boundary-value-analysis

This repository is a hub of various testing techniques like Boundary value analysis and unit testing

Test automation project for Parking Cost Calculator JS application with Cypress and QUnit

CENG352 Software V&V Assignment: Automated form validation and CI/CD pipeline implementation using Jest. Includes extensive BVA and EP testing scenarios.

Manual QA portfolio: AI product configurator, Alice on iOS, web case studies and test design. 9 bug reports, 6 mobile test cases, 18 boundary checks.

Turn a parameter's constraints into a boundary-value and equivalence-class test case suite.

Collection of manual testing projects demonstrating test case design, bug reporting, test planning, and QA documentation.

Тестовая документация для сервиса каршеринга и такси

Automation manual and software testing from HCLTECH company

Black-box testing using Equivalence Partitioning (EP) and Boundary Value Analysis (BVA).

Program that generates test case input for Boundary Value Analysis (BVA) test variables

The purpose of this project is to generate test inputs using the Boundary Value Analysis (BVA) Input Generator, which will be used in conjunction with manually input test cases for the Triangle_Test and NextDate programs. The goal is to test the accuracy and reliability of the programs under various input conditions to identify concerns.

Manual testing portfolio: test cases, bug reports, checklists, decomposition and test design techniques (Equivalence Partitioning, Boundary Value Analysis)

Manual QA portfolio: test plans, checklists, test cases, EP/BVA, traceability, bug reports (Swag Labs).

Schema-aware test data generation: boundary values, negative tests, insurance/user/transaction domains.

QA portfolio project showing equivalence partitioning, boundary value analysis, and automated testing with Java, Maven, and JUnit 5.

Sample Test Case Design.

Intelligent test generation: AST parsing, boundary value analysis, equivalence partitioning, coverage gap detection. PyPI package.

Professional QA Portfolio - Comprehensive manual testing, bug reporting, and test case documentation for 'DC People', an enterprise HR, Recruitment, and Broker management web application.

This project implements an unsupervised machine learning approach to detect anomalous financial transactions using K-Means clustering algorithm. The system analyzes transaction patterns based on amount and timing to identify potentially fraudulent activities in financial data.