Ai Product Development for improving Qa in software development cycle

We are looking for an experienced AI builder to create a cutting-edge product aimed at enhancing the Quality Assurance (QA) process in software development lifecycle. The ideal candidate should have a strong background in using ai tools, with a clear understanding of qa methodologies. Your role will involve designing, developing, and testing ai-driven solutions that streamline qa tasks and improve overall software quality. If you have a passion for innovation and a track record in AI product development, we would love to hear from you!

Quality Assurance (QA) Automation Process - Step-by-Step Guide
This document outlines the QA automation process for product development, detailing each step involved and highlighting where external support is needed to enhance our capabilities. This structured approach ensures consistency, efficiency, and accuracy across our testing and QA activities.

- Overview of the Flow
The QA process is divided into three main steps:
Product Development and Sprint Planning
Test Case Creation and Execution
Automated Test Case Management and Integration

Step 1: Product Development and Sprint Planning
Task: Sprint Planning and Task Assignment
Description: In this step, the product team gathers requirements and defines user stories for development. These are broken down into tasks and added to the sprint backlog.
Activities:
Create user stories and assign tasks based on requirements.
Document tasks in the sprint backlog for the development and QA teams.
Task: Integration with Jira APIs
Description: Integrating APIs between Jira and other systems, including OpenAI, to automate task logging and tracking.
Outcome: A consistent and up-to-date task list that aligns with the development process.

Step 2: Test Case Creation and Execution
Task: Test Case Design and Development
Description: Based on the user stories and sprint tasks, the QA team creates test cases to cover each scenario, ensuring comprehensive coverage.
Activities:
Design test cases for each story.
Update the test cases in our QA tool (e.g., Zephyr).
Outcome: A detailed set of test cases for the sprint.
Task: Test Case Execution Across Environments
Description: The QA team executes test cases in different environments (Preview, dev, and uat) to verify functionality at each stage of the development cycle.
Activities:
Run tests in Preview, then move to dev and finally to uat.
Monitor and log test results for each environment, ensuring defects are tracked and managed.
Outcome: Comprehensive test execution across all stages, with reports generated for each phase.

Step 3: Automated Test Case Management and Integration (Problem Area)
Task: Automation of Test Cases
Description: This step focuses on automating test cases using Zephyr, Reflect, and AI-driven systems. The objective is to automate the creation, updating, and execution of test cases as the product evolves.
Problem Statement:
Context: Step 3 in our QA automation process is where test cases need to be automated based on product updates and new sprint tasks. Our goal is to ensure that our test coverage is robust, dynamic, and efficient.
Problem: Our current capabilities are limited in dynamically creating and updating test cases based on new requirements and automating their execution across different environments (Preview, dev, and uat). Integration with defect tracking systems (like Jira) and AI platforms (like OpenAI) is necessary but currently beyond our expertise.
Objective: We need the expertise of an AI partner to:
Develop a system that can interpret sprint tasks and requirements automatically to create and update test cases in Zephyr.
Automate the execution of these cases, synchronizing with Jira for defect tracking and AI systems for test optimization.
Ensure that the solution provides reporting on test outcomes and integrates seamlessly with our existing tools.
Task: AI-Driven Test Optimization and Defect Management
Description: The third-party ai system should not only automate test case creation but also optimize test coverage using ai-driven insights. Additionally, the system should link with Jira APIs for efficient defect management.
Activities:
AI analyzes and interprets requirements, creating and updating test cases as needed.
Automate test case execution and generate defect reports automatically linked with Jira.
Outcome: An intelligent, adaptive test management system that ensures continuous alignment with product requirements.