Search Results for "data dictionary postgresql"

Database Subsetting and Relational Data Browsing Tool. Navigate bidirectionally through the database by following foreign-key-based or user-defined relationships. Exports consistent, referentially intact row-sets from relational databases. Removes data w/o violating integrity. Generates topologically sorted SQL-DML, hierarchically structured XML. and DbUnit datasets.

benerator is a framework for creating realistic and valid high-volume test data, used for load and performance testing and showcase setup. Data is generated from an easily configurable metadata model and exported to databases, XML, CSV or flat files.

DB Sanity performs checks on a database for verifying data consistency and correctness of aplication deployments. It is invoked by command line and creates an HTML report with aggregated diagnostic summaries and listings of faulty database entries.

Analysis tools for scale test data generated by The Grinder.

A collection of reporting utilities specific to The Grinder test tool. The tools consist of a reporting database and graphing utilites based upon jyplot and jFreechart written in jython.

A program written in Java that allows to generate test data for SQL databases. Constraints on the data output can be flexibly specified via a GUI. At the moment, only PostgreSQL is supported, but support for other databases can be added via metadata.

Lastest.cloud is a free, open-source verification of development, self-hosted visual regression and end-to-end testing platform for web applications. An AI agent records you clicking through your running app and generates Playwright tests with multi-selector fallback. Replays are deterministic and token-free, so your CI/CD bill doesn't scale with your test suite. Lastest ships three diff engines side-by-side — pixel (pixelmatch), structural (SSIM), and perceptual (Butteraugli) — so...

Tesbo is an open source AI-powered test case management software built for QA teams. Tesbo's Context Engine reads your actual project requirements from Jira and Linear, existing test cases, product documentation, and defect history before generating a single test case. Every draft cites exactly which requirement, document, and defect shaped it. Nothing enters your repository without a QA engineer approving it first. The engine learns from every approval, edit, and rejection, so generated...