Test Coverage and Software Quality

Find bugs earlier and at less cost to fix. Provide on demand data for every variation.

Your software is only as good as the data used to test and develop it. Rigorous testing and rapid development requires a far wider variety of data combinations than exist in narrow production data, or which could ever be made manually during short sprints.

Does your non-production data contain all the permutations needed to deliver quality software within rapid release cycles?

Invalid Combinations
Data that Integrates Across Systems
Positive Scenarios Found in Production

Extra Database-1

Transaction and Temporal Data
Data of the Right Volumes
Data for Parallel Testing and Development

50% Better Test Coverage

Using Test Modeller, testers and BAs at a $300 million telco start-up generated Jira tests and automated UI tests directly from BPMN requirements.

25x Reduction in Test Volume

ThinkDonate auto-generated targeted tests into their REST Assured framework, using test results to formulate accurate API specifications.

Selenium test generation at a large bank-1

25x Faster Test Creation

At a large bank, it had taken 10 testers two months to script 800 tests. With Test Modeller, one tester created 1000 Selenium tests in a single month.

Test Data for Quality Software Delivery

Software defects cost exponentially more to fix the longer they go undetected. Test Data Automation creates all the data combinations needed to find bugs earlier and at less cost to fix, providing on demand data at every stage of the CI/CD lifecycle.

Understand The Data You Need

A comprehensive set of integrated tools enable you to understand your data model and identify what’s missing, allowing you to create all the permutations needed for rapid software development:

Data Coverage Analysis

Measure which scenarios your data can cover, identifying gaps to fill. Ensure that testers and developers continuously access all the data needed to deliver better software, faster.

Data Comparisons

Compare data across schemas, databases or snapshots, measuring the density of data in different environments and identifying gaps to fill in your non-production data.

Data Pattern Analysis

Use ML algorithms to find potential relationships in data, understanding which combinations should and should not exist. Then, apply the probability analysis to generate rich test data.

Data Validation

Identify unknown relationships in data, find invalid combinations, and test the “soft” relationships imposed on your data. Then, generate valid and invalid combinations that reflect every relationship.

Complete Test Data Throughout CI/CD

Once you’ve registered your data model and measured its coverage, you’re ready to create data that links consistently across your systems, and which fulfils every scenario needed to test and develop in-sprint.

Create All The Data You Need

Test Data Automation offers a range of techniques for creating complete and compliant test data. These techniques are all reusable on demand by testers, developers and automation frameworks during self-service data automation:

Quality Data is Consistent, Rightsized, and Available in Parallel

Providing quality data doesn’t just ensure the quality of your software; it also removes delivery bottlenecks and helps reduce compliance risks.

Your teams require parallel access to data that integrates across fast-changing systems. Otherwise, tests will fail due to broken data, and when a test or engineer edits data in a shared environment. Quality data must also be “rightsized”, from unit all the way to stress testing. Otherwise, test runs will be slow and costly, and might risk non-compliance with data minimisation requirements.

"Just In Time" Test Data

Test Data Automation provides an integrated toolset for ensuring that every tester, developer and automated test enjoys on-the-fly access to data that links consistently across integrated systems:

Data Virtualisation

Create copies of databases at a fraction of the time and cost associated with physical copies. Virtualisation spins up data copies in seconds, allowing testers, developers and frameworks to work undisturbed in parallel environments.

Resolvable Lists

Maintain lists of reusable data lookups, from lists of all past data requests, to data for targeted scenarios. Resolving lists can populate whole environments with rich data, while engineers, frameworks and models can perform on demand look ups.

What We Do

Explore Curiosity's Platform Use Cases

Explore Curiosity's Resources

Creating Quality Since 1995

Test Coverage and Software Quality

Find bugs earlier and at less cost to fix. Provide on demand data for every variation.

Is Test Data Exposing You To Costly Bugs?

Can you automate as quickly as EVERFI?

50% Better Test Coverage

25x Reduction in Test Volume

25x Faster Test Creation

Test Data for Quality Software Delivery

Understand The Data You Need

Data Coverage Analysis

Data Comparisons

Data Pattern Analysis

Data Validation

Complete Test Data Throughout CI/CD

Create All The Data You Need

Synthetic Data Generation

Data Cloning

Data Flow Modelling

A Hybrid Approach

Quality Data is Consistent, Rightsized, and Available in Parallel

"Just In Time" Test Data

Data Allocation

Test Data Find and Makes

Data Virtualisation

Resolvable Lists

Our Platform

Solutions

Resources

Company