Model-Based BDD: Convert Gherkin into automated test suites with on-the-fly data

Automatically generate Behaviour-Driven specifications with matching tests, delivering accurately built, fully tested software in short iterations. With Test Modeller, business users, QA teams and developers collaborate from accurately modelled Feature Files, generating complete tests with “just in time data in-sprint. 

Rapid change needs rapid, rigorous test automation 

Behaviour-Driven Development offers parallel design, development and testing, with cross-functional teams working from easy-to-read specifications and free-flowing change requests. However, the flexibility of piling up Gherkin Feature Files can spell trouble for developers tasked with building the fast-flowing systems. Unconnected Gherkin provide little guidance on how vast system logic should relate, while the repetitive written specifications introduce ambiguity. These design defects create frustrating and costly bugs in code. 

The faster QA finds these bugs, the easier they are to fix. However, manual processes frequently force test execution ever-further behind production releases. Manually converting Gherkin into automated tests and glue code is too slow and tends to create incomplete test suites. Test data then throws up lengthy delays as QA waits for slow and manual data refreshes. Inaccurate and out-of-date data in turn creates frustrating test failures, while incomplete data further undermines test coverage. Truly parallel testing and development instead requires collaboration from complete, up-to-date system designs, with complete test automation and data prepared accurately in-sprint. 

Virtual Meetup: Using Visual Models to Unlock BDD 

Design, develop and test in-sprint 

With Test Modeller, business users, QA teams and developers collaborate in parallel from up-to-date specifications, generating complete automated testas systems are being built. Cross-functional teams can automatically model Gherkin Feature Files, rapidly updating the clear and complete models as change requests flow in. Developers working from the logically accurate specifications in turn avoid the bugs crated by unconnectedambiguous Feature Files. QA teams can work in parallel, rigorously testing fast-changing systems before every release. Automated test design and code generation produce executable test suites or BDD “glue code” at the click of a button, removing the bottlenecks of repetitive test scripting and maintenance for in-sprint test execution. 

Model-based BDD Dynamic Data

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Test Modeller further ensures that every test has the data it needs, exactly when it needs it. A range of techniques find, make and allocate data, passing parameters from one Gherkin step to another to create consistent data journeys. Example data and data tables import from Gherkin Feature Files, while a central test data catalogue automatically looks up data from across files and databases. Assigning the automated data finds to the modelled Gherkin steps automatically builds equivalence classes, rapidly creating a spread of values to drive test automation. Test generation resolves the data finds “just in time”embedding rich data as dynamic references and functions in scripts and glue code. Missing data is created on-the-fly, enabling rigorous testing without the delays of unavailable or invalid test data. 

Communicate, Collaborate, Generate 

Watch this demo of using Excel Data, Gherkin and a Cucumber framework to test a web UI, and discover how: 

  1. A central test data catalogue provides automated data lookups for a wide array of data sources, allowing manual testers and automation frameworks to request unique data sets on demand. 
  2. Customisableeasy-to-use web forms allow QA teams and developers to request and receive data in parallel, selecting inputs to populate accurate data into multiple environments.
  3. Test Modeller builds visual flowchart models of Gherkin scenarios, rapidly adding logic to generate complete specifications with matching test suites.
  4. Selecting data lookups from the data catalogue automatically models equivalence classes for Gherkin steps, rapidly creating a rich spread of data to drive comprehensive automated testing.
  5. Dynamic data tags at the model level automatically lookup data from a wide range of data sources, creating a rich spread of consistent journeys from the modelled Gherkin. 
  6. Automated test generation pushes the Gherkin or automation glue code to integrated frameworks and ALM tools, complete with test data for rigorous test execution.
  7. Test Data Automation” jobs defined at the model level will hunt for the data needed during automated testing, finding and making data on-the-fly to eradicate test data bottlenecks. 
  8. Updating the central models regenerates the linked Gherkin, automated tests and glue codes in a single click, allocating new data for rigorous regression testing in-sprint.

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