End-to-End Digital Thread Test Automation with Keysight Eggplant Test

Modern design and manufacturing organizations increasingly depend on a connected digital thread spanning Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), Product Lifecycle Management (PLM), Application Lifecycle Management (ALM), Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and the wider enterprise IT environment. Data moves continuously between these systems from initial concept and engineering through planning, production, and business operations. As a result, a problem in one application or at one integration point can propagate across the wider Digital Engineering and Manufacturing (DEM) landscape.

This solution brief explores three fundamental testing challenges created by this interconnected environment: maintaining accuracy across systems, preserving consistency during changes and upgrades, and protecting performance and availability.

Accuracy is essential because configurations, custom business logic, designs, bills of materials, and other information must remain consistent as data passes between applications. A failure does not necessarily result in an obvious application crash. Incorrect or corrupted information can instead move quietly downstream, potentially reaching procurement or production before anyone identifies the issue. Organizations therefore need to validate not only individual applications but the complete workflows connecting them.

Change introduces a second challenge. Updates to CAD platforms, PLM logic, ERP or MES applications, integrations, and other components can create regression risk elsewhere in the digital thread. The increasing adoption of cloud and SaaS applications further accelerates the frequency of releases. Traditional manual testing can struggle to provide adequate coverage at this pace, while automation technologies focused on individual application objects or APIs may not fully validate the cross-system experiences engineers and operators actually use.

Keysight Eggplant Test addresses this problem by providing automation and enterprise workflow assurance from the user perspective. Its computer vision, optical character recognition, and vision-language capabilities allow it to interact directly with graphical interfaces, including visually complex 2D and 3D applications. It can automate workflows across web, mobile, native and custom applications, documents, IIoT technologies, and other platforms without requiring knowledge of their underlying code structure.

Using a straightforward connection model, Eggplant can move between multiple applications within the same automated test. This enables organizations to validate, for example, that a design modification in CAD results in the expected information appearing in PLM, ERP, MES, spreadsheets, documents, databases, and other connected systems. The approach allows testing to mirror the way engineers and operators actually perform their work.

Eggplant also helps organizations maintain automation as applications evolve. Its UI-focused approach, image sets, computer vision, and intelligent healing capabilities help tests accommodate interface changes. Its low-code SenseTalk scripting language reduces the requirement for specialist programming skills.

The solution extends this accessibility through AI-driven natural-language test creation. Users can describe the workflow they want to validate in plain language and use Eggplant to create and execute the test. Eggplant Digital Automation Intelligence (DAI) further supports model-based testing by representing how users move through applications and automatically generating and prioritizing test paths, including combinations that manual test designers may not consider.

Beyond functional correctness, Eggplant supports user-centric performance and availability monitoring. Organizations can define expected user outcomes, measure how long workflows or application states take to complete, compare performance against baselines or thresholds, and feed results into alerts or operational dashboards.

Together, these capabilities enable organizations to move beyond isolated application testing toward continuous assurance of the complete digital thread. By automating realistic workflows across diverse technologies, Eggplant helps reduce manual testing effort and risk while supporting the accuracy, resilience, usability, and performance of increasingly complex DEM environments.

2. Automating Digital Thread Validation in Modern Vehicle Development

Explore how Keysight Eggplant enables AI-driven test automation across automotive CAD, CAE, PLM, ALM, MES, ERP, and connected enterprise applications. The solution brief explains how computer vision, self-healing automation, natural-language test creation, and end-to-end workflow testing can improve coverage while reducing regression testing and maintenance effort.

Abstract

Modern vehicle development relies on an increasingly complex Digital Engineering and Manufacturing environment. Automotive organizations use interconnected Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), Product Lifecycle Management (PLM), Application Lifecycle Management (ALM), Manufacturing Execution System (MES), and Enterprise Resource Planning (ERP) platforms to develop products and move information from engineering into production.

Testing these environments creates significant challenges. Automotive applications frequently contain complex 2D and 3D graphics and highly visual interactions such as scrolling, drag and drop, and manipulating models. At the same time, vehicle programs must manage constantly changing requirements across hardware, software, mechanical, and electrical systems. Changes made within one part of the digital thread can affect multiple downstream systems and workflows, making rapid and comprehensive validation essential.

This solution brief explains how Keysight Eggplant helps automotive organizations automate testing across the complete DEM and Enterprise IT landscape rather than treating each application as an isolated system.

Eggplant uses computer vision technologies including optical character recognition and graphic-element recognition to interact with applications through the same visual interface used by engineers. It controls keyboard and mouse inputs and interprets text and graphics displayed on screen, allowing organizations to automate applications whose graphical complexity may make conventional automation difficult.

This approach supports end-to-end scenarios spanning multiple integrated systems. Organizations can create tests that move between CAD, CAE, PLM, ALM, MES, ERP, and associated applications, helping them validate whether information and workflows operate correctly throughout the digital thread. Eggplant is also technology and platform agnostic, reducing dependence on the technical architecture of individual applications.

Image-based testing enables Eggplant to handle use cases such as validating CAD applications, interacting with moving 3D images, creating components, and associating those components with a bill of materials in PLM. The solution can also accommodate application customizations and complex business scenarios involving interactions such as scrolling and drag and drop.

Maintaining automated tests as systems evolve represents another major concern for automotive organizations. Eggplant provides self-healing capabilities designed to improve script robustness when graphical elements move or application interfaces change. Because its automation works visually rather than depending solely on underlying tags and locators, version upgrades and alterations to HTML structures can require less test maintenance.

The solution also aims to make test automation accessible to the automotive engineers who understand engineering workflows most deeply. Eggplant's low-code SenseTalk language provides a natural-language-like approach to scripting, while newer capabilities allow users to describe desired tests in plain language so that Eggplant can build and run them. This can reduce the barrier between subject-matter experts and test automation specialists.

Eggplant Digital Automation Intelligence (DAI) extends this approach through AI-driven model-based testing. DAI models real automotive engineering workflows across CAD, PLM, and connected systems, then automatically generates and executes test paths through that model. It can explore edge cases that engineers or testers may not have manually scripted and re-explore the model as vehicle variants, suppliers, or application versions change.

Eggplant can also integrate into DevOps environments, operate in headless mode, and transmit detailed results including screenshots and video.

Together, these capabilities give automotive organizations a way to automate complex user-centric workflows across both engineering and enterprise systems. By combining computer vision, AI-driven exploration, self-healing automation, and cross-platform testing, Eggplant can help reduce manual regression testing and maintenance effort while improving coverage and supporting faster, more reliable automotive design and development.

3. Automating Digital Thread Validation in Secure Aerospace Environments

Learn how Keysight Eggplant delivers secure, non-invasive test automation across aerospace and defense CAD, CAE, PLM, ALM, ERP, MES, and other mission-critical systems. Explore AI-powered exploratory testing, computer vision, natural-language test creation, self-healing automation, secure connectivity, and end-to-end validation without requiring access to application source code.

Abstract

Aerospace and defense organizations depend on complex Digital Engineering and Manufacturing environments that connect Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), Product Lifecycle Management (PLM), Application Lifecycle Management (ALM), Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and other enterprise technologies. Testing these environments presents many of the same challenges found across engineering organizations, but safety, security, privacy, and mission-critical requirements add another level of complexity.

This solution brief presents Keysight Eggplant Test as a non-invasive automation platform designed to validate workflows across secure DEM and Enterprise IT ecosystems without requiring access to application source code.

Engineering applications frequently contain functionality that conventional automation technologies can find difficult to test, including 2D geometry, 3D models, graphical files, scrolling, and drag-and-drop interactions. Furthermore, the applications involved may use different languages, architectures, devices, and deployment models. The requirement to validate workflows across interconnected systems compounds the challenge.

Eggplant addresses this through black-box testing that interacts with applications as a user would. Its computer vision technology combines optical character recognition and graphic-element recognition to interpret what appears on screen and control keyboard and mouse interactions. This allows teams to automate testing without instrumenting the application or gaining access to its internal implementation.

This non-invasive architecture is particularly relevant to highly secure environments. Test automation can operate without affecting the system under test or exposing sensitive source code, helping organizations validate mission-critical workflows while respecting security and privacy requirements. Eggplant also supports secure RDP and VNC connections, allowing test engineers to connect to multiple enterprise applications and devices in parallel sessions.

Eggplant can automate end-to-end business scenarios spanning CAD, CAE, PLM, ALM, MES, ERP, and other applications. Rather than testing individual components in isolation, teams can validate how data and user workflows move through the entire digital thread. Because Eggplant is agnostic to application architecture, it can work with native applications, rich clients, client-server systems, mainframes, embedded technologies, and cloud environments.

The platform also provides self-healing capabilities intended to reduce maintenance as applications change. Its computer vision approach can adjust to graphical changes and evolving interface structures, while SenseTalk provides an accessible natural-language scripting environment.

The solution brief also highlights capabilities intended to bring aerospace and defense engineers more directly into the testing process. Users who understand a workflow can describe what they want to validate in plain language and use Eggplant to build and run the corresponding test without conventional scripting. Because this automation remains focused on what users see and do rather than underlying source-code structures, tests can remain resilient through application upgrades while preserving the non-invasive approach required in secure environments.

Eggplant Digital Automation Intelligence (DAI) adds AI-powered model-based and exploratory testing. DAI models expected system behaviour and automatically generates, prioritizes, and executes paths through that model. By moving beyond predefined regression tests and conventional "happy paths", it can help uncover edge cases and regression risks that manually scripted suites may miss.

The platform also supports data-driven testing by retrieving inputs from familiar applications such as Microsoft Excel. This allows teams and field users to expand test coverage across multiple parameters without maintaining large numbers of separate scripts. Reporting capabilities provide visibility into test results, support customized reporting requirements, and create audit trails. Test results can include screenshots and videos and integrate with DevSecOps toolchains.

By combining non-invasive automation, secure connectivity, computer vision, AI-driven exploratory testing, end-to-end workflow validation, and accessible test creation, Eggplant provides aerospace and defense organizations with a way to increase test coverage while protecting the integrity and security of mission-critical engineering environments.