# test data A collection of test input values and expected results used during software testing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Write End-to-End Tests in Your Backend's Language DevFeed: [Write End-to-End Tests in Your Backend's Language]() Original publisher: [Read original article]() Author: James McConkey Published: 2026-09-17T12:00:42Z Content type: tutorial Language: en Sources: [Atomic Object]() Topics: [Testing](), [Playwright](), [FastAPI](), [Python](), [test data](), [Pydantic](), [SQLAlchemy](), [ASP.NET Core](), [PostgreSQL](), [Docker Compose]() Tags: [asp-net-core](), [code](), [data](), [docker-compose](), [fastapi](), [playwright](), [postgresql](), [project-team-management](), [python](), [sqlalchemy](), [tests](), [the-software-life]() ### AI overview This article argues that end-to-end test-data setup is often the main design challenge, because tests must create consistent domain records while running alongside other tests. It recommends using browser-testing tools in the backend's language when possible, keeping meaningful relationships inline, and extracting small creation helpers without hiding scenario intent. ### Source excerpt The browser is often the easiest part of an end-to-end test. Consider a test that verifies a user can complete an overdue task. The visible interaction is small: sign in, find the task, click Complete, and observe the new status. Before any of that can happen, the test needs a workspace, a user, a project, and [...] The post Write End-to-End Tests in Your Backend's Language appeared first on Atomic Spin. ## Designing a custom AI agent for repetitive QA workflows DevFeed: [Designing a custom AI agent for repetitive QA workflows]() Original publisher: [Read original article]() Author: Rimple Sharma Published: 2026-05-13T09:34:47Z Content type: tutorial Language: en Sources: [Amex GBT Technology]() Topics: [AI Agent](), [Automation](), [test](), [test data](), [configuration](), [pull-requests](), [consistency]() Tags: [ai-agent](), [ai-skills](), [automation](), [configuration](), [consistency](), [pull-requests](), [qa](), [software-testing](), [test-automation](), [test-data]() ### AI overview This article defines a repetitive QA workflow involving configuration updates, test-data additions, mapping checks, coverage validation, and pull requests. It argues that the main bottleneck is contextual decision-making and cross-file validation, which motivates designing a custom AI agent. ### Source excerpt Part 1: Core problem definition, three pillars of an effective agent & why structured instructions matter.Source: AI generated image Engineers on QA teams spend a disproportionate share of their time on work that follows a consistent pattern: updating configurations, modifying test artifacts, executing focused validations, and managing pull requests. The tasks are well-defined but the repetition adds up fast. Individually, none of this is complicated. But taken together, these tasks are: Repetitive Error-prone Hard to repeat reliably During my time in automation, I kept running into the same problem. Every release brought the same requirements. New datasets had to be added so the test suite could cover the scenarios tied to them. It was never a one-time effort. The same work had to be repeated across different combinations, every single time. The process typically looked like this: 1. Updating multiple configuration files. 2. Adding the corresponding test data. 3. Making sure everything was mapped correctly. 4. Validating coverage and consistency. 5. Committing the changes and raising a PR. Each step on its own was straightforward. But strung together and repeated across releases, it consumed a meaningful chunk of time that could have gone elsewhere. Problem breakdownFig 2.0 Pain of manual repetitive process (Source: AI generated image) Each step is straightforward. But together, context switching between files, making manual edits, double checking mappings, it added up to 30 to 60 minutes per release cycle in our case. Miss one mapping, introduce a typo, and the build breaks. It wasn't hard work, just repetitive. And that's exactly the kind of work that makes test suites brittle over time. Even with automation in place, every iteration still required someone to identify what had changed, figure out which files were affected, and make sure everything stayed consistent across the board. The framework we had was solid. Scripts handled the heavy lifting well. But there ## Simple Data Modeling with a Test Data Set DevFeed: [Simple Data Modeling with a Test Data Set]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2017-11-27T15:23:44Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [data-modeling](), [PostgreSQL](), [test data](), [SQL]() Tags: [data-modeling](), [how-to](), [postgresql](), [sql](), [test-data]() ### AI overview This tutorial explains how to develop and test a PostgreSQL database schema using SQL scripts, sample data, explicit transactions, and rollback. It demonstrates a forum-style schema populated with random data so the model can be refined interactively without affecting existing application code. ### Source excerpt In How to Write SQL we saw how to write SQL queries as separate .sql files, and we learnt about using query parameters with the psql syntax for that (:variable, :'variable', and :"identifier"). For writing our database model, the same tooling is all we need. An important aspect of using psql is its capacity to provide immediate feedback, and we can also have that with modeling too.