PredictQA Documentation

PredictQA is designed to integrate predictive quality intelligence into your existing software delivery pipeline. Before you begin, ensure your team understands the core concepts of Predictive Quality Engineering (PQE).

• Sign in to PredictQA Studio and create your first project.
• Define requirements and link them to your application modules.
• Run the initial risk assessment to establish baseline quality scores.
• Invite team members and configure roles for Test Design, Review, and Execution.

For detailed setup instructions, architecture decisions, and module guides, explore the sections below.

The Predictive Quality Engineering Architecture (PQEA) is built on four pillars: Requirements Intelligence, Risk Intelligence, Test Intelligence, and Release Intelligence.

• Requirements Intelligence: Converts natural language requirements into structured, testable knowledge with clarity scoring.
• Risk Intelligence: Continuously scores risk across modules, requirements, and defects to focus effort.
• Test Intelligence: Uses AI to generate, review, and approve test cases with human-in-the-loop governance.
• Release Intelligence: Aggregates quality signals into release readiness scores and go / no-go recommendations.

PredictQA Studio provides a unified workspace with nine integrated modules that cover the full predictive quality lifecycle:

PredictQA follows a layered architecture that separates data ingestion, AI processing, business logic, and presentation layers.

• Ingestion Layer: Connects to requirements tools, test management systems, CI/CD pipelines, and defect trackers.
• AI Layer: Hosts risk models, test generation models, and release scoring algorithms.
• Business Logic Layer: Enforces workflows, governance rules, and lifecycle state management.
• Presentation Layer: Delivers dashboards, review screens, and executive reports.

All layers communicate through secure APIs with authentication, audit logging, and role-based access control.

The core data model connects quality entities across the software lifecycle. Key entities include:

• Project / Version / Environment: Top-level scoping for all quality data.
• Module & Flow: Logical and functional decomposition of the application under test.
• Requirement: Business requirements linked to tests, risks, and defects.
• Test Case: Draft and approved test cases with lifecycle state tracking.
• Defect: Issues with severity, module linkage, and trend attributes.
• Release: Release candidates with readiness scores and decision history.

AI Risk Scoring combines historical defect data, requirement complexity, code churn, and test coverage to produce a dynamic risk score for every module and requirement.

• Inputs: Defect density, code complexity, change frequency, and coverage gaps.
• Model: Weighted scoring algorithm refined with team-specific baselines.
• Output: Risk scores from 0–100 with categorized tiers (Low, Medium, High, Critical).
• Action: Recommendations for additional testing, review focus, or release constraints.

The Release Decision Workflow helps teams move from subjective go / no-go calls to structured, data-driven release readiness assessments.

1. Aggregate Signals: Collect test results, defect trends, risk scores, and coverage metrics.
2. Compute Readiness: Generate an overall release readiness score with top risk drivers.
3. Review & Discuss: Stakeholders review the score, risk notes, and recommended mitigations.
4. Decide & Record: Approve, conditionally approve, or reject the release with full audit history.