap.
Este documento describe cómo las escuelas tradicionales están siendo atravesadas por las nuevas tecnologías de la información y la comunicación (TIC), pero no las están incorporando de manera efectiva. Las escuelas mantienen una actitud defensiva y de sospecha hacia las TIC en lugar de usarlas de forma creativa en los procesos de enseñanza y aprendizaje. Los docentes no están capacitados para usar las TIC, mientras que los estudiantes son nativos digitales. Se necesita una política educativa que incluya a los docentes en el
The document discusses property-based testing and provides examples of property tests in different programming languages like Java, PHP, Go, and Rust. It explains the concept of property-based testing where tests make statements about the output of code based on different inputs. Examples include testing properties of a rectangle area function, offer calculations, mean/variance/standard deviation calculations, and quickcheck tests.
The document discusses property-based testing and how it can be used to test code more effectively than example-based testing. It introduces the concepts of testing properties like commutativity, associativity, and identity to specify requirements at a deeper level. QuickCheck is presented as a tool that generates random test values to check properties, finding bugs more quickly than testing by hand. JUnit Quickcheck and JSVerify are given as libraries for implementing property tests in Java and JavaScript. Potential applications of property testing like testing SDKs and data processing frameworks are outlined.
Making property-based testing easier to read for humansLaura M. Castro
Agile practices have taught us that both stakeholder involvement and early testing are key to quality software. However, it is usually the case that tools for good communication are not that good for testing, and vice-versa.
In this talk, readSpec (one of the results of the PF7 EU PROWESS project) is presented, a tool that attempts to fill in this gap.
The document discusses property-based testing for services in Scala. It introduces property-based testing as an alternative to example tests that can generate random inputs to test definitions of how a program should behave. The document provides an example of generating test data using Scalacheck, writing properties to define expected outputs, and using for-comprehensions to combine generated values into more complex types for testing.
Property-based testing (PBT) focuses on testing specifications rather than implementations. It uses random testing against properties expressed as code to generate many test cases, reducing testing effort. PBT represents a system as states, commands to transition between states, and properties relating commands to expected states. This allows effective testing of stateful systems. PBT has been used successfully for concurrency, distributed systems, and finding bugs unit tests missed. Popular PBT libraries include Scalacheck, QuickCheck, and Hypothesis.
Better Tests, Less Code: Property-based TestingC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2ms4qCS.
Matt Bachmann presents a few patterns meant to inspire developers to get started with Property-based Testing. Filmed at qconsf.com.
Matt Bachmann works as a Software Engineer in the Commerce team at Fitbit Matt. The Commerce team helps maintain the order pipeline from cart to post purchase support. Prior to his work at Fitbit he worked on content analysis platforms for the identification of PII, PCI, and other potentially sensitive information.
Property based Testing - generative data & executable domain rulesDebasish Ghosh
- Property based testing verifies properties and invariants of code through automated generation of random test data rather than testing with specific hard-coded test cases. This helps uncover subtle bugs and edge cases.
- Key aspects include defining properties to specify constraints that must hold, using a library like ScalaCheck to generate random data and verify properties hold, and defining custom generators for domain-specific types.
- Properties can verify things like business rules of a domain model in a more abstract way compared to traditional unit tests.
Video and more content at fsharpforfunandprofit.com/pbt
"The lazy programmer's guide to writing 1000's of tests: An introduction to property based testing"
We are all familiar with example-based testing, as typified by TDD and BDD. Property-based testing takes a very different approach, where a single test is run hundreds of times with randomly generated inputs.
Property-based testing is a great way to find edge cases, and also helps you to understand and document the behaviour of your code under all conditions.
This talk will introduce property-based testing and show how it works, and why you should consider adding it to your arsenal of testing tools.
Unit testing involves testing individual units or components of code to ensure they work as intended. It focuses on testing small, isolated units of code to check functionality and edge cases. Benefits include faster debugging, development and regression testing. Guidelines for effective unit testing include keeping tests small, automated, independent and focused on the code's public API. Tests should cover a variety of inputs including boundaries and error conditions.
The document discusses property-based testing for services in Scala. It introduces property-based testing as an alternative to example tests that can generate random inputs to test definitions of how a program should behave. The document provides an example of generating test data using Scalacheck, writing properties to define expected outputs, and using for-comprehensions to combine generated values into more complex types for testing.
Property-based testing (PBT) focuses on testing specifications rather than implementations. It uses random testing against properties expressed as code to generate many test cases, reducing testing effort. PBT represents a system as states, commands to transition between states, and properties relating commands to expected states. This allows effective testing of stateful systems. PBT has been used successfully for concurrency, distributed systems, and finding bugs unit tests missed. Popular PBT libraries include Scalacheck, QuickCheck, and Hypothesis.
Better Tests, Less Code: Property-based TestingC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2ms4qCS.
Matt Bachmann presents a few patterns meant to inspire developers to get started with Property-based Testing. Filmed at qconsf.com.
Matt Bachmann works as a Software Engineer in the Commerce team at Fitbit Matt. The Commerce team helps maintain the order pipeline from cart to post purchase support. Prior to his work at Fitbit he worked on content analysis platforms for the identification of PII, PCI, and other potentially sensitive information.
Property based Testing - generative data & executable domain rulesDebasish Ghosh
- Property based testing verifies properties and invariants of code through automated generation of random test data rather than testing with specific hard-coded test cases. This helps uncover subtle bugs and edge cases.
- Key aspects include defining properties to specify constraints that must hold, using a library like ScalaCheck to generate random data and verify properties hold, and defining custom generators for domain-specific types.
- Properties can verify things like business rules of a domain model in a more abstract way compared to traditional unit tests.
Video and more content at fsharpforfunandprofit.com/pbt
"The lazy programmer's guide to writing 1000's of tests: An introduction to property based testing"
We are all familiar with example-based testing, as typified by TDD and BDD. Property-based testing takes a very different approach, where a single test is run hundreds of times with randomly generated inputs.
Property-based testing is a great way to find edge cases, and also helps you to understand and document the behaviour of your code under all conditions.
This talk will introduce property-based testing and show how it works, and why you should consider adding it to your arsenal of testing tools.
Unit testing involves testing individual units or components of code to ensure they work as intended. It focuses on testing small, isolated units of code to check functionality and edge cases. Benefits include faster debugging, development and regression testing. Guidelines for effective unit testing include keeping tests small, automated, independent and focused on the code's public API. Tests should cover a variety of inputs including boundaries and error conditions.
