AI-augmented, software-testing tools provide capabilities for advanced, self-optimizing and adaptive automated testing through the use of AI technologies, such as machine learning (ML), self-healing heuristics or computer vision. They assist humans in their testing efforts and reduce the need for human intervention in the following areas: Test case and test data generation Test suite optimization and coverage detection Test efficacy and robustness Test analysis and defect prediction Test effort estimation and decision making AI-augmented testing tools streamline, accelerate and improve the test workflow, as models can be retrained based on the data collected from the activities they perform — thereby enhancing human productivity.
'Application integration platforms enable independently designed applications, apps and services to work together. Key capabilities of application integration technologies include: • Communication functionality that reliably moves messages/data among endpoints. • Support for fundamental web and web services standards. • Functionality that dynamically binds consumer and provider endpoints. • Message validation, mapping, transformation and enrichment. • Orchestration. • Support for multiple interaction patterns, content-based routing and typed messages.
Gartner defines integration platform as a service (iPaaS) as a vendor-managed cloud service that enables end users to implement integrations between a variety of applications, services and data sources, both internal and external to their organization.
Intelligent document processing (IDP) solutions extract data to support automation of high-volume, repetitive document processing tasks and for analysis and insight. IDP uses natural language technologies and computer vision to extract data from structured and unstructured content, especially from documents, to support automation and augmentation.
Gartner defines process mining platforms as platforms that are designed to discover, monitor and improve processes by extracting knowledge from events captured in information systems to continuously deliver visibility and insights. Process mining includes automated process discovery (extracting process models from an event log), conformance checking (monitoring deviations by comparing model and log), social network/organizational mining, automated construction of simulation models, model extension, model repair, case prediction and history-based recommendations. Process mining platforms extend process mining capabilities by advanced process analytics, process improvement detection and process improvement recommendations, partly driven by AI and generative AI (GenAI).
Gartner defines robotic process automation (RPA) as the software to automate tasks within business and IT processes via software scripts that emulate human interaction with the application user interface. RPA enables a manual task to be recorded or programmed into a software script, which users can develop by programming, or by using the RPA platform’s low-code and no-code graphical user interfaces. This script can then be deployed and executed into different runtimes. The runtime executable of the deployed script is referred to as a bot, or robot.
Task mining is a technique by which enterprises can infer meaningful information by scraping desktop-level event data. This data may be from individual users or a cohort of individuals (e.g., in a call center) and takes the form of screen recordings, keystrokes, mouse clicks and data entries. Additional mining capabilities interpret the data by applying natural language processing and optical character recognition to correlate data in different ways. Task mining helps an enterprise identify inefficiencies and automation or AI potential, improve task execution and enhance the employee experience.