Agentic AI-based
Testing Tool

The global aerospace software testing market is projected to grow from $1.2 billion in 2023 to $2.5 billion by 2032, at a CAGR (compound annual growth rate) of 8.1% (Source: Dataintelo, Aerospace Software Testing Market Report). A key driver of this growth is the rising demand for advanced communication systems, and integration with cutting-edge technologies into modern aircraft and avionics systems.

At the same time, the aerospace industry is facing unprecedented system complexity while testing flight control, navigation, and other mission critical software systems to ensure safety and reliability. The increasing regulatory scrutiny also makes verification and validation (V&V) workflows more resource-intensive and error-prone. Compliance with standards such as DO-178C demands thorough traceability, exhaustive documentation, and repeatable testing processes. In such a situation, traditional V&V methods struggle to keep pace, both in terms of agility and assurance. Hence the need for robust software testing solutions is growing to ensure reliable system performance.

To address these growing challenges, eInfochips has developed a multi-agent AI testing tool where an intelligent agent parses complex regulatory and requirement documentation and generates test cases. The tool utilizes Agentic AI, Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to automate the generation of test cases from standard software requirements documents. The tool significantly reduces manual effort, improves test coverage, minimizes human error, and accelerates certification cycles.

Key Features

The tool is built on a multi-agent architecture and comprises of three autonomous agents: a Requirement Analysis Agent, a Test Case Generation Agent, and a Verification Agent. Each agent works collaboratively to streamline the end-to-end verification and validation workflow.

Responsive User Interface

Dynamic and responsive UI, built for efficiency and usability, ensuring users can effortlessly access AI-generated insights and test artifacts.

Intelligent Query Resolution

Integrated with an interactive, context-aware requirements chatbot that enables efficient knowledge retrieval and real-time query resolution, enhancing user engagement.

Document Processing

Ability to upload and understand complex project documentation, including system architecture, user guides, and requirements.

User Authentication / Role-Based Access Control

Secure login mechanism with token-based authentication, ensuring only authorized users can interact with the system. Provides differentiated access for users and admins, with admins granted enhanced privileges for system management.

DO-178-based Test Case Generation

Generate compliance or standard specific test cases.

Project and Document Management

Admins can create, edit, and maintain projects and their associated documents.

Custom Configuration

The web platform supports flexible configuration tailored to specific project requirements or custom requirement templates, enabling seamless integration with diverse aerospace development workflows.

Security

On-premises deployment ensures that all the components, frontend, backend, and AI processing, operate within the organization’s secure environment on dedicated hardware, complying with data privacy standards and aligning with organizational security requirements.

On-Prem Deployment

A dedicated on-premises high-performance server hosts AI models, including large language models, to generate and verify test cases, process project documents, and respond to technical queries. It leverages retrieval-augmented generation (RAG) to enhance contextual accuracy by dynamically accessing indexed document repositories.

Benefits

Faster Time-to-Market

The accelerator shortens V&V cycles and certification timelines by automating verification workflows, helping organizations bring aerospace systems to market faster without compromising safety or quality.

Improved Productivity

Reduces cognitive load on engineers by automating documentation analysis and test case design, allowing teams to focus on higher-value system design and decision-making tasks, and improving productivity up to 50%.

Reduced Human Error

The tool achieves up to 70% accuracy in automated test case generation, significantly reducing manual effort and minimizing the risk of human error in interpreting requirements and designing test scenarios.

Reduced Cost

The centralized AI server facilitating on-prem AI deployment supports multiple engineering teams or business units, enabling shared usage of models, RAG pipelines, and indexing infrastructure thereby reducing costs.

Improved Test Coverage

Real-time, traceable insights generated by LLMs provide engineers and QA teams with up to a 40% improvement in test coverage, greater requirement clarity, and enhanced regulatory readiness.

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